Improving Probabilistic Models In Text Classification Via Active Learning

Abstract

Social scientists often classify text documents to use the resulting labels as an outcome or a predictor in empirical research. Automated text classification has become a standard tool since it requires less human coding. However, scholars still need many human-labeled documents for training. To reduce labeling costs, we propose a new algorithm for text classification that combines a probabilistic model with active learning. The probabilistic model uses both labeled and unlabeled data, and active learning concentrates labeling efforts on difficult documents to classify. Our validation study shows that with few labeled data, the classification performance of our algorithm is comparable to state-of-the-art methods at a fraction of the computational cost. We replicate the results of two published articles with only a small fraction of the original labeled data used in those studies and provide open-source software to implement our method.

INTRODUCTION

Text classification—the act of measuring underlying concepts by categorizing sequences of text into two or more categories—is a fundamental task in social science research. In political science, researchers have used this approach to classify a wide variety of textual data, including legislative speeches (Motolinia 2021; Peterson and Spirling 2018), correspondences to administrative agencies (Lowande 2018), public statements of politicians (Airoldi, Fienberg, and Skinner 2007; Stewart and Zhukov 2009), news articles (Boydstun 2013), election manifestos (Catalinac 2016), social media posts (King, Pan, and Roberts 2017), religious speeches (Nielsen 2017), and human rights text (Cordell et al. 2022; Greene, Park, and Colaresi 2019). ¹

Because manually labeling a large number of documents to classify text is too costly, researchers are increasingly turning to machine learning and natural language processing (NLP) methodologies to automate this task. For example, to investigate the relationship between Internet access and state repression in Syria, Gohdes (2020) manually labeled 2,000 out of 65,274 documents in order to train a machine learning model to predict the class of the documents in the rest of the corpus. Similarly, Park, Greene, and Colaresi (2020) train a classifier using 4,000 of the 2,473,874 documents in their corpus to analyze the association between Information Communication Technologies (ICTs) and the U.S. Department of State’s human rights reports. Although these approaches are more efficient than manually labeling an entire corpus, labeling thousands of documents still demands considerable time and effort, as the authors of these studies acknowledge.

To help researchers reduce the amount of labeled data required to train an accurate classification model, we introduce activeText, a fast and easy-to-use algorithm for text classification that can be run on a standard laptop. Our method combines probabilistic modeling, semi-supervised learning (Nigam et al. 2000; Zhu and Goldberg 2022), and active learning (McCallum and Nigam 1998; Miller, Linder, and Mebane 2020; Settles 2011) to help guide researchers to label documents more efficiently.

Recently, deep learning models like Bidirectional Encoder Representations from Transformers (BERT, Devlin et al. 2019) have become popular due to the impressive performance on many text classification tasks (Liu et al. 2019). However, they require substantial computational resources to train and can be prone to overfitting when labeled data are scarce (Yue et al. 2021). In addition, it is difficult to understand how their predictions are generated (Rudin 2019). Consequently, deep learning models may not be the best choice when researchers have limited computational resources and labeled data. We present activeText as an additional option for researchers who face these challenges.

The simple mixture model we use at the core of activeText runs an order of magnitude faster than deep learning models by employing the expectation–maximization (EM) algorithm (Dempster, Laird, and Rubin 1977) for parameter estimation. Because of the computational efficiency of our model, we embed it in an active learning framework where the most informative documents are selected for human labeling. As a result, users of activeText can rapidly iterate between training the model to generate estimates of label uncertainty and selecting informative documents for human labeling to train a classification model with high accuracy using only a small fraction of the documents in their corpus.

Another key feature of our approach is that the parameters of our mixture model can be easily inspected to understand how the model is making its predictions. We leverage this interpretability to allow users with existing subject expertise to boost classification performance by upweighting keywords associated with each classification category. All these features make of activeText an attractive option for social science researchers for whom labeled data and computational resources are scarce.

We demonstrate the performance of activeText in three ways. First, we conduct a series of validation experiments to assess our performance on four common political science classification tasks using real text corpora: identifying news articles as political, identifying toxic hate speech, classifying the topic of supreme court rulings, and identifying mentions of physical integrity violations in human rights reports. Our validation experiments show that when there are few labeled documents, activeText generally outperforms alternatives, including DistilBERT (Sanh et al. 2019), a more computationally efficient variant of BERT, in terms of classification performance. We show that this benefit is most pronounced when the corpus is unbalanced, and that upweighting keywords can boost further model performance. We also show that in contrast to models such as BERT, which require substantial computational resources to train, activeText can easily be run on a standard laptop using the statistical programming language R, with the prediction of classification labels typically completing in seconds for a corpus with tens of thousands of documents.

Second, we replicate Gohdes (2020) and Park, Greene, and Colaresi (2020) using activeText to show how researchers could have used our method to reach the same substantive conclusions—a higher level of Internet access is associated with a larger proportion of targeted killings, and ICTs are not associated with the sentiment of the State Department’s human rights reports, respectively—with far fewer labeled documents.

Third, we use simulations to explore the general conditions under which activeText performs well, and to evaluate the impact of mislabeling documents and the potential biases introduced by active learning on the classification performance of activeText. We show that activeText is robust to minor instances of mislabeling and that the in-sample bias introduced by active learning does not affect out-of-sample classification performance.

This article proceeds as follows: In the Section “Machine Learning Approaches to Text Classification,” we introduce readers to the concepts of semi-supervised and active learning approaches to text classification. In the Section “The Method,” we describe both the semi-supervised and the active learning components of activeText, and how we combine the two. In the Section “Validation Performance,” we show the results from comparing our model to popular alternatives on validation datasets. Then, the Section “Reanalysis with Fewer Human Annotations” presents the results of our replication studies. Finally, we discuss several practical concerns, directions for future research, and possible improvements to the algorithm in the Section “Discussion.” Code and data to reproduce the analysis of this article are available at the American Political Science Review Dataverse (Bosley et al. 2024).

MACHINE LEARNING APPROACHES TO TEXT CLASSIFICATION

In social science research, it is common for researchers to want to classify a large collection of text documents into two or more categories based on the content of the text in order to test a substantive hypothesis. The biggest impediment to this process is the cost of manually labeling a large collection of text documents. Rather than exhaustively labeling all documents, researchers often use machine learning techniques to automate the process, with supervised learning being the most common paradigm. In supervised learning, a model is trained on a labeled dataset to learn the relationship between text features and class labels (Kotsiantis 2007), and a variety of supervised learning algorithms, such as naive Bayes, support vector machine (SVM), and logistic regression, have been applied to text classification tasks in political science research (Colleoni, Rozza, and Arvidsson 2014; Hillard, Purpura, and Wilkerson 2008).

Even though supervised learning reduces the amount of labeling required relative to hand-coding all documents, it still requires a substantial amount of labeled data to train a model that generalizes well to the entire corpus, typically in the order of several thousand labeled documents. In most applied political science applications, however, researchers start with little to no labeled data, making it laborious to label sufficient data to train accurate classifiers. In this section, we discuss two machine learning approaches that we leverage in our method to address the challenge of labeled data scarcity: semi-supervised learning and active learning. ²

Solutions to Labeled Data Scarcity

To address the challenge of labeled data scarcity, several approaches have been proposed in the machine learning literature, including semi-supervised and active learning. Semi-supervised learning aims to leverage the structure of large amounts of unlabeled data to improve classification performance (Miller and Uyar 1996; Nigam et al. 2000). In a semi-supervised setting, the model learns from both labeled and unlabeled data, using the labeled data as a foundation for measurement and incorporating patterns recovered from the unlabeled data to produce more accurate and robust predictions. This approach is particularly useful when labeled data are scarce, but unlabeled data are abundant.

Active learning, on the other hand, focuses on strategically selecting the most informative instances for labeling, minimizing the labeling effort while maximizing the model’s performance (Settles 2011). One of the most studied active learning approaches is uncertainty sampling (Lewis and Gale 1994; Yang et al. 2015), where documents are chosen for labeling based on how uncertain the model is about their correct classification. By focusing labeling efforts on these informative documents, active learning can learn the decision boundary more efficiently than randomly selecting documents for labeling. In addition, active learning approaches have been shown to be particularly effective when the classification categories are imbalanced, which is a common occurrence in social science classification exercises (Miller, Linder, and Mebane 2020).

An active learning algorithm typically involves a sequence of iterative steps applicable to any classification methodology. The first step is to estimate the probability that each document belongs to a specific classification outcome. The second step involves actively selecting the documents that the model is most uncertain about and focusing manual labeling efforts among those documents (Hoi, Jin, and Lyu 2006). Then, the class probabilities are re-estimated using the newly labeled data. The algorithm cycles through these steps until a stopping criterion is met, such as a fixed budget for labeling (Ishibashi and Hino 2020) or a threshold for improvement in accuracy metrics such as precision, recall, or F1 score (Altschuler and Bloodgood 2019).

To illustrate the difference between passive and active learning for labeling a document, consider the scenario where a researcher aims to classify each unlabeled (U) document as either political (P) or nonpolitical (N) based on the frequency of terms like “Spending” and “Gridlock” (Figure 1, panel a, presents the corpus). In passive learning (panel b), the next document to be labeled is randomly selected, regardless of its position in the feature space. In contrast, active learning (panel c) prioritizes labeling documents in the region of uncertainty (shaded region), where the model is less confident about their true labels. By focusing labeling efforts on these informative documents, active learning can learn the decision boundary more efficiently than passive learning

Figure 1. Labeling: Passive vs. Active Learning

Note: Panel a presents a corpus where a classifier based on term frequencies of “Spending” and “Gridlock” is utilized to categorize unlabeled (U) documents as political (P) and nonpolitical (N). Panel b depicts a passive learning approach where the next document to be labeled is randomly selected. In contrast, panel c demonstrates active learning, where obtaining the true label of the U located in the region of uncertainty for the classifier (shaded region) is prioritized, as it provides more informative insights into learning the decision boundary between P and N.

Deep Learning and activeText

In recent years, transfer learning, especially using deep learning approaches and pretrained embeddings, has become popular. Transfer learning is a machine learning technique where knowledge gained from solving one problem is applied to a different but related problem, often leading to improved performance and reduced training time compared to training from scratch (Ruder et al. 2019). This approach relies on leveraging large pretrained models, such as BERT (Devlin et al. 2019), which have been trained on vast amounts of unlabeled text data using the Transformer architecture (Vaswani et al. 2017) to learn rich, contextual representations of words and sentences. Rather than encode text as a sparse vector of word frequencies and learn the relationship between text features and class labels, as in the bag-of-words representation, these models learn embeddings—dense, vector representations of words that capture semantic and syntactic similarities between words and documents. Once the embedding representations of text data are learned, they can be fine-tuned toward a specific classification task using a collection of labeled data, ³ allowing the model to adapt its learned representations to the specific domain and task at hand.

While transfer learning with deep learning models has been shown to excel at many text classification tasks (Devlin et al. 2019; Liu et al. 2019), simpler models still have a place in the text classification toolkit, especially when labeled data and computing resources are scarce. Deep learning models require significant computational resources and can be time-consuming to train, even when fine-tuning pretrained models (Strubell, Ganesh, and McCallum 2019), and require substantial technical expertise in machine learning and natural language processing to implement relative to simpler models. They also have an extremely large number of parameters, making them more prone to overfitting when labeled data are limited (Yue et al. 2021). ⁴ In addition, their complex architectures and high-dimensional representations can make them difficult to interpret (Guidotti et al. 2018). For a model to be interpretable, we mean both that the model’s predictions can be explained in terms of the input features and that the model’s parameters can be used to gain insights into the underlying phenomena and test substantive theories, both of which are essential in political science research. ⁵

Because of these limitations, we argue that when labeled data are scarce, computational resources are limited, and model interpretability is crucial—that is, the conditions under which the typical political scientist operates—combining semi-supervised and active learning techniques with a simple mixture model ⁶ is a viable alternative to deep learning approaches at a fraction of the computational cost. ⁷

In the following sections, we propose activeText, a novel method that combines semi-supervised learning and active learning with a generative mixture model based on bag-of-words representations of text data. Our approach leverages the EM algorithm to learn from both labeled and unlabeled data and incorporates uncertainty-based active learning to strategically select examples for labeling. We demonstrate the effectiveness of our approach through experiments and case studies on real-world political science datasets, highlighting its performance, interpretability, and computational efficiency compared to alternative methods.

THE METHOD

In this section, we present our modeling strategy and describe our active learning algorithm. For the probabilistic model (a mixture model for discrete data) at the heart of the algorithm, we build on the work of Nigam et al. (2000), who show that probabilistic classifiers can be augmented by combining the information coming from labeled and unlabeled data. As we will discuss below, we insert our model into an active learning algorithm and use the EM algorithm to maximize the observed-data log-likelihood function and estimate the model parameters.

Model

Consider the task of classifying N documents as one of two classes (e.g., political vs. nonpolitical). Let $ \mathbf{D} $ be a $ N\times V $ document feature matrix, where V is the number of features. ⁸ In most applications, features are words, but they can also be bi-grams, tri-grams, or other tokens such word embeddings, etc. We use $ \mathbf{Z} $ , a vector of length N, where each entry represents the class assigned to each document. If a document i is assigned to the kth class (out of K classes), then $ {Z}_i=k $ , where $ k\in \{0,1\} $ (e.g., $ k=1 $ represents the class of documents about politics, and $ k=0 $ those that are nonpolitical). Because we use a semi-supervised approach, some documents are already hand-labeled. This means that the value of $ {Z}_i $ is known for the labeled documents and is unknown for unlabeled documents.

To facilitate exposition, we assume that the classification goal is binary, however, our approach can be extended to accommodate (1) multiclass classification, where $ K>2 $ and each document is classified into one of the K classes, for example, classifying news articles into three classes: politics, business, and sports (see Section D of the Supplementary Material); and (2) modeling more than two classes but keeping the final classification output binary (see Section E of the Supplementary Material). ⁹

Equations 1–7 summarize the model:

Labeled Data:

(1) $$ \begin{array}{rl}\hskip4em {Z}_i=k& \to \mathrm{hand}\hbox{-} \mathrm{coded},\hskip1em k\in \{0,1\}.\end{array} $$

(2)

(3)

Unlabeled Data:

(4) $$ \begin{array}{rl}\pi & \hskip-1.3em \sim \mathrm{Beta}({\alpha}_0,{\alpha}_1).\end{array} $$

(5)

(6)

(7)

If document i is unlabeled, we first draw the parameter $ \pi =p({Z}_i=1) $ , the overall probability that any given document belongs to the first class (e.g., political documents), from a Beta distribution with hyperparameters $ {\alpha}_0 $ and $ {\alpha}_1 $ . ¹⁰ Similarly, for the other class (e.g., nonpolitical documents), we have that $ 1-\pi =p({Z}_i=0) $ . Given $ \pi $ , for each document indexed by i, we draw the class assignment indicator $ {Z}_i $ from a Bernoulli distribution. ¹¹ Then, we draw features for document i from a multinomial distribution governed by the total number of words in document i ( $ {n}_i $ ) and the vector $ {\boldsymbol{\eta}}_{\cdot k} $ represents the kth column of the $ V\times K $ matrix $ \boldsymbol{\eta} $ , where each entry of $ {\boldsymbol{\eta}}_{\cdot k} $ is represented by the scalar $ {\eta}_{vk}=p({D}_{iv}|{Z}_i=k) $ . The prior of $ {\boldsymbol{\eta}}_{\cdot k} $ is the Dirichlet distribution with hyperparameter vector $ {\boldsymbol{\beta}}_{\cdot k} $ (the kth column of the $ V\times K $ matrix $ \boldsymbol{\beta} $ ). Finally, $ {\mathbf{D}}_{i\cdot } $ is a row vector of length V that represents the word counts of document i. Conditional on $ {Z}_i=k $ , $ {\mathbf{D}}_{i\cdot } $ is drawn from a multinomial distribution with parameters $ {n}_i $ and $ {\boldsymbol{\eta}}_{\cdot k} $ . If document i has a label, the key distinction from the scenario with unlabeled data is that each $ {Z}_i $ is not drawn from a Bernoulli distribution. Instead, its value is manually determined through hand-coding. ¹² Other than this point, the structure of the model remains unchanged for the labeled data.

Altogether, if we denote $ {\mathcal{L}}_{\mathrm{obs}}(\pi, \boldsymbol{\eta} |\mathbf{D},\mathbf{Z},\lambda ) $ as the observed data log-likelihood for all the data, based on Equations 1–7, then we can express it as

(8) $$ \begin{array}{r}{\mathcal{L}}_{\mathrm{obs}}(\pi, \boldsymbol{\eta} |\mathbf{D},\mathbf{Z},\lambda )={\mathcal{L}}_{\mathrm{labeled}}(\pi, \boldsymbol{\eta} |{\mathbf{D}}_{\mathrm{labeled}},{\mathbf{Z}}_{\mathrm{labeled}})\\ {}+\lambda \times {\mathcal{L}}_{\mathrm{unlabeled}}(\pi, \boldsymbol{\eta} |{\mathbf{D}}_{\mathrm{unlabeled}},{\mathbf{Z}}_{\mathrm{unlabeled}}).& \end{array} $$

In other words, the result of adding the information from the observed log-likelihoods for the labeled and unlabeled data, respectively, and where, $ \lambda \in [0,1] $ is a parameter that adjusts the influence originating from the unlabeled data on the observed data log-likelihood. The inclusion of such a parameter follows from the scarcity of labeled data compared to the abundance of unlabeled data, which is a significant challenge in implementing semi-supervised learning approaches, as the likelihood function of text in unlabeled documents is likely to overwhelm that of the labels. This is a common problem with combining information from text and other sources through likelihood-based methods, as text data usually contain an order of magnitude more observed variables—features—than other types of data. To ensure that a classifier effectively extracts information from labeled data and is not solely influenced by unlabeled data, it is crucial to enhance the relative importance of labeled data; otherwise, the signal from labeled data will be overshadowed by the overwhelming presence of unlabeled data. To address this, we weight information from unlabeled documents by utilizing a decision factor, λ (Nigam et al. 2000). ¹³ When λ equals 1, the model equally considers each document, irrespective of whether it is labeled by human supervision or labeled probabilistically by the algorithm. As λ moves from 1 to 0, the model reduces the importance of information contributed by probabilistically labeled documents in the estimation of η and π. When λ reaches 0, the model disregards the information from all probabilistically labeled documents, turning it into a supervised algorithm.

Additionally, note that an important advantage of the interpretability of the key model parameters facilitates augmentation of the model using additional information such as domain knowledge. For example, each element of $ {\boldsymbol{\eta}}_{\cdot k} $ represents the probability of observing a unique feature given the class of the document. In the Section “Active Keyword Upweighting,” we show how to augment the model to allow some features to be highly associated with an specific class and in that way improve performance.

Finally, because the observed data log-likelihood of our model is difficult to maximize, we use the EM algorithm to estimate the parameters. ¹⁴

An Active Learning Algorithm

Our active learning algorithm (see Algorithm 1) can be split into the following steps: estimation of the probability that each unlabeled document belongs to the positive class, selection of the unlabeled documents whose predicted class is most uncertain, and labeling of the selected documents by human coders. The algorithm iterates until a stopping criterion is met. In this section, we also describe an optional keyword upweighting feature, where a set of user-provided keywords provide prior information about the likelihood that a word is generated by a given class to the model. These keywords can either be provided at the outset of the model or identified during the active learning process.

We now proceed to describe in detail each step of our algorithm:

Algorithm 1. Active Learning with EM Algorithm to Classify Text

Result: Obtain predicted classes of all documents.

Randomly select a small subset of documents, and ask humans to label them

[Active Keyword]: Ask humans to provide initial keywords

While: Stopping conditions are not met yet do

1. [Active Keyword]: Up-weight the important of keywords associated with a class;

2. Predict labels for unlabeled documents using EM algorithm

3. Select documents with the highest uncertainty among unlabeled documents, and ask humans to label them

4. [Active Keyword]: Select words most strongly associated with each class, and ask humans to label them;

5. Update sets of labeled and unlabeled documents for the next iteration

end

Estimation

In the first iteration, the model is initialized with a small number of labeled documents. ¹⁵ The information from these documents is used to estimate the parameters of the model: the probability of a document being, for example, about politics, $ \pi $ , and $ V\times 2 $ matrix $ \boldsymbol{\eta} $ , represents the feature-class probabilities. If there is no labeled data, the model can be initialized by manually assigning initial values to the model parameters. These values can be set randomly or to a fixed value. From the second iteration on, we use information from both labeled and unlabeled documents to estimate the parameters using the EM algorithm, with the log-likelihood of unlabeled documents being weighted by $ \lambda, $ and with the η and π values from the previous iteration as the initial values. Using the estimated parameters, we compute the probability that each unlabeled document belongs to the politics class.

Selection

Using the predicted probability that each unlabeled document belongs to the politics class, we use Shannon Entropy (i.e., the level of uncertainty) to determine which of the probabilistically labeled documents it was least certain about. In the binary classification case, this is the equivalent of calculating the absolute value of the distance between the politics class probability and 0.50 for each document. Using this criterion, the model ranks all probabilistically labeled documents in descending order of uncertainty. The n most uncertain documents are then selected for human labeling, where n is the number of documents to be labeled by humans at each iteration.

Labeling

A human coder reads each document selected by the algorithm and imputes the “correct” label. For example, the researcher may be asked to label as political or nonpolitical each of the following sentences:

The 2020 Presidential Election had the highest turnout in U.S. history $ \to $ [Political]

Argentina wins the 2022 FIFA World Cup, defeating France $ \to $ [Nonpolitical]

These newly labeled documents are then added to the set of human-labeled documents, and the process is repeated from the estimation stage.

Stopping Rule

Our method is highly modular and supports a variety of stopping rules. This includes an internal stability criterion, where stoppage is based on small amounts of change of the internal model parameters, as well as the use of a small held-out validation set to assess the marginal benefit of labeling additional documents on measures of model evaluation such as accuracy or F1. With either rule, the researcher specifies some bound such that if the change in model parameters or out-of-sample performance is less than the prespecified bound, then the labeling process ends. For example, we use the out-of-sample validation stopping rule with a bound of 0.01 for the F1 score in Section “Reanalysis with Fewer Human Annotations.”

Active Keyword Upweighting

The researcher also has the option to use an active keyword upweighting scheme, where a set of keywords is used to provide additional information. This is done by incrementing elements of the β (the prior parameter of $ \boldsymbol{\eta} $ ) by γ, a scalar value chosen by the researcher. In other words, we impose a tight prior on the probability that a given keyword is associated with each class. ¹⁶ To build the set of keywords for each class, (1) activeText proposes a set of candidate words, (2) the researcher decides whether they are indeed keywords or not, ¹⁷ and (3) activeText updates the parameters based on the set of keywords.

To select a set of candidate keywords, activeText calculates the ratio that each word was generated by a particular class using the η parameter. Specifically, it computes $ {\eta}_{vk}/{\eta}_{v{k}^{\prime }} $ for $ k=\{0,1\} $ with $ {k}^{\prime } $ the opposite class of k, and chooses top m words whose $ {\eta}_{vk}/{\eta}_{v{k}^{\prime }} $ are the highest as candidate keywords to be queried for class k. ¹⁸ Intuitively, words closely associated with the classification classes are proposed as candidate keywords. For example, words such as “vote,” “election,” and “president,” are likely to be proposed as the keywords for the political class of documents in the classification between political vs. nonpolitical documents.

After activeText proposes candidate keywords, the researcher decides whether they are indeed keywords or not. This is where the researcher can use her expertise to provide additional information. For example, she can decide names of legislators and acronyms of bills as keywords for the political class.

Using the set of keywords for each class, activeText creates a V × 2 keyword-class matrix κ , where each element κ _vk takes the value of γ if word v is a keyword for class k, otherwise 0. Before we estimate parameters in each active iteration, we perform a matrix sum β ← κ + β to incorporate information from keywords. The keyword approach therefore effectively upweights our model with prior information about words that the researcher thinks are likely to be associated with one class rather than another.

VALIDATION PERFORMANCE

This section shows the performance comparisons between activeText and other classification methods. First, we show comparisons between active and passive learning. Then, we compare classification and time performance between activeText and a version of BERT called DistilBERT, a state-of-the-art text classification model using word embeddings as vector representations of the data. ¹⁹ Finally, we show how keyword upweighting can improve classification accuracy.

We compare the classification performance on each of the following sets of documents: internal forum conversations of Wikipedia editors (class of interest: toxic comment), BBC News articles (political topic), the United States Supreme Court decisions (criminal procedure), and Human Rights allegations (physical integrity rights allegation). ²⁰ We use 80% of each dataset for the training data and hold out the remaining 20% for evaluation. Documents to be labeled are sampled only from the training set, and documents in the test set are not included to train the classifier, even in our semi-supervised approach. The out-of-sample F1 score is calculated using the held-out testing data. ²¹

Classification Performance

Figure 2 shows the results from three model specifications: activeText (denoted by the solid line); Random Mixture, a version of activeText that uses passive instead of active learning (denoted by the dotted line); and DistilBERT (denoted by the dashed line).

Figure 2. Comparison of Classification Results with Random and Active Versions of activeText and DistilBERT

Each panel corresponds to a unique combination of a dataset and the proportion of documents associated with the class of interest, with the rows corresponding to the datasets and the columns corresponding to the proportions. The parentheses beside the name of each corpus represent the proportion of positive labels in the population configuration, that is, the proportion of documents in the corpus that are labeled as the class of interest. ²² Within each panel, the x-axis represents the number of documents labeled, and the y-axis represents the average out-of-sample F1 score averaged across 50 and 10 Monte Carlo simulations in the case of the activeText models and the DistilBERT model, respectively. In the activeText models, 20 documents are labeled in each iteration. ²³

There are two key takeaways from Figure 2. First, we show that activeText is either equivalent to or outperforms its random sampling counterpart in nearly all cases, and the benefit from active learning is larger when the proportion of documents in the class of interest is smaller. The exception is the Human Rights corpus, where the benefit of active learning is marginal, and where at the 50% proportion, random sampling slightly outperforms active learning with less than two hundred labeled documents.

Second, we show that in nearly all cases, activeText either outperforms or performs comparably to the DistilBERT model. As in the comparison between the active and random versions of activeText, the advantage of activeText is larger when the proportion of documents in the class of interest is small. This is particularly true in the case of the BBC, Supreme Court, and Wikipedia corpora for the 5% and population specifications. This advantage is not permanent, however: as the number of labeled documents increases, DistilBERT (as expected) performs well and even exceeds the F1 score of activeText in the case of Wikipedia. As before, the exception is the Human Rights corpus, where DistilBERT outperforms activeText at the 50% and population levels. ²⁴

The early poor performance of activeText on the Human Rights corpus may be due to the fact that documents are short. Short-labeled documents provide less information, making it more difficult for the model to distinguish between classes. We discuss how the information can be augmented using keywords to improve our method’s classification performance in Section “Benefits of Keyword Upweighting.” The keyword upweighting we propose takes advantage of the substantive interpretability of the feature-class matrix $ \boldsymbol{\eta} $ in our generative model.

Runtime

In Figure 3, we compare computational runtime for activeText and DistilBERT. For this analysis, our goal was to compare how long it would take a researcher without access to a high-performance computing (HPC) platform or an expensive GPU to train these models. To this end, we trained the activeText and DistilBERT models on a base model M1 Macbook Air with 8 GB of RAM and seven GPU cores. While the activeText models were trained using a single central processing unit (CPU), we used the recent implementation of support for the GPU in M1 Macs in PyTorch ²⁵ to parallelize the training of the BERT model using the M1 Mac’s GPU cores. ²⁶ We also computed the time values cumulatively for activeText since it is expected that model will be fit over and over again as part of the active learning process, whereas for a model like BERT, we expect that the model would only be run once, and as such do not calculate its run-time cumulatively. For the Human Rights and Wikipedia corpora, which each have several hundred thousand entries, we used a random subsample of fifty thousand documents. For the Supreme Court and BBC corpora, we used the full samples. Finally, we present the time results in logarithmic scale to improve visual interpretation.

Figure 3. Comparison of Classification and Time Results across activeText and DistilBERT

Figure 3 shows that using DistilBERT comes at a cost of several orders of magnitude of computation time relative to activeText. Using the Wikipedia corpus as an example, at five hundred documents labeled the baseline activeText would have run to convergence 25 times, and the sum total of that computation time would have amounted to just under 100 seconds. With DistilBERT, however, training a model with five hundred documents and labeling the remaining 45,500 on an average personal computer would take approximately 10,000 seconds (2.78 hours).

Benefits of Keyword Upweighting

In Figure 2, active learning did not improve the performance on the human rights corpus, and the F1 score was lower than other corpora in general. One reason for the early poor performance of activeText may be the length of the documents. Because each document of the human rights corpus consists of one sentence only, the average length is shorter than other corpora. ²⁷ This means that the information the models can learn from labeled documents is less compared to the other corpora with longer documents. ²⁸ In situations like this, providing keywords in addition to document labels can improve classification performance because it directly shifts the values of the feature-class probability matrix, $ \boldsymbol{\eta}, $ even when the provided keywords is not in the already labeled documents.

Figure 4 compares the performance with and without providing keywords. The darker lines show the results with keywords and the lighter lines without. The columns specify the proportion of documents associated with the class of interests: 5%, 50%, and the population proportion (16%). As in the previous exercises, 20 documents are labeled at each sampling step, and one hundred Monte Carlo simulations are performed to stabilize the randomness due to the initial set of documents to be labeled. We simulated the process of a user starting with no keywords for either class and then being queried with extreme words indexed by v whose $ {\eta}_{vk}/{\eta}_{v{k}^{\prime }} $ is the highest for each class k, with up to 10 keywords for each class being chosen based on the estimated $ \boldsymbol{\eta} $ at a given iteration of the active process. To determine whether a candidate keyword should be added to the list of keywords or not, our simulated user checked whether the word under consideration was among the set of most extreme words in the distribution of the “true” $ \boldsymbol{\eta} $ parameter, which we previously estimated by fitting our mixture model with the complete set of labeled documents. ²⁹

Figure 4. Classification Results of activeText with and without Keywords

The results suggest that providing keywords improves performance when the proportion of documents is markedly imbalanced across classes. The keywords scheme improved the performance when the number of labeled documents is smaller on the corpus with 5% or 16% (population) labels associated with the class of interest. By contrast, it did not on the corpus where both classes were evenly balanced. These results highlight that our active keyword approach benefits the most when the dataset suffers from serious class imbalance problems. ³⁰

One caveat is that we provided “true” keywords, in the sense that we used the estimated $ \boldsymbol{\eta} $ from a fully labeled dataset. In practice, researchers have to decide if candidate keywords are indeed keywords using their substantive knowledge. In this exercise, we believe that the keywords supplied to our simulation are what researchers with substantive knowledge about physical integrity rights can confidently adjudicate. For example, the keywords, such as “torture,” “beat,” and “murder,” match our substantive understanding of physical integrity right violation. Nevertheless, as we explain in Section “Labeling Error” and Section F of the Supplementary Material, humans can make mistakes, and some words may be difficult to judge. Thus, we examined the classification performance with varying degrees in the amount of error at the keyword labeling step. In Section K of the Supplementary Material, we show that the active keyword approach still improves the classification performance compared to the no-keyword approach—even in the presence of small amounts (less than 20%) of “honest” (random) measurement error in keyword labeling.

REANALYSIS WITH FEWER HUMAN ANNOTATIONS

To further illustrate the benefits of our proposed approach for text classification, we conduct reanalyses of two recently published aritcles: Gohdes (2020) and Park, Greene, and Colaresi (2020). We show that with activeText, we can arrive at the same substantive conclusions advanced by these authors but using only a small fraction of the labeled data they originally used.

Internet Accessibility and State Violence

In the article “Repression Technology: Internet Accessibility and State Violence,” Gohdes (2020) argues that higher levels of Internet accessibility are associated with increases in targeted repression by the state. The rationale behind this hypothesis is that through the rapid expansion of the Internet, governments have been able to improve their digital surveillance tools and target more accurately those in the opposition. Thus, even when digital censorship is commonly used to diminish the opposition’s capabilities, Gohdes (2020) claims that digital surveillance remains a powerful tool, especially in areas where the regime is not fully in control.

To measure the extent to which killings result from government targeting operations, Gohdes (2020) collects 65,274 reports related to lethal violence in Syria. These reports contain detailed information about the person killed, date, location, and cause of death. The period under study goes from June 2013 to April 2015. Among all the reports, 2,346 were hand-coded by Gohdes, and each hand-coded report can fall under one of three classes: (1) government-targeted killing, (2) government-untargeted killing, and (3) non-government killing. Using a document-feature matrix (based on the text of the reports) and the labels of the hand-coded reports, Gohdes (2020) trained and tested a state-of-the-art supervised decision tree algorithm (extreme gradient boosting, XGboost). Using the parameters learned at the training stage, Gohdes (2020) predicts the labels for the remaining reports for which the hand-coded labels are not available. For each one of the 14 Syrian governorates (the second largest administrative unit in Syria), Gohdes (2020) calculates the proportion of biweekly government targeted killings. In other words, Ghodes collapses the predictions from the classification stage at the governorate-biweekly level.

We replicate Gohdes (2020) classification tasks using activeText. In terms of data preparation, we adhere to the very same decisions made by Gohdes (2020). To do so, we use the same 2,346 hand-labeled reports (1,028 referred to untargeted killing, 705 to a targeted killing, and 613 a non-government killing) of which 80% were reserved for training and 20% to assess classification performance. In addition, we use the same document-feature matrices. ³¹ As noted in Section “An Active Learning Algorithm,” because activeText selects (at random) a small number of documents to be hand-labeled to initialize the process, we conduct one hundred Monte Carlo simulations and present the average performance across initializations. As in “Validation Performance,” we set $ \lambda =0.001 $ . The performance of activeText and XGboost is evaluated in terms of out-of-sample F1 score. Following the discussion above, we stopped the active labeling process at the 30th iteration when the out-of-sample F1 score stopped increasing by more than 0.01 units (our pre-specified threshold). Table 1 presents the results. ³² Overall, we find that as the number of active learning steps increases, the classification performance of activeText is similar to the one in Gohdes (2020). However, the number of hand-labeled documents that are required by activeText is significantly smaller (around one-third, as indicated by the bold text in Table 1) if compared to the ones used by Gohdes (2020).

Table 1. Classification Performance: Comparison with Gohdes (2020) Results

Note: Bold values represent the number of manually labeled documents and the classification performance if activeText stops at the iteration with less than 0.01 increase of the out-of-sample F1 score.

In social science research, text classification is often not the end goal but a means to quantify a concept that is difficult to measure and make inferences about the relationship between this concept and other constructs of interest. In that sense, to empirically test her claims, Gohdes (2020) conducts regression analyses where the proportion of biweekly government targeted killings is the dependent variable and Internet accessibility is the main independent variable—both covariates are measured at the governorate-biweekly level. Gohdes (2020) finds that there is a positive and statistically significant relationship between Internet access and the proportion of targeted killings by the Syrian government. Using the predictions from activeText, we construct the main dependent variable and replicate the main regression analyses in Gohdes (2020). ³³

Tables I.2 and I.3 in Section I.2 (Dataverse-only) report the estimated coefficients, across the same model specifications in Gohdes (2020). The point estimates and the standard errors are almost identical whether we use XGboost or activeText. Moreover, Figure 5 presents the expected proportion of targeted killings by region and Internet accessibility, using the preferred regression specification by Gohdes. This model, as detailed in column V of Tables I.2 and I.3 in Dataverse, incorporates the interaction between region and Internet accessibility. Gohdes finds that in the Alawi region, which is recognized for its loyalty to the regime, higher levels of Internet access correspond to a significantly lower expected proportion of targeted killings compared to other regions in Syria. In the absence of the Internet, however, there is no discernible difference across regions (see Figure 5, right panel). Our reanalysis does not change the substantive conclusions by Gohdes (2020) (Figure 5, left panel), however, it comes just at a fraction of the labeling efforts (labeling 620 instead of 1,876 reports).

Figure 5. Replication of Figure 3 in Gohdes (2020): Expected Proportion of Target Killings, Given Internet Accessibility and Whether a Region is Inhabitated by the Alawi Minority

Note: The results from activeText are presented in the left panel and those of Gohdes (2020) are on the right.

Human Rights Are Increasingly Plural

Park, Greene, and Colaresi (2020) investigate how the rapid growth (in the last four decades) of ICTs has changed the composition of texts referring to human rights, and show that the average sentiment with which human rights reports are written has not drastically changed over time. They claim that if one wants to really understand the effect of changes in the access to information on the composition of human rights reports, it is necessary to internalize the fact that human rights are plural (i.e., bundles of related concepts). In other words, the authors argue that having access to new information has indeed changed the taxonomy of human rights over time, even when there has not been a change in tone.

To empirically test such a proposition, Park, Greene, and Colaresi (2020) conduct a two-step approach. First, by training an SVM for text classification with three classes (negative, neutral, and positive sentiment), the authors show that the average sentiment of human rights reports has indeed remained stable even in periods where the amount of information available has become larger. ³⁴ Second, they use a network modeling approach to show that while the average sentiment of these reports has remained constant over time, the taxonomy has drastically changed. In this section, using activeText, we focus on replicating the text classification task of Park, Greene, and Colaresi (2020), which is key to motivating their puzzle.

As in the reanalyses of Gohdes (2020), we adhere to the same preprocessing decisions made by Park, Greene, and Colaresi (2020) when working with their corpus of Country Reports on Human Rights Practices from 1977 to 2016 by the U.S. Department of State. In particular, we use the same four hand-labeled human rights reports (1,182 are positive, 1,743 are negative, and 1,075 are neutral) and use the same document-feature matrices (which contain thirty thousand features, a combination of unigrams and bigrams). Again, we conduct one hundred Monte Carlo simulations and present the average performance across initializations. As shown in Figure J.1 in Section J (Dataverse-only), we stopped the active labeling process at the 25th iteration of our algorithm as the out-of-sample F1 score (from an 80/20 training/test split) does not increase by more than 0.01 units. ³⁵ Using the results from the classification task via activeText, the sentiment scores of 2,473,874 documents are predicted. With those predictions, we explore the evolution of the average sentiment of human rights reports per average information density score. ³⁶

Figure 6 shows that by labeling only five hundred documents with activeText, instead of four thousand labeled documents used by Park, Greene, and Colaresi (2020) to fit their SVM classifier, we arrive at the same substantive conclusion: the average sentiment of human rights reports has remained stable and almost neutral over time. In Figure J.2 in Section J (Dataverse-only), we also show that this result is not an artifact of our stopping rule and it is robust to the inclusion of additional label documents (e.g., labeling 1,000, 1,500, and 2,000 documents instead of just 500).

Figure 6. Replication of Figure 1 in Park, Greene, and Colaresi (2020): The Relationship between Information Density and Average Sentiment Score

DISCUSSION

In this section, we address three key issues concerning our proposed approach: (1) the (in-sample) bias that occurs when actively selecting observations to train a model; (2) the impact of mislabeling documents and keywords; and (3) the practical considerations about downweighting unlabeled data.

The Bias of Active Learning

As highlighted by Dasgupta (2011), Dasgupta and Hsu (2008), and Farquhar, Gal, and Rainforth (2021), active learning introduces in-sample statistical bias due to training non-i.i.d data. As described above, active learning involves selecting the most informative data points for training. This selection can introduce bias because the newly labeled documents may not represent the entire of the target population.

To illustrate the nature of this bias, let’s denote the population risk as $ r={\unicode{x1D53C}}_{\mathbf{Z},\mathbf{D}}[L({Z}_i,{\hat{Z}}_i)] $ . Here, L represents a loss function, such as the L1 or L2 norms. For each observation indexed by i, $ {Z}_i $ denotes its true label, and $ {\hat{Z}}_i $ denotes the predictions made based on the model parameters and the data’s features ( $ \mathbf{D} $ ). It is important to note that $ {\unicode{x1D53C}}_{\mathbf{Z},\mathbf{D}} $ indicates the expectation calculated over the joint distribution of $ \mathbf{Z} $ and $ \mathbf{D} $ . In the training stage, we want to find the model parameters that minimize the population risk. However, the population risk is not observed, and we can only estimate it using the labeled data. Hence, the empirical risk, derived from an i.i.d sample of the population with a size denoted as $ {N}^l $ , can be computed by the formula: $ \hat{R}=\frac{1}{N^l}{\sum}_{j=1}^{N^l}[L({Z}_j,{\hat{Z}}_j)] $ . This calculation provides an unbiased estimate of r because the labeled data are randomly selected from the population. With active learning, the empirical risk is $ \overset{\sim }{R}=\frac{1}{M}{\sum}_{m=1}^M[L({Z}_m,{\hat{Z}}_m)] $ , where M is the number of labeled data selected actively. Because these M data points are not a random sample of the population, $ \overset{\sim }{R} $ is no longer an unbiased estimator of r.

Farquhar, Gal, and Rainforth (2021) propose an unbiased estimator of the population risk with active learning, called leveled unbiased risk estimator (LURE), $ {\overset{\sim }{R}}_{\mathrm{lure}}=\frac{1}{M}{\sum}_{m=1}^M[{v}_mL({Z}_m,{\hat{Z}}_m)] $ , where $ {v}_m $ is a function of the sampling probabilities of the actively selected documents (refer to Section F of the Supplementary Material for more details). They show that $ {\overset{\sim }{R}}_{\mathrm{lure}} $ is an unbiased estimate with minimum variance in its class of weighted estimators for the empirical risk. In other words, the in-sample bias in the active learning process can be corrected by $ {\overset{\sim }{R}}_{\mathrm{lure}} $ (refer to Theorems 3 and 4 in Farquhar, Gal, and Rainforth 2021).

Importantly, according to Farquhar, Gal, and Rainforth (2021), one factor that determines whether the in-sample bias correction improves out-of-sample predictive performance is the complexity of the model. For example, they empirically show that the in-sample bias correction with LURE improves the out-of-sample classification performance for a simple linear regression, but not for a neural network. Farquhar, Gal, and Rainforth (2021) argue that for overparameterized models, correcting the in-sample bias from active learning might not be advantageous to improve out-of-sample classification because active learning can serve as a regularization mechanism against overfitting bias. While activeText does not possess as many parameters as a neural network, it has many more parameters than linear regressions depending on the number of features of the data. This implies that it is an open question whether the in-sample bias correction improves the out-of-sample predictive performance of activeText.

To examine whether the in-sample statistical bias has adverse effects on the out-of-sample classification performance, we conducted a series of simulation studies involving 108 different configurations. In our simulation studies, we manipulated various aspects of the simulated data, such as the number of unique words, the average length of the documents (measured in number of words), the difficulty of classification, and the proportion of positive class documents in the corpus.

Figure 7 presents the results of implementing the LURE bias correction to activeText under a simulation setup. This setup involves generating simulation data with one thousand documents, five hundred unique features, an average of 50 features per document, and the reference class accounting for 10% of the corpus. We perform one hundred Monte Carlo simulations in this context. The left panel presents the in-sample bias of the $ \overset{\sim }{R} $ and $ {\overset{\sim }{R}}_{\mathrm{lure}} $ , and the right panel presents the corresponding out-of-sample F1 scores. The bias is calculated as the difference between the population risk and the empirical risk. For active learning, this is represented as $ r-\unicode{x1D53C}[\overset{\sim }{R}] $ , while for its bias-corrected version, it is $ r-\unicode{x1D53C}[{\overset{\sim }{R}}_{\mathrm{lure}}] $ .

Figure 7. Bias of the Empirical Risk for Labeled Data (Left Panel) and Out-of-Sample Classification Performance (Right Panel) of activeText and activeText+LURE

Note: For each panel, the x-axis represents the number of documents labeled and the y-axis represents the average bias and average out-of-sample F1 score across one hundred Monte Carlo simulations. Shaded areas represent the 95% confidence intervals across Monte Carlo simulations.

Figure 7 shows that regarding the in-sample bias of the empirical risk, $ \overset{\sim }{R} $ exhibits an upward bias in the early stages of the labeling process, which gradually decreases as more documents are labeled. This bias arises because the most uncertain documents are labeled first in active learning, causing the empirical risk to initially surpass the population risk. In contrast, we find that LURE weights effectively eliminate the in-sample bias of activeText. However, as the right panel of Figure 7 shows, the unadjusted version of activeText demonstrates better out-of-sample classification performance compared to its bias-corrected counterpart. Consequently, our findings indicate that addressing in-sample bias does not necessarily improve the out-of-sample classification performance of activeText. These results hold across different simulation settings and in our validation data. For a more detailed overview of the simulation results, interested readers can refer to Section F of the Supplementary Material.

Labeling Error

Although our main findings operate under the assumption that labelers are accurate, it is important to acknowledge that human labelers can still make errors. We will now investigate how mislabeling of documents and keywords that are actively selected impacts the classification performance of activeText. Specifically, we aim to demonstrate the potential impact of measurement errors in labeling, particularly focusing on the effect of “honest” mistakes (classical measurement error) on classification performance and basic downstream analysis.

In the case of mislabeling actively selected documents, our results show that random perturbations from true document labels do hurt the classification performance. ³⁷ For example, in Section G.1 of the Supplementary Material, we find that in the case of the BBC News articles dataset described above, when about 20 out of two hundred documents are labeled with the incorrect label (10% mislabeling), the out-of-sample F1 score remains high at around 0.87. However, when the mislabeling of documents exceeds one in five documents ( $ \ge $ 20% mislabeling), there is a significant decrease in the out-of-sample F1 score (the F1 is less than 0.75). This pattern holds across all validation datasets (refer to Figure G.1 in Section G.1 of the Supplementary Material).

To illustrate how mislabeling affects downstream analyses, we consider a simple example. Suppose we are trying to predict the number of documents related to a specific category, like politics in the BBC data. In our validation studies, we already know the actual proportions of documents in the categories we are interested in. For instance, in the BBC dataset, 19% of the articles cover politics, while in the Wikipedia corpus, 9% of the documents are deemed toxic. Similarly, 26% of Supreme Court cases involve criminal procedure, and 16% of Human Rights reports include allegations of physical integrity rights violations. To gauge the impact of mislabeling, we assess the bias in the predictions for the proportion of documents in the target class made by our model. As illustrated in Figure G.2 in Section G.1 of the Supplementary Material, the bias increases as the rate of mislabeling rises. For example, in the Wikipedia comments dataset, if we accurately label around two hundred documents, the bias is minimal. However, introducing 30% mislabeling results in a bias increase of 0.25 units. This trend is consistent across all datasets, mirroring what we observed with the F1 score. Essentially, even minor unintentional errors (honest mistakes) diminish accuracy when computing simple summary statistics such as a sample mean.

In contrast to our results for the mislabeling of documents, if compared to the no-keyword approach, a small amount of classical measurement error on keyword labeling does not hurt the classification performance. ³⁸ For example, the results presented in Figure G.3 in Section G.2 of the Supplementary Material shows that when random noise is introduced, the classification performance of activeText for binary classification decreases slightly as the proportion of mislabeled keywords increases to 30% or more. This trend remains consistent across different validation datasets and values of $ \gamma $ (the upweight assigned to each keyword).

In light of these results, we believe that future research focus on developing new active learning algorithms that prioritize assigning labelers based on their labeling expertise and adapt to various types of labeling errors. One approach could involve allocating the most skilled labelers to annotate the most uncertain or challenging documents, while assigning simpler tasks to less proficient labelers. This strategy could optimize the efficiency of the labeling process. Additionally, as we discussed above, inaccurate predictions can introduce bias, particularly in downstream tasks. This bias can be exacerbated by departures from classical measurement error, making it difficult to determine its direction. Therefore, as recently recognized by authors such as Knox, Lucas, and Cho (2022) and Fong and Tyler (2021) further investigation is necessary to directly tackle these potential biases, especially in settings such as a poplar inference methods such as a regression framework. ³⁹

Tuning the Value of $ \lambda $

As noted above, we downweight the information from unlabeled documents as we typically have more unlabeled than labeled documents. Moreover, since the labeled documents have been classified by an expert, we want to rely more on the information they bring for prediction.

An important practical consideration is how to select the appropriate value of $ \lambda $ . One possible approach would be to adopt popular model selection methods (e.g., cross-validation) to choose the appropriate $ \lambda $ value during the model initialization process. However, cross-validation may not be practical when the labeled data are scarce (or absent at the beginning of the process). We have consistently observed across a variety of applications that very small values (e.g., 0, 0.001, or 0.01) work the best on the corpora we used (see Figures H.1 and H.2 in Section H.1 [Dataverse-only]). However, more work is needed to clearly understand the optimality criteria needed to select $ \lambda $ . We leave this question for future research.

CONCLUSION

Human labeling of documents is the most labor-intensive part of social science research that uses text data. For automated text classification to work, a machine classifier needs to be trained on the relationship between text features and class labels, and the labels in training data are given manually. In this article, we have described a new active learning algorithm that combines a mixture model and active learning to incorporate information from labeled and unlabeled documents and better select which documents to be labeled by a human coder. Our validation and simulation studies showed that a moderate number of documents are labeled, and the proposed algorithm performed at least as well as state-of-the-art methods such as BERT at a fraction of the cost. We replicated two published political science studies to show that our algorithm lead to the same conclusions as the original articles but needed much fewer labeled documents. In sum, our algorithm enables researchers to save their manual labeling efforts without sacrificing quality.

Machine learning techniques are becoming increasingly popular in political science, but the barrier to entry remains too high for researchers without a technical background to make use of advances in the field. As a result, there is an opportunity to democratize access to these methods. We believe that our model and R-package will provide applied researchers with a tool that they can use to efficiently categorize documents in corpora of varying sizes and topics.