Irony and sarcasm are two complex linguistic phenomena that are widely used in everyday language and especially over the social media, but they represent two serious issues for automated text understanding. Many labeled corpora have been extracted from several sources to accomplish this task, and it seems that sarcasm is conveyed in different ways for different domains. Nonetheless, very little work has been done for comparing different methods among the available corpora. Furthermore, usually, each author collects and uses their own datasets to evaluate his own method. In this paper, we show that sarcasm detection can be tackled by applying classical machine-learning algorithms to input texts sub-symbolically represented in a Latent Semantic space. The main consequence is that our studies establish both reference datasets and baselines for the sarcasm detection problem that could serve the scientific community to test newly proposed methods.

The analysis of societies, and of systems of business within them, has tended to be heavily influenced by institutionalist perspectives. Many scholars using this approach include culture as a subset of institutions, but often without specifying the logics of doing so. Others remove culture from the account, or acknowledge its significance without placing it clearly in their models. Culturalists, however, tend not to venture into the details of economic coordination and action. To resolve the theoretical challenges posed by this set of contrasting views, it is necessary to specify how culture works and how it is different from institutions. As ‘the societal effect’ is the influence of culture on institutions, it may thus be easier to study its workings. Here culture is seen, following Sorge, as meaning relevant within a series of semantic spaces, each related to a field of action, the total integrated coherently by social axioms binding the spaces and the meanings within them into a total societal fabric of meaning. The private sector of the Chinese economy is analysed, drawing from recent extensive empirical reports as to its functioning. Theory development is in line with business systems theory.