techniques like stemming and lemmatisation in order to obtain statistically relevant listing of the who, what, where & when in text through named-entity recognition and Topic model programs. The same methods have been applied with NLP techniques like a bag-of-words model to obtain statistical measures of emotional context through sentiment analysis programs. The accuracy of a sentiment analysis system is, in principle, how well it agrees with human judgments. Because evaluation of sentiment analysis is becoming more and more specialty based, each implementation needs a separate training model and specialized human verification raising Inter-rater reliability issues. However, the accuracy is considered generally acceptable for use in evaluating emotional context at a statistical or group level. A developmental trajectory of NLP to understand contextual pragmatics in text involving emulating intelligent behavior and apparent comprehension of natural language is cognitive NLP. This method is a rules based approach which involves assigning meaning to a word, phrase, sentence or piece of text based on the information presented before and after the piece of text being analyzed. Controversy The specific meaning of cognitive linguistics, the proper address of the name, and the scientific status of the enterprise have been called into question. Criticism includes an overreliance on introspective data, a lack of experimental testing of hypotheses and little integration of findings from other fields of cognitive science. Some researchers go as far as to consider calling the field 'cognitive' at all a misnomer. Members of such frameworks are also said to have used other researchers’ findings to present them as their own work. While this criticism is accepted for most part, it is claimed that some of the research has nonetheless produced useful insights. See also Embodied cognition Psycholinguistics Theory of language Usage-based models of language References