Sentimental Analysis on Cognitive Data Using R

Internet is now vested with new form of societal interactive activities like social media, online portals, feeds, reviews, ratings, posts, critics etc., where people are able to post their expression-of-interest as tweets. Sentiment Analysis (SA) is used for better understanding of such linguistics tweets, extracting features, determine subjectivity and polarity of text located in these tweets. SA inherits text mining approach to process, investigate, and analyze idiosyncratic evidences from text. Now a days, SA was screamed as one of a predictor tool for improvement in knowledge management, revenue generation and decision-making in many businesses firms. The purpose of this work is to leverage a constructive tactic for SA towards dispensation of cognitive information, and seed pragmatic alley to researchers in cognitive science community. This study uses machine learning packages of R language over cognitive data to gain knowledge, discover sentiment polarity and better prediction over the data. To carry out a semantic study over cognitive data we thrived text from numerous numbers of social networking sites. This data was articulated in form of unstructured sentences, words and phrases in a document. Suitable linguistic features are captured to engender dissimilar sentiment polarity and analyze expression-of-interest of user. One of the most prevalent text classification method, Naive bayes is applied over the text corpus to pinpoint the sentiment and assign its polarity. The connotation in this approaches are evaluated in terms of statistical measures precision, recall, f-measure, and accuracy, thereby these substantial outcomes help to arcade user behavior and predict future trends using SA.