Sentiment Analysis of English Tweets Using Bigram Collocation

Abstract

Community and portal websites like Twitter, Facebook, Tumbler, Instagram, and LinkedIn etc. have significant impact in our day-to-day life. One of the most popular micro-blogging platforms is twitter that can provide a huge amount of data which in future can be used for various applications of opinion mining like predictions, reviews, elections, marketing etc. The users use this platform to share their views, express sentiments on various events of their daily life. Previously, many researchers have worked with twitter sentiment analysis and compared various classifiers and got the accuracy below 82%. In this work for classifying tweets into sentiments, we have used various classifiers such as Naïve Bayes, Support Vector Machine and Maximum Entropy that segregate the positive and negative tweets. Using Bigram Collocation with classifiers, we’ve acquired 88.42% accuracy. KEYWORDS: Twitter; Sentiment Classification; Machine Learning; NLTK; Python; Naïve Bayes; Support Vector Machine (SVM); Maximum Entropy

Identification of Affective States Based on Automatic Analysis of Texts of Comments in Social Networks

“…Wolk et al [5] have demonstrated the efficiency of sentiment-based comment text classification and depression detection based on call-gram analysis and the deep model BERT of the language representation, while Moyeen et al [11] note that bigram-based vectorization considerably increases the quality of classification, in contrast to the use of trigrams, and so the study of the authors was aimed at identifying bigrams and trigrams that describe the subject area and considering approaches to the vectorization of comment texts based on the analysis of bigrams and their characteristics to solve the problems of classifying and clustering comments containing a description of affective disorders.…”