The Sentence Level Sentiment Analysis of Cyber Trolling Tweets Using Machine Learning Technique

Abstract

People are utilizing the networking site Twitter not only for social interaction but also to express their opinions, thoughts, news, and personal information in the form of text, videos, and pictures. Many of these tweets are cyber-trolling-related, psychologically devastating, and should be on the notice of the police. However, analyzing these tweets manually is quite difficult. Therefore, an intelligent mechanism is required to examine and polarize those cyber trolling-related tweets. Thus, in this paper, Valence Aware Dictionary Sentence (VADsentence) Miner has been proposed to perform Sentence Level Sentiment Analysis (SLSA) using machine learning (ML) techniques. For this purpose, tweets are pre-processed and sentences are extracted on the base of adjectives, adverbs and noun phrases. For SLSA, a combination of lexicon and rule-based approach named Valence Aware Dictionary and Sentiment Reasoner (VADER) is used to compute the sentiment polarity of tweets based on sentences. The proposed VADsentence Mines experimented with the feature selection technique TF-IDF and machine learning algorithms. Results of VADsentence Miner are compared with TextBlob in that VADsentence Miner outperformed 90% in accuracy, 82% in precision, 74% in recall, and 78% in F1-score on the Random Forest machine learning classifier and Term Frequency Inverse Document Frequency (TF-IDF). Textblob however, could archive 67% of accuracy on Random Forest and Term Frequency Inverse document frequency (TF-IDF).