An efficient hybrid filter and evolutionary wrapper approach for sentiment analysis of various topics on Twitter

Abstract Sentiment Analysis is currently considered as one of the most attractive research topics in Natural Language Processing (NLP) field. The main objective of sentiment analysis is to identify the opinions and emotions of the users through written contents. While there are different studies that have approached this field using various techniques, it is still considered a challenging topic with many difficulties that are yet to be solved, such as having modern accents, slang words, spelling and grammatical mistakes, and other issues that cannot be overcome with traditional methods and sentiment lexicons. In this work, we propose a hybrid machine learning approach to enhance sentiment analysis; as we build a classification model based on three classes, which are positive, neutral, and negative emotions, using Support Vector Machines (SVM) classifier, while combining two feature selection techniques using the ReliefF and Multi-Verse Optimizer (MVO) algorithms. We also extract more than 6900 tweets from Twitter social network to test our work. Our hybrid method is compared against other classifiers and methods in terms of accuracy. Results show that our proposed method outperforms other techniques and classifiers, by obtaining better results in most of the datasets while reducing the number of features by up to 96.85% from the original feature set. We also categorize the extracted features into Objective, Subjective and Emoticon words to analyze them during the first and the final feature selection processes and find any existing relations. Very similar results are obtained by both feature selection techniques; due to a number of factors that are explained in this paper.