A Systematic Literature Review of Sentiment Analysis Techniques - Kaur, J; Sehra, SS; Sehra, SK (International Journal of Computer Sciences and Engineering, 2025)
Development of Web 2.0 has resulted in enormous increase in the vast source of opinionated user generated data. Sentiment Analysis includes extracting, grasping, arranging and presenting the feelings or suppositions communicated in the information gathered from the clients. This paper exhibits an efficient writing survey of different strategies of sentiment analysis. A model for sentiment analysis of twitter data using existing techniques is constructed for comparative analysis of various approaches. Dataset is pre-processed for noise removal and unigrams as well as bigrams are used for feature extraction with term frequency as weighting criteria. Maximum accuracy is achieved by using a combination of SVM and Naïve Bayes at 78.60% employing unigrams and 81.40% employing bigrams as features.