Abstract : As social media has become an important part and package in everyone’s daily life. Human emotions are persistently being expressed in real time on various social networking platforms. The availability of a gigantic amount of opinion-rich data from various social networking platforms has fueled interest in opinion mining and sentiment analysis. Applying mining techniques and sentiment analysis over big data (unstructured) is considered a great challenge in the sentiment analysis research area. There are mainly following methodologies for performing sentiment analysis that is machine learning-based approach, the lexicon-based approach and hybrid approach. In this paper, we chose to limit our study to just the lexicon-based approach of sentiment anal Lexicon-based based approach relies on the lexicons for categorizing input A lexicon is a set of words, idioms, phrases, etc. having a semantic meaning. In this paper, earlier research done in lexicon-based sentiment analysis has been studied; also, are view of some state-of-the-art lexicon-based solutions has been presented for polarity classification of Sentiment Analysis. This paper is mainly oriented toward the various lexicons used for polarity classification.
Keywords : Opinion mining, sentiment analysis, Big data, machine learning, lexicon and hybrid approach.
Cite : Sharma, P., Pal, R., & Shukla, A. P. (2023). Lexicon–Based Approaches for Sentiment Classification (1st ed., pp. 47-57). Noble Science Press. https://doi.org/10.52458/9789388996570.2023.eb.ch10
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