期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2022
卷号：13
期号：2
DOI：10.14569/IJACSA.2022.0130254
语种：English
出版社：Science and Information Society (SAI)
摘要：A comprehensive review of sentiment analysis for code-mixed and switched text corpus of Indian social media using machine learning (ML) approaches, based on recent research studies has been presented in this paper. Code-mixing and switching are linguistic behavior shown by the bilingual/multilingual population, primarily in spoken but also in written communication, especially on social media. Code-mixing involves combining lower linguistic units like words and phrases of a language into the sentences of other language (the base language) and code-switching involves switching to another language, for the length of one sentence or more. In code-mixing and switching, a bilingual person takes one or more words or phrases from one language and introduces them into another language while communicating in that language in spoken or written mode. People nowadays express their views and opinions on several issues on social media. In multilingual countries, people express their views using English as well as their native languages. Several reasons can be attributed to code-mixing. Lack of knowledge in one language on a particular subject, being empathetic, interjection and clarification are some to name. Sentiment analysis of monolingual social media content has been carried out for the last two decades. However, during recent years, Natural Language Processing (NLP) research focus has also shifted towards the exploration of code-mixed data, thereby, making code mixed sentiment analysis an evolving field of research. Systems have been developed using ML techniques to predict the polarity of code-mixed text corpus and to fine tune the existing models to improve their performance.
关键词：Sentiment analysis; code mixing; corpus; deep learning; machine learning; NLP; social media text
