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Recommendation system based on Tamil-English code-mixed text analysis

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Abstract

The cinema industry has always been popular since its inception many years ago and is a preferred pastime of many people. It can be observed that even though online movie applications are popular in multilingual society, English is the preferred language. Naturally, people of other languages mix their native language with English during communications resulting in an abundance of multilingual data called code-mixed data, available in today's world. This research focuses on the movie recommendation system whose primary objective is to make a recommender system through Natural Language Processing (NLP) Tools for Tamil-English Code-mixed (Tanglish) Comments. Our recommendation system will be a filtering scheme whose primary objective is to predict a viewer's rating or preference towards a movie or web series.

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Decryption, Encryption, Language prediction, NLTK, Sentiment analysis, Tanglish, Translation

Citation

S. Vijayakumar, G. Murugaiah, J. Sivanesan, K. Archchana, W. Tissera and S. Vidhanaarachchi, "Recommendation system based on Tamil-English code-mixed text analysis," 2022 IEEE 13th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), Vancouver, BC, Canada, 2022, pp. 0378-0383, doi: 10.1109/IEMCON56893.2022.9946465.

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