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

dc.contributor.authorVijayakumar, S
dc.contributor.authorMurugaiah, G
dc.contributor.authorSivanesan, J
dc.contributor.authorArchchana, K
dc.contributor.authorTissera, W
dc.contributor.authorVidhanaarachchi, S
dc.date.accessioned2023-01-24T05:35:21Z
dc.date.available2023-01-24T05:35:21Z
dc.date.issued2022-10-15
dc.description.abstractThe 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.en_US
dc.identifier.citationS. 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.en_US
dc.identifier.doi10.1109/IEMCON56893.2022.9946465en_US
dc.identifier.issn978-166546316-4
dc.identifier.urihttps://rda.sliit.lk/handle/123456789/3158
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.relation.ispartofseries2022 IEEE 13th Annual Information Technology, Electronics and Mobile Communication Conference, IEMCON 2022;Pages 378 - 383
dc.subjectDecryptionen_US
dc.subjectEncryptionen_US
dc.subjectLanguage predictionen_US
dc.subjectNLTKen_US
dc.subjectSentiment analysisen_US
dc.subjectTanglishen_US
dc.subjectTranslationen_US
dc.titleRecommendation system based on Tamil-English code-mixed text analysisen_US
dc.typeArticleen_US
dspace.entity.typePublication

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