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  Published Paper Details:

  Paper Title

CODE-MIXED SENTIMENT ANALYSIS FOR MULTI-LINGUAL[HI-EN] SOCIAL MEDIA TEXT

  Authors

  Sushrut Kuchik,  Rutuja Pujare,  Anupama Rai,  Romax Rajeev,  Dr. Sharvari Govilkar

  Keywords

Code Mixed Text, Social Media Text, Natural Language Processing, Sentiment Analysis, Lexicon Approach, Supervised and Unsupervised Learning.

  Abstract


India has a great diversity of languages. People use English and colloquial languages to put forward their ideas and views on social media platforms like Twitter and Facebook. This gives rise to code mixed text. Sometimes, emoticons are also used along with text to express the emotions and to indicate sarcasm. Analyzing this complex multilingual code-mixed text becomes an enormous challenge for researchers who work in the domain of Text Analytics. The research work regarding sentiment analysis of code-mixed text, sarcasm detection and negation handling has been done separately and there has been no cumulative model to do the same. The proposed research work will use a hybrid approach, consisting of Lexicon based methodology and Unsupervised Deep Learning algorithms to extract sentiments from multilingual code-mixed text from social media especially for the language pair: English-Hindi.

  IJCRT's Publication Details

  Unique Identification Number - IJCRT2102405

  Paper ID - 203508

  Page Number(s) - 3367-3372

  Pubished in - Volume 9 | Issue 2 | February 2021

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  Sushrut Kuchik,  Rutuja Pujare,  Anupama Rai,  Romax Rajeev,  Dr. Sharvari Govilkar,   "CODE-MIXED SENTIMENT ANALYSIS FOR MULTI-LINGUAL[HI-EN] SOCIAL MEDIA TEXT", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.9, Issue 2, pp.3367-3372, February 2021, Available at :http://www.ijcrt.org/papers/IJCRT2102405.pdf

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ISSN: 2320-2882
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ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
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