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Sentiment Analysis typically refers to using natural language processing, text analysis, and computational linguistics to extract effect and emotion-based information from text data. Our work explores how we can effectively use deep neural networks in transfer learning and joint dual input learning settings to effectively classify sentiments and detect hate speech in Hindi and Bengali data.]]>
Sentiment Analysis typically refers to using natural language processing, text analysis, and computational linguistics to extract effect and emotion-based information from text data. Our work explores how we can effectively use deep neural networks in transfer learning and joint dual input learning settings to effectively classify sentiments and detect hate speech in Hindi and Bengali data.]]> Fri, 22 Apr 2022 12:37:02 GMT /slideshow/bijrnlc20221101pdf/251642666 IJRNLCJOURNAL@slideshare.net(IJRNLCJOURNAL) Hindi/Bengali Sentiment Analysis using Transfer Learning and Joint Dual Input Learning with Self Attention IJRNLCJOURNAL Sentiment Analysis typically refers to using natural language processing, text analysis, and computational linguistics to extract effect and emotion-based information from text data. Our work explores how we can effectively use deep neural networks in transfer learning and joint dual input learning settings to effectively classify sentiments and detect hate speech in Hindi and Bengali data. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/bijrnlc20221101-220422123702-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Sentiment Analysis typically refers to using natural language processing, text analysis, and computational linguistics to extract effect and emotion-based information from text data. Our work explores how we can effectively use deep neural networks in transfer learning and joint dual input learning settings to effectively classify sentiments and detect hate speech in Hindi and Bengali data.
Hindi/Bengali Sentiment Analysis using Transfer Learning and Joint Dual Input Learning with Self Attention from BOHR International Journal of Research on Natural Language Computing
]]> 34 0 https://cdn.slidesharecdn.com/ss_thumbnails/bijrnlc20221101-220422123702-thumbnail.jpg?width=120&height=120&fit=bounds document Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://cdn.slidesharecdn.com/profile-photo-IJRNLCJOURNAL-48x48.jpg?cb=1656152557 BOHR International Journal of Research on Natural Language Computing (BIJRNLC) is an open access peer-reviewed journal that publishes articles which contribute new results in all the areas of Natural Language Computing. Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in this area www.bohrpub.com/journals/BIJRNLC