Sentiment Analysis of Code-Mixed Languages leveraging Resource Rich Languages
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2018
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| _version_ | 1866907824018161664 |
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| author | Choudhary, Nurendra Singh, Rajat Bindlish, Ishita Shrivastava, Manish |
| author_facet | Choudhary, Nurendra Singh, Rajat Bindlish, Ishita Shrivastava, Manish |
| contents | Code-mixed data is an important challenge of natural language processing because its characteristics completely vary from the traditional structures of standard languages.
In this paper, we propose a novel approach called Sentiment Analysis of Code-Mixed Text (SACMT) to classify sentences into their corresponding sentiment - positive, negative or neutral, using contrastive learning. We utilize the shared parameters of siamese networks to map the sentences of code-mixed and standard languages to a common sentiment space. Also, we introduce a basic clustering based preprocessing method to capture variations of code-mixed transliterated words. Our experiments reveal that SACMT outperforms the state-of-the-art approaches in sentiment analysis for code-mixed text by 7.6% in accuracy and 10.1% in F-score. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1804_00806 |
| institution | arXiv |
| publishDate | 2018 |
| record_format | arxiv |
| spellingShingle | Sentiment Analysis of Code-Mixed Languages leveraging Resource Rich Languages Choudhary, Nurendra Singh, Rajat Bindlish, Ishita Shrivastava, Manish Computation and Language Code-mixed data is an important challenge of natural language processing because its characteristics completely vary from the traditional structures of standard languages. In this paper, we propose a novel approach called Sentiment Analysis of Code-Mixed Text (SACMT) to classify sentences into their corresponding sentiment - positive, negative or neutral, using contrastive learning. We utilize the shared parameters of siamese networks to map the sentences of code-mixed and standard languages to a common sentiment space. Also, we introduce a basic clustering based preprocessing method to capture variations of code-mixed transliterated words. Our experiments reveal that SACMT outperforms the state-of-the-art approaches in sentiment analysis for code-mixed text by 7.6% in accuracy and 10.1% in F-score. |
| title | Sentiment Analysis of Code-Mixed Languages leveraging Resource Rich Languages |
| topic | Computation and Language |
| url | https://arxiv.org/abs/1804.00806 |