Preprocessing of Aspect-based English Telugu Code Mixed Sentiment Analysis
Extracting sentiments from the English-Telugu code-mixed data can be challenging and is still a relatively new research area. Data obtained from the Twitter API has to be in English-Telugu code-mixed language. That data is free-form text, noisy, lexicon borrowings, code-mixed, phonetic typing and mi...
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University of Tehran
2023-03-01
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| Series: | Journal of Information Technology Management |
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| Online Access: | https://jitm.ut.ac.ir/article_91573_ed3783ba435e864a38d862a99ecfc33e.pdf |
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| author | Arun Kodirekka Ayyagari Srinagesh |
| author_facet | Arun Kodirekka Ayyagari Srinagesh |
| author_sort | Arun Kodirekka |
| collection | DOAJ |
| description | Extracting sentiments from the English-Telugu code-mixed data can be challenging and is still a relatively new research area. Data obtained from the Twitter API has to be in English-Telugu code-mixed language. That data is free-form text, noisy, lexicon borrowings, code-mixed, phonetic typing and misspelling data. The initial step is language identification and sentiment class labels assigned to each tweet in the dataset. The second step is the data normalization task, and the final step is classification, which can be achieved using three different methods: lexicon, machine learning, and deep learning. In the lexicon-based approach, tokenize each tweet with its language tag. If the language tag is in Telugu, transliterate the roman script into native Telugu words. Words are verified with TeluguSentiWordNet, and the Telugu sentiments are extracted, and English SentiWordNets are used to extract sentiments from the English tokens. In this paper, the aspect-based sentiment analysis approach is suggested and used with normalized data. In addition, deep learning and machine learning techniques are applied to extract sentiment ratings, and the results are compared to prior work. |
| format | Article |
| id | doaj-art-cf21fcec12fc47c299be3adadce66f33 |
| institution | DOAJ |
| issn | 2008-5893 2423-5059 |
| language | English |
| publishDate | 2023-03-01 |
| publisher | University of Tehran |
| record_format | Article |
| series | Journal of Information Technology Management |
| spelling | doaj-art-cf21fcec12fc47c299be3adadce66f332025-08-20T03:23:43ZengUniversity of TehranJournal of Information Technology Management2008-58932423-50592023-03-0115Special Issue: Digital Twin Enabled Neural Networks Architecture Management for Sustainable Computing15016310.22059/jitm.2023.9157391573Preprocessing of Aspect-based English Telugu Code Mixed Sentiment AnalysisArun Kodirekka0Ayyagari Srinagesh1Y.S.Rajasekhar Reddy University College of Engineering & Technology Acharya Nagarjuna University, Andhra Pradesh, India.Department of Computer Science and Engineering, RVR & JC College of Engineering, Andhra Pradesh, India.Extracting sentiments from the English-Telugu code-mixed data can be challenging and is still a relatively new research area. Data obtained from the Twitter API has to be in English-Telugu code-mixed language. That data is free-form text, noisy, lexicon borrowings, code-mixed, phonetic typing and misspelling data. The initial step is language identification and sentiment class labels assigned to each tweet in the dataset. The second step is the data normalization task, and the final step is classification, which can be achieved using three different methods: lexicon, machine learning, and deep learning. In the lexicon-based approach, tokenize each tweet with its language tag. If the language tag is in Telugu, transliterate the roman script into native Telugu words. Words are verified with TeluguSentiWordNet, and the Telugu sentiments are extracted, and English SentiWordNets are used to extract sentiments from the English tokens. In this paper, the aspect-based sentiment analysis approach is suggested and used with normalized data. In addition, deep learning and machine learning techniques are applied to extract sentiment ratings, and the results are compared to prior work.https://jitm.ut.ac.ir/article_91573_ed3783ba435e864a38d862a99ecfc33e.pdfenglish-telugu code-mixed datanatural language processingtelugu senti wordnetmachine learningdeep learning |
| spellingShingle | Arun Kodirekka Ayyagari Srinagesh Preprocessing of Aspect-based English Telugu Code Mixed Sentiment Analysis Journal of Information Technology Management english-telugu code-mixed data natural language processing telugu senti wordnet machine learning deep learning |
| title | Preprocessing of Aspect-based English Telugu Code Mixed Sentiment Analysis |
| title_full | Preprocessing of Aspect-based English Telugu Code Mixed Sentiment Analysis |
| title_fullStr | Preprocessing of Aspect-based English Telugu Code Mixed Sentiment Analysis |
| title_full_unstemmed | Preprocessing of Aspect-based English Telugu Code Mixed Sentiment Analysis |
| title_short | Preprocessing of Aspect-based English Telugu Code Mixed Sentiment Analysis |
| title_sort | preprocessing of aspect based english telugu code mixed sentiment analysis |
| topic | english-telugu code-mixed data natural language processing telugu senti wordnet machine learning deep learning |
| url | https://jitm.ut.ac.ir/article_91573_ed3783ba435e864a38d862a99ecfc33e.pdf |
| work_keys_str_mv | AT arunkodirekka preprocessingofaspectbasedenglishtelugucodemixedsentimentanalysis AT ayyagarisrinagesh preprocessingofaspectbasedenglishtelugucodemixedsentimentanalysis |