{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T13:47:16Z","timestamp":1780408036666,"version":"3.54.1"},"reference-count":35,"publisher":"Association for Computing Machinery (ACM)","issue":"5","license":[{"start":{"date-parts":[[2021,6,30]],"date-time":"2021-06-30T00:00:00Z","timestamp":1625011200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2021,9,30]]},"abstract":"<jats:p>Linguistic resources for commonly used languages such as English and Mandarin Chinese are available in abundance, hence the existing research in these languages. However, there are languages for which linguistic resources are scarcely available. One of these languages is the Hindi language. Hindi, being the fourth-most popular language, still lacks in richly populated linguistic resources, owing to the challenges involved in dealing with the Hindi language. This article first explores the machine learning-based approaches\u2014Na\u00efve Bayes, Support Vector Machine, Decision Tree, and Logistic Regression\u2014to analyze the sentiment contained in Hindi language text derived from Twitter.<\/jats:p>\n          <jats:p>Further, the article presents lexicon-based approaches (Hindi Senti-WordNet, NRC Emotion Lexicon) for sentiment analysis in Hindi while also proposing a Domain-specific Sentiment Dictionary. Finally, an integrated convolutional neural network (CNN)\u2014Recurrent Neural Network and Long Short-term Memory\u2014is proposed to analyze sentiment from Hindi language tweets, a total of 23,767 tweets classified into positive, negative, and neutral. The proposed CNN approach gives an accuracy of 85%.<\/jats:p>","DOI":"10.1145\/3450447","type":"journal-article","created":{"date-parts":[[2021,6,30]],"date-time":"2021-06-30T20:06:29Z","timestamp":1625083589000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":43,"title":["Toward Integrated CNN-based Sentiment Analysis of Tweets for Scarce-resource Language\u2014Hindi"],"prefix":"10.1145","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8109-498X","authenticated-orcid":false,"given":"Vedika","family":"Gupta","sequence":"first","affiliation":[{"name":"Department of Computer Science &amp; Engineering, Bharati Vidyapeeth's College of Engineering, New Delhi, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nikita","family":"Jain","sequence":"additional","affiliation":[{"name":"Department of Computer Science &amp; Engineering, Bharati Vidyapeeth's College of Engineering, New Delhi, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shubham","family":"Shubham","sequence":"additional","affiliation":[{"name":"Department of Computer Science &amp; Engineering, Bharati Vidyapeeth's College of Engineering, New Delhi, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Agam","family":"Madan","sequence":"additional","affiliation":[{"name":"Department of Computer Science &amp; Engineering, Bharati Vidyapeeth's College of Engineering, New Delhi, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ankit","family":"Chaudhary","sequence":"additional","affiliation":[{"name":"Department of Computer Science &amp; Engineering, Bharati Vidyapeeth's College of Engineering, New Delhi, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qin","family":"Xin","sequence":"additional","affiliation":[{"name":"Faculty of Science and Technology, University of the Faroe Islands, Faroe Islands"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,6,30]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.5555\/1273073.1273173"},{"key":"e_1_2_1_2_1","volume-title":"Proceedings of the IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS\u201915)","author":"Jha V.","unstructured":"V. Jha , N. Manjunath , P. D. Shenoy , K. R. Venugopal , and L. M. Patnaik . 2015. Homs: Hindi opinion mining system . In Proceedings of the IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS\u201915) . IEEE, 366\u2013371. V. Jha, N. Manjunath, P. D. Shenoy, K. R. Venugopal, and L. M. Patnaik. 2015. Homs: Hindi opinion mining system. In Proceedings of the IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS\u201915). IEEE, 366\u2013371."},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.3233\/JIFS-179021"},{"key":"e_1_2_1_4_1","doi-asserted-by":"crossref","unstructured":"R. Piryani V. Gupta V. K. Singh and U. Ghose. 2017. A linguistic rule-based approach for aspect-level sentiment analysis of movie reviews. In Advances in Computer and Computational Sciences. Springer Singapore 201\u2013109.  R. Piryani V. Gupta V. K. Singh and U. Ghose. 2017. A linguistic rule-based approach for aspect-level sentiment analysis of movie reviews. In Advances in Computer and Computational Sciences. Springer Singapore 201\u2013109.","DOI":"10.1007\/978-981-10-3770-2_19"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.3233\/JIFS-169272"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2021.110708"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.5555\/1868771.1868774"},{"key":"e_1_2_1_8_1","volume-title":"Proceedings of the 8th International Conference on Natural Language Processing (ICON\u201910)","author":"Joshi A.","unstructured":"A. Joshi , A. R. Balamurali , and P. Bhattacharyya . 2010. A fall-back strategy for sentiment analysis in Hindi: a case study . Proceedings of the 8th International Conference on Natural Language Processing (ICON\u201910) . A. Joshi, A. R. Balamurali, and P. Bhattacharyya. 2010. A fall-back strategy for sentiment analysis in Hindi: a case study. Proceedings of the 8th International Conference on Natural Language Processing (ICON\u201910)."},{"key":"e_1_2_1_9_1","unstructured":"A. Karthikeyan. 2010 May. Hindi English Wordnet Linkage. Dual-degree thesis CSE Dept. IIT Bombay.  A. Karthikeyan. 2010 May. Hindi English Wordnet Linkage. Dual-degree thesis CSE Dept. IIT Bombay."},{"key":"e_1_2_1_10_1","volume-title":"Proceedings of the Workshop on Sentiment Analysis where AI meets Psychology (SAAIP\u201911)","author":"Bakliwal A.","unstructured":"A. Bakliwal , P. Arora , A. Patil , and V. Varma . 2011. Towards enhanced opinion classification using NLP techniques . In Proceedings of the Workshop on Sentiment Analysis where AI meets Psychology (SAAIP\u201911) . 101\u2013107. A. Bakliwal, P. Arora, A. Patil, and V. Varma. 2011. Towards enhanced opinion classification using NLP techniques. In Proceedings of the Workshop on Sentiment Analysis where AI meets Psychology (SAAIP\u201911). 101\u2013107."},{"key":"e_1_2_1_11_1","volume-title":"Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC\u201912)","author":"Bakliwal A.","unstructured":"A. Bakliwal , P. Arora , and V. Varma . 2012. Hindi subjective lexicon: A lexical resource for Hindi polarity classification . In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC\u201912) . 1189\u20131196. A. Bakliwal, P. Arora, and V. Varma. 2012. Hindi subjective lexicon: A lexical resource for Hindi polarity classification. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC\u201912). 1189\u20131196."},{"key":"e_1_2_1_12_1","first-page":"25","article-title":"Hindi subjective lexicon generation using WordNet graph traversal","volume":"3","author":"Arora P.","year":"2012","unstructured":"P. Arora , A. Bakliwal , and V. Varma . 2012 . Hindi subjective lexicon generation using WordNet graph traversal . International J. Comput. Linguist. Appl. 3 , 1 (2012), 25 \u2013 39 . P. Arora, A. Bakliwal, and V. Varma. 2012. Hindi subjective lexicon generation using WordNet graph traversal. International J. Comput. Linguist. Appl. 3, 1 (2012), 25\u201339.","journal-title":"International J. Comput. Linguist. Appl."},{"key":"e_1_2_1_13_1","volume-title":"Proceedings of the International Conference on Computational Linguistics (COLING\u201912). 1847","author":"Mukherjee S.","year":"1864","unstructured":"S. Mukherjee and P. Bhattacharyya . 2012. Sentiment analysis in Twitter with lightweight discourse analysis . In Proceedings of the International Conference on Computational Linguistics (COLING\u201912). 1847 \u2013 1864 . S. Mukherjee and P. Bhattacharyya. 2012. Sentiment analysis in Twitter with lightweight discourse analysis. In Proceedings of the International Conference on Computational Linguistics (COLING\u201912). 1847\u20131864."},{"key":"e_1_2_1_14_1","volume-title":"Proceedings of the 11th Workshop on Asian Language Resources. 45\u201350","author":"Mittal N.","unstructured":"N. Mittal , B. Agarwal , G. Chouhan , N. Bania , and P. Pareek . 2013. Sentiment analysis of Hindi reviews based on negation and discourse relation . In Proceedings of the 11th Workshop on Asian Language Resources. 45\u201350 . N. Mittal, B. Agarwal, G. Chouhan, N. Bania, and P. Pareek. 2013. Sentiment analysis of Hindi reviews based on negation and discourse relation. In Proceedings of the 11th Workshop on Asian Language Resources. 45\u201350."},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.5121\/ijcsa.2014.4405"},{"key":"e_1_2_1_16_1","volume-title":"Proceedings of the 3rd International Conference on Recent Advances in Information Technology (RAIT\u201916)","author":"Ravi K.","unstructured":"K. Ravi and V. Ravi . 2016. Sentiment classification of Hinglish text . In Proceedings of the 3rd International Conference on Recent Advances in Information Technology (RAIT\u201916) . IEEE, 641\u2013645. K. Ravi and V. Ravi. 2016. Sentiment classification of Hinglish text. In Proceedings of the 3rd International Conference on Recent Advances in Information Technology (RAIT\u201916). IEEE, 641\u2013645."},{"key":"e_1_2_1_17_1","volume-title":"Proceedings of the International Conference on Intelligent Data Communication Technologies and Internet of Things. Springer, Cham, 1006\u20131012","author":"Ansari M. Z.","unstructured":"M. Z. Ansari , T. Ahmad , and M. A. Ali . 2018. Cross script Hindi-English NER corpus from Wikipedia . In Proceedings of the International Conference on Intelligent Data Communication Technologies and Internet of Things. Springer, Cham, 1006\u20131012 . M. Z. Ansari, T. Ahmad, and M. A. Ali. 2018. Cross script Hindi-English NER corpus from Wikipedia. In Proceedings of the International Conference on Intelligent Data Communication Technologies and Internet of Things. Springer, Cham, 1006\u20131012."},{"key":"e_1_2_1_18_1","first-page":"83","article-title":"Generating aspect-based extractive opinion summary: Drawing inferences from social media texts","volume":"22","author":"Piryani R.","year":"2018","unstructured":"R. Piryani , V. Gupta , and V. K. Singh . 2018 . Generating aspect-based extractive opinion summary: Drawing inferences from social media texts . Comput. Sistem. 22 , 1 (2018), 83 \u2013 91 . R. Piryani, V. Gupta, and V. K. Singh. 2018. Generating aspect-based extractive opinion summary: Drawing inferences from social media texts. Comput. Sistem. 22, 1 (2018), 83\u201391.","journal-title":"Comput. Sistem."},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cogsys.2018.12.015"},{"key":"e_1_2_1_20_1","first-page":"1309","article-title":"Emotion recognition of audio\/speech data using deep learning approaches","volume":"41","author":"Gupta V.","year":"2020","unstructured":"V. Gupta , S. Juyal , G. P. Singh , C. Killa , and N. Gupta . 2020 . Emotion recognition of audio\/speech data using deep learning approaches . J. Info. Optimiz. Sci. 41 , 6 (2020), 1309 \u2013 1317 . V. Gupta, S. Juyal, G. P. Singh, C. Killa, and N. Gupta. 2020. Emotion recognition of audio\/speech data using deep learning approaches. J. Info. Optimiz. Sci. 41, 6 (2020), 1309\u20131317.","journal-title":"J. Info. Optimiz. Sci."},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.3233\/IDT-190079"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3359988"},{"key":"e_1_2_1_23_1","volume-title":"Proceedings of the International Conference on Computational Linguistics and Intelligent Text Processing.","author":"Tummalapalli M.","unstructured":"M. Tummalapalli , M. Chinnakotla , and R. Mamidi . 2018, March. Towards better sentence classification for morphologically rich languages . In Proceedings of the International Conference on Computational Linguistics and Intelligent Text Processing. M. Tummalapalli, M. Chinnakotla, and R. Mamidi. 2018, March. Towards better sentence classification for morphologically rich languages. In Proceedings of the International Conference on Computational Linguistics and Intelligent Text Processing."},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2020.03.306"},{"key":"e_1_2_1_25_1","volume-title":"Proceedings of the 26th International Conference on Computational Linguistics (COLING\u201916)","author":"Akhtar M. S.","unstructured":"M. S. Akhtar , A. Kumar , A. Ekbal , and P. Bhattacharyya . 2016, December. A hybrid deep learning architecture for sentiment analysis . In Proceedings of the 26th International Conference on Computational Linguistics (COLING\u201916) . 482\u2013493. M. S. Akhtar, A. Kumar, A. Ekbal, and P. Bhattacharyya. 2016, December. A hybrid deep learning architecture for sentiment analysis. In Proceedings of the 26th International Conference on Computational Linguistics (COLING\u201916). 482\u2013493."},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/0306-4573(81)90028-5"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1097\/00001648-199609000-00030"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.rinp.2021.103813"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.5555\/1873781.1873815"},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.5555\/2491748.2491787"},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-29038-1_29"},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.5555\/2107636.2107650"},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-8640.2012.00460.x"},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106198"},{"key":"e_1_2_1_35_1","first-page":"313","article-title":"Analyzing sentiment in Indian languages micro text using a recurrent neural network","volume":"7","author":"Seshadri S.","year":"2016","unstructured":"S. Seshadri , A. K. Madasamy , S. K. Padannayil , and M. A. Kumar . 2016 . Analyzing sentiment in Indian languages micro text using a recurrent neural network . Inst. Integr. Omics Appl. Biotechnol. J. 7 (2016), 313 \u2013 318 . S. Seshadri, A. K. Madasamy, S. K. Padannayil, and M. A. Kumar. 2016. Analyzing sentiment in Indian languages micro text using a recurrent neural network. Inst. Integr. Omics Appl. Biotechnol. J. 7 (2016), 313\u2013318.","journal-title":"Inst. Integr. Omics Appl. Biotechnol. J."}],"container-title":["ACM Transactions on Asian and Low-Resource Language Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3450447","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3450447","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:18:45Z","timestamp":1750191525000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3450447"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,30]]},"references-count":35,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2021,9,30]]}},"alternative-id":["10.1145\/3450447"],"URL":"https:\/\/doi.org\/10.1145\/3450447","relation":{},"ISSN":["2375-4699","2375-4702"],"issn-type":[{"value":"2375-4699","type":"print"},{"value":"2375-4702","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,30]]},"assertion":[{"value":"2020-08-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-02-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-06-30","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}