{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T02:22:04Z","timestamp":1764210124280,"version":"3.41.0"},"reference-count":45,"publisher":"Association for Computing Machinery (ACM)","issue":"8","license":[{"start":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T00:00:00Z","timestamp":1722988800000},"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":[[2024,8,31]]},"abstract":"<jats:p>Recently the field of sentiment analysis has gained a lot of attraction in literature. The idea that a machine can dynamically spot the text\u2019s sentiments is fascinating. In this paper, we propose a method to classify the textual sentiments in Twitter feeds. In particular, we focus on analyzing the tweets of products as either positive or negative. The proposed technique utilizes a deep learning schema to learn and predict the sentiment by extracting features directly from the text. Specifically, we use Convolutional Neural Networks with different convolutional layers. Further, we experiment with LSTMs and try an ensemble of multiple models to get the best results. We employ an n-gram-based word embeddings approach to get the machine-level word representations. Testing of the method is conducted on real-world datasets. We have discovered that the ensemble technique yields the best results after conducting experiments on a huge corpus of more than one million tweets. To be specific, we get an accuracy of 84.95%. The proposed method is also compared with several existing methods. An extensive numerical investigation has revealed the superiority of the proposed work in actual deployment scenarios.<\/jats:p>","DOI":"10.1145\/3615356","type":"journal-article","created":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T11:14:37Z","timestamp":1691752477000},"page":"1-14","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Cognitive Hybrid Deep Learning-based Multi-modal Sentiment Analysis for Online Product Reviews"],"prefix":"10.1145","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7150-4471","authenticated-orcid":false,"given":"Ashwin","family":"Perti","sequence":"first","affiliation":[{"name":"Galgotia University, Greater Noida, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0242-7860","authenticated-orcid":false,"given":"Amit","family":"Sinha","sequence":"additional","affiliation":[{"name":"ABES Engineering College, Ghaziabad, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8026-4246","authenticated-orcid":false,"given":"Ankit","family":"Vidyarthi","sequence":"additional","affiliation":[{"name":"Jaypee Institute of Information Technology, Noida, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,8,7]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.5555\/2021109.2021114"},{"key":"e_1_3_1_3_2","doi-asserted-by":"crossref","DOI":"10.1108\/EL-05-2014-0084","article-title":"Exploring academic libraries\u2019 use of Twitter: A content analysis","author":"Al-Daihani Sultan M.","year":"2015","unstructured":"Sultan M. Al-Daihani and Suha A. AlAwadhi. 2015. Exploring academic libraries\u2019 use of Twitter: A content analysis. The Electronic Library (2015).","journal-title":"The Electronic Library"},{"key":"e_1_3_1_4_2","doi-asserted-by":"crossref","first-page":"2047","DOI":"10.1109\/ICCSP.2017.8286763","volume-title":"2017 International Conference on Communication and Signal Processing (ICCSP\u201917)","author":"Baktha Kiran","year":"2017","unstructured":"Kiran Baktha and B. K. Tripathy. 2017. Investigation of recurrent neural networks in the field of sentiment analysis. In 2017 International Conference on Communication and Signal Processing (ICCSP\u201917). IEEE, 2047\u20132050."},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/S17-2126"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00051"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13278-023-01024-9"},{"key":"e_1_3_1_8_2","article-title":"Learning phrase representations using RNN encoder-decoder for statistical machine translation","author":"Cho Kyunghyun","year":"2014","unstructured":"Kyunghyun Cho, Bart Van Merri\u00ebnboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014).","journal-title":"arXiv preprint arXiv:1406.1078"},{"key":"e_1_3_1_9_2","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1007\/978-3-030-34784-0_7","volume-title":"Neuroergonomics","author":"Choo Sanghyun","year":"2020","unstructured":"Sanghyun Choo and Chang S. Nam. 2020. Deep learning techniques in neuroergonomics. In Neuroergonomics. Springer, 115\u2013138."},{"key":"e_1_3_1_10_2","article-title":"Empirical evaluation of gated recurrent neural networks on sequence modeling","author":"Chung Junyoung","year":"2014","unstructured":"Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014).","journal-title":"arXiv preprint arXiv:1412.3555"},{"key":"e_1_3_1_11_2","article-title":"BB_twtr at SemEval-2017 task 4: Twitter sentiment analysis with CNNs and LSTMs","author":"Cliche Mathieu","year":"2017","unstructured":"Mathieu Cliche. 2017. BB_twtr at SemEval-2017 task 4: Twitter sentiment analysis with CNNs and LSTMs. arXiv preprint arXiv:1704.06125 (2017).","journal-title":"arXiv preprint arXiv:1704.06125"},{"issue":"5","key":"e_1_3_1_12_2","doi-asserted-by":"crossref","first-page":"795","DOI":"10.1080\/1369118X.2013.783608","article-title":"An investigation of influentials and the role of sentiment in political communication on Twitter during election periods","volume":"16","author":"Dang-Xuan Linh","year":"2013","unstructured":"Linh Dang-Xuan, Stefan Stieglitz, Jennifer Wladarsch, and Christoph Neuberger. 2013. An investigation of influentials and the role of sentiment in political communication on Twitter during election periods. Information, Communication & Society 16, 5 (2013), 795\u2013825.","journal-title":"Information, Communication & Society"},{"key":"e_1_3_1_13_2","first-page":"1","volume-title":"2014 International Conference on Computer Communication and Informatics","author":"Das Tushar Kanti","year":"2014","unstructured":"Tushar Kanti Das, D. P. Acharjya, and M. R. Patra. 2014. Opinion mining about a product by analyzing public tweets in Twitter. In 2014 International Conference on Computer Communication and Informatics. IEEE, 1\u20134."},{"key":"e_1_3_1_14_2","first-page":"1124","volume-title":"Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval\u201916)","author":"Deriu Jan Milan","year":"2016","unstructured":"Jan Milan Deriu, Maurice Gonzenbach, Fatih Uzdilli, Aurelien Lucchi, Valeria De Luca, and Martin Jaggi. 2016. Swisscheese at SemEval-2016 task 4: Sentiment classification using an ensemble of convolutional neural networks with distant supervision. In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval\u201916). 1124\u20131128."},{"key":"e_1_3_1_15_2","first-page":"69","volume-title":"Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers","author":"Santos Cicero dos","year":"2014","unstructured":"Cicero dos Santos and Maira Gatti. 2014. Deep convolutional neural networks for sentiment analysis of short texts. In Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers. 69\u201378."},{"key":"e_1_3_1_16_2","first-page":"1","article-title":"Sentiment analysis on Twitter data by using convolutional neural network (CNN) and long short term memory (LSTM)","author":"Gandhi Usha Devi","year":"2021","unstructured":"Usha Devi Gandhi, Priyan Malarvizhi Kumar, Gokulnath Chandra Babu, and Gayathri Karthick. 2021. Sentiment analysis on Twitter data by using convolutional neural network (CNN) and long short term memory (LSTM). Wireless Personal Communications (2021), 1\u201310.","journal-title":"Wireless Personal Communications"},{"key":"e_1_3_1_17_2","article-title":"Sentiment analysis of Twitter data: A survey of techniques","author":"Kharde Vishal","year":"2016","unstructured":"Vishal Kharde and Prof. Sonawane. 2016. Sentiment analysis of Twitter data: A survey of techniques. arXiv preprint arXiv:1601.06971 (2016).","journal-title":"arXiv preprint arXiv:1601.06971"},{"key":"e_1_3_1_18_2","article-title":"Convolutional neural networks for sentence classification. arxXiv, doi","author":"Kim Y.","year":"2014","unstructured":"Y. Kim. 2014. Convolutional neural networks for sentence classification. arxXiv, doi. arXiv preprint arXiv:1408.5882 (2014).","journal-title":"arXiv preprint arXiv:1408.5882"},{"key":"e_1_3_1_19_2","first-page":"538","volume-title":"Proceedings of the International AAAI Conference on Web and Social Media","volume":"5","author":"Kouloumpis Efthymios","year":"2011","unstructured":"Efthymios Kouloumpis, Theresa Wilson, and Johanna Moore. 2011. Twitter sentiment analysis: The good the bad and the omg!. In Proceedings of the International AAAI Conference on Web and Social Media, Vol. 5. 538\u2013541."},{"key":"e_1_3_1_20_2","doi-asserted-by":"crossref","unstructured":"S. Lai L. Xu K. Liu and J. Zhao. [n. d.]. Recurrent convolutional neural networks for text classification Twenty-ninth AAAI Conference on Artificial Intelligence 2015.","DOI":"10.1609\/aaai.v29i1.9513"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-46805-6_19"},{"key":"e_1_3_1_22_2","article-title":"Semi-supervised question retrieval with gated convolutions","author":"Lei Tao","year":"2015","unstructured":"Tao Lei, Hrishikesh Joshi, Regina Barzilay, Tommi Jaakkola, Katerina Tymoshenko, Alessandro Moschitti, and Lluis Marquez. 2015. Semi-supervised question retrieval with gated convolutions. arXiv preprint arXiv:1512.05726 (2015).","journal-title":"arXiv preprint arXiv:1512.05726"},{"key":"e_1_3_1_23_2","first-page":"35","volume-title":"International Conference on Applications of Natural Language to Information Systems","author":"Lev Guy","year":"2015","unstructured":"Guy Lev, Benjamin Klein, and Lior Wolf. 2015. In defense of word embedding for generic text representation. In International Conference on Applications of Natural Language to Information Systems. Springer, 35\u201350."},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2017.06.037"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-02145-9"},{"issue":"2010","key":"e_1_3_1_26_2","first-page":"627","article-title":"Sentiment analysis and subjectivity.","volume":"2","author":"Liu Bing","year":"2010","unstructured":"Bing Liu. 2010. Sentiment analysis and subjectivity. Handbook of Natural Language Processing 2, 2010 (2010), 627\u2013666.","journal-title":"Handbook of Natural Language Processing"},{"key":"e_1_3_1_27_2","first-page":"1","volume-title":"2020 International Conference for Emerging Technology (INCET\u201920)","author":"Mandloi Lokesh","year":"2020","unstructured":"Lokesh Mandloi and Ruchi Patel. 2020. Twitter sentiments analysis using machine learning methods. In 2020 International Conference for Emerging Technology (INCET\u201920). IEEE, 1\u20135."},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asej.2014.04.011"},{"key":"e_1_3_1_29_2","article-title":"Efficient estimation of word representations in vector space","author":"Mikolov Tomas","year":"2013","unstructured":"Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013).","journal-title":"arXiv preprint arXiv:1301.3781"},{"key":"e_1_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbx044"},{"key":"e_1_3_1_31_2","first-page":"767","volume-title":"2015 12th Annual IEEE Consumer Communications and Networking Conference (CCNC\u201915)","author":"Montangero Manuela","year":"2015","unstructured":"Manuela Montangero and Marco Furini. 2015. TRank: Ranking Twitter users according to specific topics. In 2015 12th Annual IEEE Consumer Communications and Networking Conference (CCNC\u201915). IEEE, 767\u2013772."},{"key":"e_1_3_1_32_2","article-title":"Sentiment analysis in Twitter","volume":"5","author":"Nazare Sayali P.","year":"2018","unstructured":"Sayali P. Nazare, Prasad S. Nar, Akshay S. Phate, and D. R. Ingle. 2018. Sentiment analysis in Twitter. Int. Res. J. Eng. Technol. (IRJET) 5 (2018).","journal-title":"Int. Res. J. Eng. Technol. (IRJET)"},{"key":"e_1_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1162"},{"key":"e_1_3_1_34_2","volume-title":"The Twenty-eighth International Flairs Conference","author":"Prusa Joseph D.","year":"2015","unstructured":"Joseph D. Prusa, Taghi M. Khoshgoftaar, and David J. Dittman. 2015. Impact of feature selection techniques for tweet sentiment classification. In The Twenty-eighth International Flairs Conference."},{"issue":"1","key":"e_1_3_1_35_2","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1007\/s13278-023-01030-x","article-title":"Sentiment analysis using Twitter data: A comparative application of lexicon-and machine-learning-based approach","volume":"13","author":"Qi Yuxing","year":"2023","unstructured":"Yuxing Qi and Zahratu Shabrina. 2023. Sentiment analysis using Twitter data: A comparative application of lexicon-and machine-learning-based approach. Social Network Analysis and Mining 13, 1 (2023), 31.","journal-title":"Social Network Analysis and Mining"},{"key":"e_1_3_1_36_2","article-title":"A mixed approach of deep learning method and rule-based method to improve aspect level sentiment analysis","author":"Ray Paramita","year":"2020","unstructured":"Paramita Ray and Amlan Chakrabarti. 2020. A mixed approach of deep learning method and rule-based method to improve aspect level sentiment analysis. Applied Computing and Informatics (2020).","journal-title":"Applied Computing and Informatics"},{"key":"e_1_3_1_37_2","first-page":"202","volume-title":"SemEval@ NAACL-HLT","author":"Rouvier Mickael","year":"2016","unstructured":"Mickael Rouvier and Benoit Favre. 2016. SENSEI-LIF at SemEval-2016 task 4: Polarity embedding fusion for robust sentiment analysis. In SemEval@ NAACL-HLT. 202\u2013208."},{"key":"e_1_3_1_38_2","doi-asserted-by":"publisher","DOI":"10.21236\/ADA164453"},{"issue":"1","key":"e_1_3_1_39_2","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1109\/TCBB.2020.2980831","article-title":"Imbalanced breast cancer classification using transfer learning","volume":"18","author":"Singh Rishav","year":"2020","unstructured":"Rishav Singh, Tanveer Ahmed, Abhinav Kumar, Amit Kumar Singh, Anil Kumar Pandey, and Sanjay Kumar Singh. 2020. Imbalanced breast cancer classification using transfer learning. IEEE\/ACM Transactions on Computational Biology and Bioinformatics 18, 1 (2020), 83\u201393.","journal-title":"IEEE\/ACM Transactions on Computational Biology and Bioinformatics"},{"key":"e_1_3_1_40_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jksuci.2022.02.008"},{"key":"e_1_3_1_41_2","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1007\/978-3-319-66429-3_34","volume-title":"International Conference on Speech and Computer","author":"Spatiotis Nikolaos","year":"2017","unstructured":"Nikolaos Spatiotis, Michael Paraskevas, Isidoros Perikos, and Iosif Mporas. 2017. Examining the impact of feature selection on sentiment analysis for the Greek language. In International Conference on Speech and Computer. Springer, 353\u2013361."},{"key":"e_1_3_1_42_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D15-1167"},{"key":"e_1_3_1_43_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0893-6080(05)80107-8"},{"key":"e_1_3_1_44_2","first-page":"621","volume-title":"Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval\u201917)","author":"Yin Yichun","year":"2017","unstructured":"Yichun Yin, Yangqiu Song, and Ming Zhang. 2017. NNEMBs at SemEval-2017 task 4: Neural Twitter sentiment classification: A simple ensemble method with different embeddings. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval\u201917). 621\u2013625."},{"issue":"1","key":"e_1_3_1_45_2","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1007\/s13278-023-01033-8","article-title":"Nuclear energy: Twitter data mining for social listening analysis","volume":"13","author":"Zarrabeitia-Bilbao Enara","year":"2023","unstructured":"Enara Zarrabeitia-Bilbao, Maite Jaca-Madariaga, Rosa Mar\u00eda Rio-Belver, and Izaskun \u00c1lvarez-Meaza. 2023. Nuclear energy: Twitter data mining for social listening analysis. Social Network Analysis and Mining 13, 1 (2023), 29.","journal-title":"Social Network Analysis and Mining"},{"key":"e_1_3_1_46_2","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1253"}],"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\/3615356","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3615356","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:36:53Z","timestamp":1750178213000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3615356"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,7]]},"references-count":45,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2024,8,31]]}},"alternative-id":["10.1145\/3615356"],"URL":"https:\/\/doi.org\/10.1145\/3615356","relation":{},"ISSN":["2375-4699","2375-4702"],"issn-type":[{"type":"print","value":"2375-4699"},{"type":"electronic","value":"2375-4702"}],"subject":[],"published":{"date-parts":[[2024,8,7]]},"assertion":[{"value":"2023-04-05","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-08-01","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-08-07","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}