{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T10:13:02Z","timestamp":1783937582749,"version":"3.55.0"},"reference-count":43,"publisher":"Association for Computing Machinery (ACM)","issue":"6","license":[{"start":{"date-parts":[[2023,6,17]],"date-time":"2023-06-17T00:00:00Z","timestamp":1686960000000},"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":[[2023,6,30]]},"abstract":"<jats:p>Before the arrival of the web as a corpus, people detected positive and negative news based on the understanding of the textual content from physical newspaper rather than an automatic identification approach from readily available e-newspapers. Thus, the earlier sentiment analysis approach is based on unimodal data, and less effort is paid to the multimodal data. However, the presence of multimodal information helps us to get a clearer understanding of the sentiment. To the best of our knowledge, less work has been introduced on the image\u2013text multimodal sentiment analysis framework of Assamese, a low-resource Indian language mostly spoken in the northeast part of India. We built an Assamese news articles dataset consisting of news text and associated images and one image caption to conduct an experimental study. Focusing on important words and discriminative regions of the images mostly related to sentiment, two individual unimodal such as textual and visual models are proposed. The visual model is developed using an encoder-decoder\u2013based image caption generation system. An image\u2013text multimodal approach is proposed to explore the internal correlation between textual and visual features for joint sentiment classification. Finally, we propose the multimodal sentiment analysis framework, i.e., Textual Visual Multimodal Fusion, by employing a late fusion scheme to merge the three different modalities for the final sentiment prediction. Experimental results conducted on the Assamese dataset built in-house demonstrate that the contextual integration of multimodal features delivers better performance than unimodal features.<\/jats:p>","DOI":"10.1145\/3584861","type":"journal-article","created":{"date-parts":[[2023,2,17]],"date-time":"2023-02-17T11:57:49Z","timestamp":1676635069000},"page":"1-30","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":42,"title":["Image\u2013Text Multimodal Sentiment Analysis Framework of Assamese News Articles Using Late Fusion"],"prefix":"10.1145","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0483-7169","authenticated-orcid":false,"given":"Ringki","family":"Das","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, National Institute of Technology Silchar, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9906-9136","authenticated-orcid":false,"given":"Thoudam Doren","family":"Singh","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, National Institute of Technology Silchar, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,6,17]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"23","volume-title":"Proceedings of the 4th International Conference on Information and Communication Systems (ICICS\u201913)","author":"Al-Kabi Mohammed","year":"2013","unstructured":"Mohammed Al-Kabi, Noor M. Al-Qudah, Izzat Alsmadi, Muhammad Dabour, and Heider Wahsheh. 2013. Arabic\/English sentiment analysis: An empirical study. In Proceedings of the 4th International Conference on Information and Communication Systems (ICICS\u201913). 23\u201325."},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/2502081.2502282"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2017.01.011"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-014-2337-z"},{"key":"e_1_3_2_6_2","first-page":"1557","volume-title":"Proceedings of the IEEE International Conference on Image Processing (ICIP\u201917)","author":"Chen Xingyue","year":"2017","unstructured":"Xingyue Chen, Yunhong Wang, and Qingjie Liu. 2017. Visual and textual sentiment analysis using deep fusion convolutional neural networks. In Proceedings of the IEEE International Conference on Image Processing (ICIP\u201917). IEEE, 1557\u20131561."},{"key":"e_1_3_2_7_2","first-page":"169","volume-title":"Proceedings of the International Conference on Computer Processing of Oriental Languages","author":"Das Amitava","year":"2010","unstructured":"Amitava Das and Sivaji Bandyopadhyay. 2010. Opinion-polarity identification in bengali. In Proceedings of the International Conference on Computer Processing of Oriental Languages. 169\u2013182."},{"key":"e_1_3_2_8_2","first-page":"231","volume-title":"Proceedings of the 18th International Conference on Natural Language Processing (ICON\u201921)","author":"Das Ringki","year":"2021","unstructured":"Ringki Das and Thoudam Doren Singh. 2021. Image caption generation framework for assamese news using attention mechanism. In Proceedings of the 18th International Conference on Natural Language Processing (ICON\u201921). 231\u2013239."},{"key":"e_1_3_2_9_2","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1007\/978-981-33-4084-8_2","volume-title":"Proceedings of the International Conference on Computing and Communication Systems (I3CS\u201920)","volume":"170","author":"Das Ringki","year":"2021","unstructured":"Ringki Das and Thoudam Doren Singh. 2021. A step towards sentiment analysis of assamese news articles using lexical features. In Proceedings of the International Conference on Computing and Communication Systems (I3CS\u201920), Vol. 170. Springer, 15."},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-022-12042-8"},{"key":"e_1_3_2_11_2","first-page":"117575","article-title":"A multi-stage multimodal framework for sentiment analysis of Assamese in low resource setting","author":"Das Ringki","year":"2022","unstructured":"Ringki Das and Thoudam Doren Singh. 2022. A multi-stage multimodal framework for sentiment analysis of Assamese in low resource setting. Expert Syst. Appl. (2022), 117575.","journal-title":"Expert Syst. Appl."},{"key":"e_1_3_2_12_2","article-title":"Social media sentiment analysis: Lexicon versus machine learning","author":"Dhaoui Chedia","year":"2017","unstructured":"Chedia Dhaoui, Cynthia M. Webster, and Lay Peng Tan. 2017. Social media sentiment analysis: Lexicon versus machine learning. J. Cons. Market. (2017).","journal-title":"J. Cons. Market."},{"key":"e_1_3_2_13_2","first-page":"755","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence (AAAI\u201904)","volume":"4","author":"Hu Minqing","year":"2004","unstructured":"Minqing Hu and Bing Liu. 2004. Mining opinion features in customer reviews. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI\u201904), Vol. 4. 755\u2013760."},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.01.019"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.5958\/2249-3220.2014.00002.0"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.3115\/1220355.1220555"},{"key":"e_1_3_2_17_2","first-page":"123","volume-title":"Proceedings of the 12th Workshop on Asian Language Resources (ALR12\u201916)","author":"Le Tuan Anh","year":"2016","unstructured":"Tuan Anh Le, David Moeljadi, Yasuhide Miura, and Tomoko Ohkuma. 2016. Sentiment analysis for low resource languages: A study on informal Indonesian tweets. In Proceedings of the 12th Workshop on Asian Language Resources (ALR12\u201916). 123\u2013131."},{"key":"e_1_3_2_18_2","first-page":"1","article-title":"Low resource language specific pre-processing and features for sentiment analysis task","author":"Meetei Loitongbam Sanayai","year":"2021","unstructured":"Loitongbam Sanayai Meetei, Thoudam Doren Singh, Samir Kumar Borgohain, and Sivaji Bandyopadhyay. 2021. Low resource language specific pre-processing and features for sentiment analysis task. Lang. Resourc. Eval. (2021), 1\u201323.","journal-title":"Lang. Resourc. Eval."},{"issue":"1","key":"e_1_3_2_19_2","first-page":"1","article-title":"Sentiment analysis for a resource poor language\u2013Roman Urdu","volume":"19","author":"Mehmood Khawar","year":"2019","unstructured":"Khawar Mehmood, Daryl Essam, Kamran Shafi, and Muhammad Kamran Malik. 2019. Sentiment analysis for a resource poor language\u2013Roman Urdu. ACM Trans. Asian Low-Resour. Lang. Inf. Proc. 19, 1 (2019), 1\u201315.","journal-title":"ACM Trans. Asian Low-Resour. Lang. Inf. Proc."},{"key":"e_1_3_2_20_2","first-page":"1","volume-title":"Proceedings of the 4th International Conference on Computing, Communications and Networking Technologies (ICCCNT\u201913)","author":"Neethu M. S.","year":"2013","unstructured":"M. S. Neethu and R. Rajasree. 2013. Sentiment analysis in twitter using machine learning techniques. In Proceedings of the 4th International Conference on Computing, Communications and Networking Technologies (ICCCNT\u201913). IEEE, 1\u20135."},{"key":"e_1_3_2_21_2","first-page":"1","article-title":"Exploiting objective text description of images for visual sentiment analysis","author":"Ortis Alessandro","year":"2020","unstructured":"Alessandro Ortis, Giovanni Maria Farinella, Giovanni Torrisi, and Sebastiano Battiato. 2020. Exploiting objective text description of images for visual sentiment analysis. Multimedia Tools Appl. (2020), 1\u201324.","journal-title":"Multimedia Tools Appl."},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.3115\/1218955.1218990"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.3115\/1219840.1219855"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.3115\/1118693.1118704"},{"key":"e_1_3_2_25_2","unstructured":"Chen Qian Edoardo Ragusa Iti Chaturvedi Erik Cambria and Rodolfo Zunino. Text-image sentiment analysis."},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-018-9670-y"},{"key":"e_1_3_2_27_2","first-page":"33","volume-title":"Proceedings of the ACL-IJCNLP Conference Short Papers","author":"Saharia Navanath","year":"2009","unstructured":"Navanath Saharia, Dhrubajyoti Das, Utpal Sharma, and Jugal Kalita. 2009. Part of speech tagger for Assamese text. In Proceedings of the ACL-IJCNLP Conference Short Papers. 33\u201336."},{"key":"e_1_3_2_28_2","first-page":"31","volume-title":"Proceedings of the IEEE Calcutta Conference (CALCON\u201917)","author":"Sarkar Kamal","year":"2017","unstructured":"Kamal Sarkar and Mandira Bhowmick. 2017. Sentiment polarity detection in bengali tweets using multinomial Na\u00efve Bayes and support vector machines. In Proceedings of the IEEE Calcutta Conference (CALCON\u201917). IEEE, 31\u201336."},{"key":"e_1_3_2_29_2","article-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan Karen","year":"2014","unstructured":"Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556. Retrieved from https:\/\/arxiv.org\/abs\/1409.1556.","journal-title":"arXiv:1409.1556"},{"key":"e_1_3_2_30_2","first-page":"240","volume-title":"Proceedings of the 18th International Conference on Natural Language Processing (ICON\u201921)","author":"Singh Alok","year":"2021","unstructured":"Alok Singh, Loitongbam Sanayai Meetei, Salam Michael Singh, Thoudam Doren Singh, and Sivaji Bandyopadhyay. 2021. An efficient keyframes selection based framework for video captioning. In Proceedings of the 18th International Conference on Natural Language Processing (ICON\u201921). 240\u2013250."},{"key":"e_1_3_2_31_2","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1007\/978-981-33-4084-8_5","volume-title":"Proceedings of the International Conference on Computing and Communication Systems (I3CS\u201920)","volume":"170","author":"Singh Thoudam Doren","year":"2021","unstructured":"Thoudam Doren Singh, Telem Joyson Singh, Mirinso Shadang, and Surmila Thokchom. 2021. Review comments of manipuri online video: Good, bad or ugly. In Proceedings of the International Conference on Computing and Communication Systems (I3CS\u201920), Vol. 170. Springer, 45."},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.05.104"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298935"},{"key":"e_1_3_2_34_2","first-page":"3484","volume-title":"Proceedings of the Internationa Joint Conference on Artificial Intelligence (IJCAI\u201916)","author":"Wang Jingwen","year":"2016","unstructured":"Jingwen Wang, Jianlong Fu, Yong Xu, and Tao Mei. 2016. Beyond object recognition: Visual sentiment analysis with deep coupled adjective and noun neural networks. In Proceedings of the Internationa Joint Conference on Artificial Intelligence (IJCAI\u201916). 3484\u20133490."},{"key":"e_1_3_2_35_2","first-page":"1","article-title":"A survey on sentiment analysis methods, applications, and challenges","author":"Wankhade Mayur","year":"2022","unstructured":"Mayur Wankhade, Annavarapu Chandra Sekhara Rao, and Chaitanya Kulkarni. 2022. A survey on sentiment analysis methods, applications, and challenges. Artif. Intell. Rev. (2022), 1\u201350.","journal-title":"Artif. Intell. Rev."},{"key":"e_1_3_2_36_2","article-title":"Visual sentiment prediction with deep convolutional neural networks","author":"Xu C.","unstructured":"C. Xu, S. Cetintas, K. C. Lee, and L. J. Li. [n.d.]. Visual sentiment prediction with deep convolutional neural networks. arXiv:1411.5731. Retrieved from https:\/\/arixv.org\/abs\/1411.5731.","journal-title":"arXiv:1411.5731"},{"key":"e_1_3_2_37_2","article-title":"Adaptive deep metric learning for affective image retrieval and classification","author":"Yao Xingxu","year":"2020","unstructured":"Xingxu Yao, Dongyu She, Haiwei Zhang, Jufeng Yang, Ming-Ming Cheng, and Liang Wang. 2020. Adaptive deep metric learning for affective image retrieval and classification. IEEE Trans. Multimedia (2020).","journal-title":"IEEE Trans. Multimedia"},{"key":"e_1_3_2_38_2","first-page":"1008","volume-title":"Proceedings of the 24th ACM International Conference on Multimedia","author":"You Quanzeng","year":"2016","unstructured":"Quanzeng You, Liangliang Cao, Hailin Jin, and Jiebo Luo. 2016. Robust visual-textual sentiment analysis: When attention meets tree-structured recursive neural networks. In Proceedings of the 24th ACM International Conference on Multimedia. 1008\u20131017."},{"key":"e_1_3_2_39_2","volume-title":"Proceedings of the 31st AAAI Conference on Artificial Intelligence","author":"You Quanzeng","year":"2017","unstructured":"Quanzeng You, Hailin Jin, and Jiebo Luo. 2017. Visual sentiment analysis by attending on local image regions. In Proceedings of the 31st AAAI Conference on Artificial Intelligence."},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1145\/2733373.2806284"},{"key":"e_1_3_2_41_2","first-page":"1","volume-title":"Proceedings of the 2nd International Workshop on Issues of Sentiment Discovery and Opinion Mining","author":"Yuan Jianbo","year":"2013","unstructured":"Jianbo Yuan, Sean McDonough, Quanzeng You, and Jiebo Luo. 2013. Sentribute: Image sentiment analysis from a mid-level perspective. In Proceedings of the 2nd International Workshop on Issues of Sentiment Discovery and Opinion Mining. 1\u20138."},{"key":"e_1_3_2_42_2","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1007\/978-3-319-18032-8_5","volume-title":"Pacific-Asia Conference on Knowledge Discovery and Data Mining","author":"Zhang Yaowen","year":"2015","unstructured":"Yaowen Zhang, Lin Shang, and Xiuyi Jia. 2015. Sentiment analysis on microblogging by integrating text and image features. In Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, 52\u201363."},{"key":"e_1_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2019.102097"},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.5555\/1613715.1613789"}],"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\/3584861","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3584861","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:08:45Z","timestamp":1750183725000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3584861"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,17]]},"references-count":43,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2023,6,30]]}},"alternative-id":["10.1145\/3584861"],"URL":"https:\/\/doi.org\/10.1145\/3584861","relation":{},"ISSN":["2375-4699","2375-4702"],"issn-type":[{"value":"2375-4699","type":"print"},{"value":"2375-4702","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,17]]},"assertion":[{"value":"2021-09-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-02-03","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-06-17","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}