Identifying Opinion and Fact Subcategories from the Social Web

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dc.contributor.authorMullick, Ankan
dc.contributor.authorGhosh D, Surjodoy
dc.contributor.authorMaheswari, Shivam
dc.contributor.authorSahoo, Srotaswini
dc.contributor.authorMaity, Suman Kalyan
dc.contributor.authorC, Soumya
dc.contributor.authorGoyal, Pawan
dc.date.accessioned2023-03-17T22:48:45Z
dc.date.available2023-03-17T22:48:45Z
dc.date.issued2018
dc.description.abstractIn this paper, we investigate the problem of building automatic classifiers to categorize opinions and facts into appropriate subcategories. While working on two English News article datasets and two social media datasets (Twitter hashtag idioms and Youtube comments), we achieve consistent performance with accuracies in the range of 70-85% for opinion and fact sub-categorization. The proposed classifiers can be instrumental in understanding argumentative relations as well as in developing fact-checking systems. It can also be used to detect anomalous behavior such as predominant drunkers or other psychological changes.en
dc.identifier.doi10.1145/3148330.3154518
dc.identifier.urihttps://dl.eusset.eu/handle/20.500.12015/4514
dc.language.isoen
dc.publisherAssociation for Computing Machinery
dc.relation.ispartofProceedings of the 2018 ACM International Conference on Supporting Group Work
dc.subjectopinion-fact diversity
dc.subjectfact classification
dc.subjectopinion classification
dc.titleIdentifying Opinion and Fact Subcategories from the Social Weben
dc.typeText/Conference Paper
gi.citation.startPage145–149
gi.citations.count6
gi.citations.elementNaoki Muramoto, Hiromi Shiraga, Kilho Shin, Hiroaki Ohshima (2019): Fatten Features and Drop Wastes, In: Proceedings of the 21st International Conference on Information Integration and Web-based Applications & Services, doi:10.1145/3366030.3366133
gi.citations.elementAnkan Mullick, Anindya Bhandari, Abhishek Niranjan, Nitesh Sckhar, Shrey Garg, Riya Bubna, Mayank Roy (2018): Drift in Online Social Media, In: 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), doi:10.1109/iemcon.2018.8614746
gi.citations.elementAnkan Mullick, Mukur Gupta (2024): Avenues in IoT with advances in Artificial Intelligence, In: 2024 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), doi:10.1109/percomworkshops59983.2024.10502625
gi.citations.elementAnkan Mullick, Sayan Ghosh, Ritam Dutt, Avijit Ghosh, Abhijnan Chakraborty (2019): Public Sphere 2.0: Targeted Commenting in Online News Media, In: Lecture Notes in Computer Science, doi:10.1007/978-3-030-15719-7_23
gi.citations.elementAnkan Mullick, Pawan Goyal, Niloy Ganguly, Manish Gupta (2018): Harnessing Twitter for Answering Opinion List Queries, In: IEEE Transactions on Computational Social Systems 4(5), doi:10.1109/tcss.2018.2881186
gi.citations.elementAnkan Mullick, Akash Ghosh, G. Sai Chaitanya, Samir Ghui, Tapas Nayak, Seung-Cheol Lee, Satadeep Bhattacharjee, Pawan Goyal (2024): MatSciRE: Leveraging pointer networks to automate entity and relation extraction for material science knowledge-base construction, In: Computational Materials Science, doi:10.1016/j.commatsci.2023.112659
gi.conference.locationSanibel Island, Florida, USA

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