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Identifying Opinion and Fact Subcategories from the Social Web

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Association for Computing Machinery


In 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.


Mullick, Ankan; Ghosh D, Surjodoy; Maheswari, Shivam; Sahoo, Srotaswini; Maity, Suman Kalyan; C, Soumya; Goyal, Pawan (2018): Identifying Opinion and Fact Subcategories from the Social Web. Proceedings of the 2018 ACM International Conference on Supporting Group Work. DOI: 10.1145/3148330.3154518. Association for Computing Machinery. pp. 145–149. Sanibel Island, Florida, USA