Identifying Opinion and Fact Subcategories from the Social Web

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.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.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.conference.locationSanibel Island, Florida, USA