SentiQ: A Probabilistic Logic Approach to Enhance Sentiment Analysis Tool Quality

Autor:	Kouadri, Wissam Maamar, Benbernou, Salima, Ouziri, Mourad, Palpanas, Themis, Amor, Iheb Ben
Rok vydání:	2020
Předmět:	Computer Science - Artificial Intelligence Computer Science - Machine Learning Computer Science - Logic in Computer Science
Druh dokumentu:	Working Paper
Popis:	The opinion expressed in various Web sites and social-media is an essential contributor to the decision making process of several organizations. Existing sentiment analysis tools aim to extract the polarity (i.e., positive, negative, neutral) from these opinionated contents. Despite the advance of the research in the field, sentiment analysis tools give \textit{inconsistent} polarities, which is harmful to business decisions. In this paper, we propose SentiQ, an unsupervised Markov logic Network-based approach that injects the semantic dimension in the tools through rules. It allows to detect and solve inconsistencies and then improves the overall accuracy of the tools. Preliminary experimental results demonstrate the usefulness of SentiQ. Comment: In Proceedings of the 9th KDD Workshop on Issues of Sentiment Discovery and Opinion Mining (WISDOM 20). San Diego, CA, USA, 8 pages
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2008.08919 Zobrazit plný text záznamu View this record from Arxiv