SentiQ: A Probabilistic Logic Approach to Enhance Sentiment Analysis Tool Quality
This addresses inconsistency issues in sentiment analysis tools for organizations relying on web and social media opinions, but it appears incremental as it builds on existing methods.
The paper tackled the problem of inconsistent polarities in sentiment analysis tools, which harms business decisions, by proposing SentiQ, an unsupervised Markov logic Network-based approach that injects semantic rules to detect and solve inconsistencies, improving overall accuracy as shown in preliminary experimental results.
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.