论文标题
Sentiq：一种增强情感分析工具质量的概率逻辑方法
SentiQ: A Probabilistic Logic Approach to Enhance Sentiment Analysis Tool Quality
论文作者
论文摘要
在各种网站和社交媒体上表达的意见是多个组织决策过程的重要贡献。现有的情感分析工具旨在从这些自以为是的内容中提取极性（即积极，负，中性）。尽管该领域的研究有所进步，但情感分析工具给出了\ textit {不一致}极性，这对业务决策有害。在本文中，我们提出了Sentiq，这是一种基于无监督的Markov逻辑网络方法，该方法通过规则注入工具中的语义维度。它允许检测和解决不一致之处，然后提高工具的整体准确性。初步实验结果证明了Sentiq的有用性。
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.
