Scalable Detection of Sentiment-Based Contradictions

Knowledge Diversity on the Web 2011.

图书情报档案学;计算机科学

Mikalai Tsytsarau ; Themis Palpanas ; Kerstin Denecke

Others  :  http://ceur-ws.org/Vol-762/paper3.pdf
PID  :  41947

来源: CEUR

【 摘 要 】

The analysis of user opinions expressed on the Web is becoming in- creasingly relevant to a variety of applications. It allows us to track the evolution of opinions or discussions in the blogosphere, or per- form product surveys. The aggregation of sentiments and analysis of contradictions is another important application, which becomes effective since we are able to capture the diversity in sentiments on different topics with more precision and on a large scale. Though, there is still a need for a scalable way of sentiment aggregation with respect to the time dimension, which preserves enough information to capture contradictions. In this paper, we are focusing on the problem of finding sentiment- based contradictions at a large scale. First, we define two types of contradictions, depending on the distributions of opposite sen- timents over time. Second, we introduce a novel measure of con- tradiction based on the mean value and the variance of sentiments among different texts. Third, we propose a scalable method for identifying both types of contradictions at different time scales. We evaluate the performance of our method using synthetic and real- world datasets, as well as a user-study. The experiments demon- strate the effectiveness of the proposed method in capturing contra-