Mining sentiment terminology through time

Abstract

The correspondence between sentiment terminology and the active language used for expressing opinions is a crucial prerequisite for effective sentiment analysis. Mining sentiment terminology includes the detection of new opinion words as well as inferring their polarities. In this paper, we first propose a novel approach based on the interchangeability characteristic of words to detect new opinion words through time. We then show that the current non-time-based polarity inference approaches may assign opposite polarity to the same opinion word at different times. To tackle this issue, we consider the polarity scores computed at different times as polarity evidences (with the possibility of flawed evidences) and combine them to compute a globally correct polarity score for each opinion word. The experiments show that our approach is effective both in terms of the quality of the discovered new opinion words as well as its ability in inferring their polarities through time. Furthermore, we show the application of mining sentiment terminology through time in the sentiment classification (SC) task. The experiments show that mining more recent new opinion words leads to greater improvement in the performance of SC. To the best of our knowledge, this is the first work that investigates "time" as an important factor in mining sentiment terminology. © 2012 ACM.