Measuring praise and criticism: Inference of semantic orientation from association
ACM Transactions on Information Systems (TOIS)
Recognizing contextual polarity in phrase-level sentiment analysis
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Semantic text similarity using corpus-based word similarity and string similarity
ACM Transactions on Knowledge Discovery from Data (TKDD)
A computer approach to content analysis: studies using the General Inquirer system
AFIPS '63 (Spring) Proceedings of the May 21-23, 1963, spring joint computer conference
Identifying text polarity using random walks
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Mining slang and urban opinion words and phrases from cQA services: an optimization approach
Proceedings of the fifth ACM international conference on Web search and data mining
Using google n-grams to expand word-emotion association lexicon
CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume 2
| Hi-index | 0.00 |
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.