On the computation of point of view
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Mining the peanut gallery: opinion extraction and semantic classification of product reviews
WWW '03 Proceedings of the 12th international conference on World Wide Web
Sentiment analysis: capturing favorability using natural language processing
Proceedings of the 2nd international conference on Knowledge capture
Predicting the semantic orientation of adjectives
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Thumbs up?: sentiment classification using machine learning techniques
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
The sentimental factor: improving review classification via human-provided information
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Using emoticons to reduce dependency in machine learning techniques for sentiment classification
ACLstudent '05 Proceedings of the ACL Student Research Workshop
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
WebKDD'06 Proceedings of the 8th Knowledge discovery on the web international conference on Advances in web mining and web usage analysis
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We investigate the impact of time on the predictability of sentiment classification research for models created from web logs. We show that sentiment classifiers are time dependent and through a series of methodical experiments quantify the size of the dependence. In particular, we measure the accuracies of 25 different time-specific sentiment classifiers on 24 different testing timeframes. We use the Naive Bayes induction technique and the holdout validation technique using equal-sized but separate training and testing data sets. We conducted over 600 experiments and organize our results by the size of the interval (in months) between the training and testing timeframes. Our findings show a significant decrease in accuracy as this interval grows. Using a paired t-test we show classifiers trained on future data and tested on past data significantly outperform classifiers trained on past data and tested on future data. These findings are for a topic-specific corpus created from political web log posts originating from 160 different web logs. We then define concepts that classify months as examplar, infrequent thread, frequent thread or outlier; this classification reveals knowledge on the topic's evolution and the utility of the month's data for the timeframe.