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
Effects of adjective orientation and gradability on sentence subjectivity
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
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
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
Determining the sentiment of opinions
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Extracting product features and opinions from reviews
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
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
Yahoo! for Amazon: Sentiment Extraction from Small Talk on the Web
Management Science
Test collection selection and gold standard generation for a multiply-annotated opinion corpus
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
Experiments on summary-based opinion classification
CAAGET '10 Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text
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Unlike news stories and product reviews which usually have a strong focus on a single topic, blog posts are often unstructured, and opinions expressed in blog posts do not necessarily correspond to a specific topic. This can lead to unsatisfactory performance of sentiment classification. In this paper we report our pilot study on addressing topic drift in blogs. We examine this phenomenon by manual inspection and extablish a ground truth. Our annotations have shown that topic drift is indeed very common, with all documents sampled showing a considerable degree of drift, averaging over 80%. The topical sentences are extracted from each post to produce an extract data set. We propose to address the topical drift problem by classifying the blog posts using the sentence-level polarities of topical extracts. We propose and evaluate two models for aggregating the sentence polarities by comparing their performance to that of a popular word-based model. Our preliminary results suggest that topical extracts can provide a concise but more accurate representation of the sentiment polarity of the blog posts. More importantly, sentence-level polarities are potentially a more reliable evidence than word distributions with regard to document polarity prediction.