Finding the WRITE Stuff: Automatic Identification of Discourse Structure in Student Essays
IEEE Intelligent Systems
Lexical cohesion computed by thesaural relations as an indicator of the structure of text
Computational Linguistics
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
Extracting knowledge from evaluative text
Proceedings of the 3rd international conference on Knowledge capture
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
Extracting product features and opinions from reviews
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Topic sentiment mixture: modeling facets and opinions in weblogs
Proceedings of the 16th international conference on World Wide Web
Genre-based paragraph classification for sentiment analysis
SIGDIAL '09 Proceedings of the SIGDIAL 2009 Conference: The 10th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Creating subjective and objective sentence classifiers from unannotated texts
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
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A qualitative examination of review texts suggests that there are consistent patterns to how topic and polarity are expressed in discourse. These patterns are visible in the text and paragraph structure, topic depth, and polarity flow. In this paper, we employ sentence-level sentiment classifiers and a hand-built tree ontology to investigate whether these patterns can be quantitatively identified in a large corpus of video game reviews. Our results indicate that the beginning and the end of major textual units (e.g. paragraphs) stand out in the flow of texts, showing a concentration of reliable opinion and key topic aspects, and that there are other important regularities in the expression of opinion and topic relevant to their ordering and the discourse markers with which they appear.