Difference between revisions of "Jansche and Abney ACL 2002"
PastStudents (talk | contribs) (Created page with '== Citation == Turney, P. D. 2002. Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In Proceedings of the 40th annual meeting of…') |
PastStudents (talk | contribs) |
||
| (One intermediate revision by the same user not shown) | |||
| Line 1: | Line 1: | ||
== Citation == | == Citation == | ||
| − | + | Martin Jansche and Steven P. Abney 2002. Information Extraction from Voicemail Transcripts. In Proceedings of the ACL-02 conference on Empirical methods in natural language processing, Volume 10. | |
== Online version == | == Online version == | ||
| Line 8: | Line 8: | ||
== Summary == | == Summary == | ||
| + | *Problem | ||
| + | the task of extracting relevant information from voicemail transcrpts | ||
This is an early and influential [[Category::paper]] presenting an unsupervised approach to [[AddressesProblem::review classification]]. The basic ideas are: | This is an early and influential [[Category::paper]] presenting an unsupervised approach to [[AddressesProblem::review classification]]. The basic ideas are: | ||
Latest revision as of 18:51, 29 September 2010
Citation
Martin Jansche and Steven P. Abney 2002. Information Extraction from Voicemail Transcripts. In Proceedings of the ACL-02 conference on Empirical methods in natural language processing, Volume 10.
Online version
Summary
- Problem
the task of extracting relevant information from voicemail transcrpts
This is an early and influential paper presenting an unsupervised approach to review classification. The basic ideas are:
- To use patterns of part of speech tags to pick out phrases that are likely to be meaningful and unambiguous with respect to semantic orientation (e.g. ADJ NOUN might pick out "good service" or "delicious desserts").
- To use pointwise mutual information (PMI) to score the similarity of each phrase in a review with the two words "excellent" or "poor", and give an overall score for the polarity to each phrase based on the difference of its PMI with "excellent" to the PMI with "poor". A large corpus was used here (the Web, via queries to a search engine).
- To score the polarity of a review based on the total polarity of the phrases in it.
This approach was fairly successful on a range of review-classification tasks: it achieved accuracy of between 65% and 85% in predicting an author-assigned "recommended" flag for Epinions ratings for eight diverse products, ranging from cars to movies. Many later writers used several key ideas from the paper, including: treating polarity prediction as a document-classification problem; classifying documents based on likely-to-be-informative phrases; and using unsupervised or semi-supervised learning methods.
Related papers
The widely cited Pang et al EMNLP 2002 paper was influenced by this paper - but considers supervised learning techniques. The choice of movie reviews as the domain was suggested by the (relatively) poor performance of Turney's method on movies.
An interesting follow-up paper is Turney and Littman, TOIS 2003 which focuses on evaluation of the technique of using PMI for predicting the semantic orientation of words.
