Difference between revisions of "Opinion mining"
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Revision as of 15:25, 1 December 2010
Summary
Opinion mining is a problem in the field of information extraction that which aims to automatically extract opinion expressions from product reviews. Also one of the goal of the opinion mining techniques is to determine the opinion direction of a review.
Various named entity type hierarchies have been proposed in the literature, such as BBN's categories (used in Question Answering) and Sekine's Extended Named Entity Hierarchy
Common Approaches
Some common models for named entity recognition include the following:
- Lexicons
- Checks if a token is part of a predefined set
- Classifying pre-segmented candidates
- Manually select candidates, then use YFCL on a piece of text to deterimine what type of entity it is
- Sliding Window
- Try all reasonable token windows (different lengths and positions), train a Naive Bayes classifier or YFCL, then extract text if Pr(class=+|prefix, contents, suffix) > some threshold
- Token Tagging / Sequential
- Classify tokens sequentially, with models like Hidden Markov Models, Maximum Entropy Markov Models, or Conditional Random Fields.
Example Systems
References / Links
- BBN Named Entity Types - [1]
- Satoshi Sekine's Extended Named Entity Hierarchy - [2]
- Wikipedia page on Named entity recognition - [3]
