Much of the past work in structured sentiment extraction has been evaluated in ways that summarize the output of a sentiment extraction technique for a particular application. In order to get a true picture of how accurate a sentiment extraction system is, however, it is important to see how well it performs at finding individual mentions of opinions in a corpus.
Past work also focuses heavily on mining opinion/product-feature pairs from product review corpora, which has lead to sentiment extraction systems assuming that the documents they operate on are review-like — that each document concerns only one topic, that there are lots of reviews on a particular product, and that the product features of interest are frequently recurring phrases.
Based on existing linguistics research, this dissertation introduces the concept of an appraisal expression, the basic grammatical unit by which an opinion is expressed about a target. The IIT sentiment corpus, intended to present an alternative to both of these assumptions that have pervaded structured sentiment analysis research, consists of blog posts annotated with appraisal expressions to enable the evaluation of how well sentiment analysis systems find individual appraisal expressions.
This dissertation introduces FLAG, an automated system for extracting appraisal expressions. FLAG operates based on a three step process: (1) identifying attitude groups using a lexicon-based shallow parser, (2) identifying potential structures for the rest of the appraisal expression by identifying patterns in a sentence's dependency parse tree, (3) selecting the best appraisal expression for each attitude group using a discriminative reranker. FLAG achieves an overall accuracy of 0.261 F1 at correctly identifying appraisal expressions, which is good considering the difficulty of the task.
|School:||Illinois Institute of Technology|
|School Location:||United States -- Illinois|
|Source:||DAI-B 73/07(E), Dissertation Abstracts International|
|Subjects:||Linguistics, Artificial intelligence, Computer science|
|Keywords:||Information extraction, Local grammars, Natural language processing, Sentiment analysis, Text processing|
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