Difference between revisions of "Comparison Andreevskaia et al ICWSM 2007 and MHurst KNigam RetrievingTopicalSentimentsFromOnlineDocumentColeections"
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== Big Idea == | == Big Idea == | ||
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| + | Both the papers try to perform sentiment or polarity classification on a per sentence basis rather than at a document or message level. This is sometimes beneficial for a fine grained identifying of sentiments pertaining to a specific entity or topic. Both the approaches use a more rule based approach by using sentiment word lists for identifying sentiments. While Hurst_et_al use a restricted sentiment word list pertaining to a single, Andreevskaia use a much bigger [[UsesDataset::HM word list]] further expanded using WordNet. Similarly where Hurst_et_al a grammatical approach to assign polarity to topics, Andreevskaia_et_al restricts to sentiment word counts for assigning sentiment labels only. | ||
== Method == | == Method == | ||
Revision as of 08:05, 6 November 2012
Contents
Papers
- All Blogs are Not Made Equal: Exploring Genre Differences in Sentiment Tagging of Blogs, Alina Andreevskaia, Sabine Bergler, and Monica Urseanu, ICWSM 2007
- Hurst, Matthew F., and Kamal Nigam. "Retrieving topical sentiments from online document collections." Proceedings of SPIE. Vol. 5296. 2004.
Problem
Andreevskaia_2007 perform sentiment classification(binary and ternary) on a per sentence basis. For their analysis they study the differences between "personal diary" and "journalistic" styled web blogs using a manually annotated data. They evaluate their performance on two systems, a sentiment word counts based system and an improved version using valence shifters.
Hurst_Nigam_2004 had previously performed a similar task of identifying polarity on a per sentence basis to discover polar sentences about a topic. Hurst and Nigam had used a linear classifier ([Winnow_Algorithm]) for topic classification and a rule based grammatical model for polarity identification.
Big Idea
Both the papers try to perform sentiment or polarity classification on a per sentence basis rather than at a document or message level. This is sometimes beneficial for a fine grained identifying of sentiments pertaining to a specific entity or topic. Both the approaches use a more rule based approach by using sentiment word lists for identifying sentiments. While Hurst_et_al use a restricted sentiment word list pertaining to a single, Andreevskaia use a much bigger HM word list further expanded using WordNet. Similarly where Hurst_et_al a grammatical approach to assign polarity to topics, Andreevskaia_et_al restricts to sentiment word counts for assigning sentiment labels only.
