Mining the peanut gallery: opinion extraction and semantic classification of product reviews
Citations
7,452 citations
Cites background or methods from "Mining the peanut gallery: opinion ..."
...More extensive comparisons of the performance of standard machine learning techniques with other types of features or feature selection schemes have been engaged in in later work [5, 69, 103, 204, 217]; see Section 4....
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...[69] on the one hand and Gamon [103], Matsumoto et al....
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...[69] find that in some settings, bigrams and trigrams yield better product-review polarity classification....
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...[69] apply a classifier trained on a pre-assembled dataset of reviews of a certain type to product reviews of a different type....
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...[69] that was published in the proceedings of the 2003 WWW conference; the publication venue may explain the popularity of the term within communities strongly associated with Web search or information retrieval....
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7,330 citations
Cites background from "Mining the peanut gallery: opinion ..."
...(2) The work in [ 9 ] does not mine product features from reviews on which the reviewers have expressed their opinions....
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...Our work is closely related to Dave, Lawrence and Pennock’s work in [ 9 ] on semantic classification of reviews....
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4,515 citations
3,459 citations
3,433 citations
Cites background from "Mining the peanut gallery: opinion ..."
...(Turney, 2002; Dave et al., 2003; Pang and Lee, 2004; Beineke et al., 2004))....
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...Much work on sentiment analysis classifies documents by their overall sentiment, for example determining whether a review is positive or negative (e.g., (Turney, 2002; Dave et al., 2003; Pang and Lee, 2004; Beineke et al., 2004))....
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References
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6,626 citations
"Mining the peanut gallery: opinion ..." refers methods in this paper
...[15] attempted to classify movie reviews posted to Usenet, using accompanying numerical ratings as ground truth....
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1,823 citations
1,442 citations
"Mining the peanut gallery: opinion ..." refers methods in this paper
...Hatzivassiloglou and McKeown [5] used textual conjunctions such as “fair and legitimate” or “simplistic but well-received” to separate similarly- and oppositely-connoted words....
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...[5] Vasileios Hatzivassiloglou and Kathleen R. McKeown....
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...Hatzivassiloglou and McKeown [5] used textual conjunctions such as fair and legitimate or simplistic but well-received to separate similarly-and oppositely-connoted words....
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