Sentiment Classification of Movie and Product Reviews Using Contextual Valence Shifters. Kennedy, A. & Inkpen, D. Technical Report ![Sentiment Classification of Movie and Product Reviews Using Contextual Valence Shifters [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
Paper Website abstract bibtex We present a method for determining the sentiment expressed by a customer review. The semantic orientation of a review can be positive , negative, or neutral. Our method counts positive and negative terms, but also takes into account contextual valence shifters, such as negations and intensifiers. Tests are done taking both negations and in-tensifiers into account, and also using only negations without intensi-fiers. Negations are used to reverse the semantic polarity of a particular term, while intensifiers are used to change the degree to which a term is positive or negative. We use the General Inquirer in order to identify positive and negative terms, as well as negations, overstatements, and understatements. We also test the impact of adding extra positive and negative terms from other sources, including a dictionary of synonym differences and a very large web corpus. To compute the corpus-based values of the semantic orientation of individual terms we use their association scores with a small group of positive and negative terms. We show that including contextual valence shifters improves the accuracy of the classification.
@techreport{
title = {Sentiment Classification of Movie and Product Reviews Using Contextual Valence Shifters},
type = {techreport},
websites = {https://web.cs.dal.ca/~kennedy/sentiment.pdf},
id = {e4811462-b75e-34a8-ad83-e149a229033f},
created = {2019-05-16T08:30:00.244Z},
accessed = {2019-05-16},
file_attached = {true},
profile_id = {1bff199d-3fb6-39f8-9a95-8103f3a5d433},
group_id = {af6234f1-5a5f-3882-bb01-83ebf2615cfd},
last_modified = {2019-05-16T08:30:01.074Z},
read = {false},
starred = {false},
authored = {false},
confirmed = {false},
hidden = {false},
private_publication = {false},
abstract = {We present a method for determining the sentiment expressed by a customer review. The semantic orientation of a review can be positive , negative, or neutral. Our method counts positive and negative terms, but also takes into account contextual valence shifters, such as negations and intensifiers. Tests are done taking both negations and in-tensifiers into account, and also using only negations without intensi-fiers. Negations are used to reverse the semantic polarity of a particular term, while intensifiers are used to change the degree to which a term is positive or negative. We use the General Inquirer in order to identify positive and negative terms, as well as negations, overstatements, and understatements. We also test the impact of adding extra positive and negative terms from other sources, including a dictionary of synonym differences and a very large web corpus. To compute the corpus-based values of the semantic orientation of individual terms we use their association scores with a small group of positive and negative terms. We show that including contextual valence shifters improves the accuracy of the classification.},
bibtype = {techreport},
author = {Kennedy, Alistair and Inkpen, Diana}
}
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