Paper Info
Title
Do We Really Need Lexical Information? Towards a Top-down Approach to Sentiment Analysis of Product Reviews.
Abstract
Most of the current approaches to sentiment analysis of product reviews are dependent on lexical sentiment information and proceed in a bottom-up way, adding new layers of features to lexical data. In this paper, we maintain that a typical product review is not a bag of sentiments, but a narrative with an underlying structure and reoccurring patterns, which allows us to predict its sentiments knowing only its general polarity and discourse cues that occur in it. We hypothesize that knowing only the review’s score and its discourse patterns would allow us to accurately predict the sentiments of its individual sentences. The experiments we conducted prove this hypothesis and show a substantial improvement over the lexical baseline.
Year
Venue
Field
2015
HLT-NAACL
Information retrieval,Sentiment analysis,Computer science,Top-down and bottom-up design,Narrative,Natural language processing,Artificial intelligence,Product reviews
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
14
2
Name
Order
Citations
PageRank
Yulia Otmakhova102.03
Hyopil Shin25310.09