Abstract
The sentiment captured in opinionated text provides interesting and valuable information for social media services. However, due to the complexity and diversity of linguistic representations, it is challenging to build a framework that accurately extracts such sentiment. We propose a semi-supervised framework for generating a domain-specific sentiment lexicon and inferring sentiments at the segment level. Our framework can greatly reduce the human effort for building a domainspecific sentiment lexicon with high quality. Specifically, in our evaluation, working with just 20 manually labeled reviews, it generates a domain-specific sentiment lexicon that yields weighted average FMeasure gains of 3%. Our sentiment classification model achieves approximately 1% greater accuracy than a state-of-the-art approach based on elementary discourse units.
Highlights
Extracting sentiments from usergenerated opinionated text is important in building social media services
Each group of bars represents the accuracy of two sentiment classification models trained using Linguistic Inquiry and Word Count (LIWC) (CRFsGeneral) and the generated domain-specific lexicon (CRFs-Domain), respectively
ReNew starts with LIWC and a labeled dataset and generates ten lexicons and sentiment classification models by iteratively learning 4,017 unlabeled reviews without any human guidance
Summary
Extracting sentiments from usergenerated opinionated text is important in building social media services. To capture more complex linguistic phenomena, leading approaches (Nakagawa et al, 2010; Jo and Oh, 2011; Kim et al, 2013) apply more advanced models but assume one document or sentence holds one sentiment. (2) ReNew leverages the relationships between consecutive segments to infer their sentiments and automatically generates a domain-specific sentiment lexicon in a semi-supervised fashion. (3) To capture the contextual sentiment of words, ReNew uses dependency relation pairs as the basic elements in the generated sentiment lexicon.
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