Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Extracting product features and opinions from reviews
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
A holistic lexicon-based approach to opinion mining
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
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Feature-based opinion extraction is a task related to opinion mining and information extraction which consists of automatically extracting feature-level representations of opinions from subjective texts. In the last years, some researchers have proposed domain-independent solutions to this task. Most of them identify the feature being reviewed by a set of words from the text. Rather than that, we propose a domain-adaptable opinion extraction system based on feature taxonomies (a semantic representation of the opinable parts and attributes of an object) which extracts feature-level opinions and maps them into the taxonomy. The opinions thus obtained can be easily aggregated for summarization and visualization. In order to increase precision and recall of the extraction system, we define a set of domain-specific resources which capture valuable knowledge about how people express opinions on each feature from the taxonomy for a given domain. These resources are automatically induced from a set of annotated documents. The modular design of our architecture allows building either domain-specific or domain-independent opinion extraction systems. According to some experimental results, using the domain-specific resources leads to far better precision and recall, at the expense of some manual effort.