Measuring praise and criticism: Inference of semantic orientation from association
ACM Transactions on Information Systems (TOIS)
Predicting the semantic orientation of adjectives
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Determining the semantic orientation of terms through gloss classification
Proceedings of the 14th ACM international conference on Information and knowledge management
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Determining the sentiment of opinions
COLING '04 Proceedings of the 20th international conference on 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
Fully automatic lexicon expansion for domain-oriented sentiment analysis
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
A knowledge-rich approach to feature-based opinion extraction from product reviews
SMUC '10 Proceedings of the 2nd international workshop on Search and mining user-generated contents
Opinion word expansion and target extraction through double propagation
Computational Linguistics
Propagation of trust and distrust for the detection of trolls in a social network
Computer Networks: The International Journal of Computer and Telecommunications Networking
'Long autonomy or long delay?' The importance of domain in opinion mining
Expert Systems with Applications: An International Journal
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In most tasks related to opinion mining and sentiment analysis, it is necessary to compute the semantic orientation (i.e., positive or negative evaluative implications) of certain opinion expressions. Recent works suggest that semantic orientation depends on application domains. Moreover, we think that semantic orientation depends on the specific targets (features) that an opinion is applied to. In this paper, we introduce a technique to build domain-specific, feature-level opinion lexicons in a semi-supervised manner: we first induce a lexicon starting from a small set of annotated documents; then, we expand it automatically from a larger set of unannotated documents, using a new graph-based ranking algorithm. Our method was evaluated in three different domains (headphones, hotels and cars), using a corpus of product reviews which opinions were annotated at the feature level. We conclude that our method produces feature-level opinion lexicons with better accuracy and recall that domain-independent opinion lexicons using only a few annotated documents.