SIGDOC '86 Proceedings of the 5th annual international conference on Systems documentation
An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Development and use of a gold-standard data set for subjectivity classifications
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
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
A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Determining the sentiment of opinions
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Automatic identification of pro and con reasons in online reviews
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Mining opinion features in customer reviews
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Recognizing contextual polarity: An exploration of features for phrase-level sentiment analysis
Computational Linguistics
A hybrid approach to emotional sentence polarity and intensity classification
CoNLL '10 Proceedings of the Fourteenth Conference on Computational Natural Language Learning
Inferring the scope of negation in biomedical documents
CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
Semi-automatic generation of recommendation processes and their GUIs
Proceedings of the 2013 international conference on Intelligent user interfaces
Are user-contributed reviews community property?: exploring the beliefs and practices of reviewers
Proceedings of the 5th Annual ACM Web Science Conference
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The information in customer reviews is of great interest to both companies and consumers. This information is usually presented as non-structured free-text so that automatically extracting and rating user opinions about a product is a challenging task. Moreover, this opinion highly depends on the product features on which the user judgments and impressions are expressed. Following this idea, our goal is to predict the overall rating of a product review based on the user opinion about the different product features that are evaluated in the review. To this end, the system first identifies the features that are relevant to consumers when evaluating a certain type of product, as well as the relative importance or salience of such features. The system then extracts from the review the user opinions about the different product features and quantifies such opinions. The salience of the different product features and the values that quantify the user opinions about them are used to construct a Vector of Feature Intensities which represents the review and will be the input to a machine learning model that classifies the review into different rating categories. Our method is evaluated over 1000 hotel reviews from booking.com. The results compare favorably with those achieved by other systems addressing similar evaluations.