Measuring praise and criticism: Inference of semantic orientation from association
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Topic sentiment mixture: modeling facets and opinions in weblogs
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A holistic lexicon-based approach to opinion mining
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Let's Tango --- Finding the Right Couple for Feature-Opinion Association in Sentiment Analysis
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Generating focused topic-specific sentiment lexicons
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Aspect and sentiment unification model for online review analysis
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LivePulse: tapping social media for sentiments in real-time
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An information theoretic approach to sentiment polarity classification
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Collocation polarity disambiguation using web-based pseudo contexts
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Towards jointly extracting aspects and aspect-specific sentiment knowledge
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Constructing chinese sentiment lexicon using bilingual information
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Generating contextualized sentiment lexica based on latent topics and user ratings
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Unsupervised sentiment analysis with emotional signals
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Automatic construction of domain and aspect specific sentiment lexicons for customer review mining
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Storing and analysing voice of the market data in the corporate data warehouse
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Polarity analysis of micro reviews in foursquare
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Automatic Domain-Specific Sentiment Lexicon Generation with Label Propagation
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The explosion of Web opinion data has made essential the need for automatic tools to analyze and understand people's sentiments toward different topics. In most sentiment analysis applications, the sentiment lexicon plays a central role. However, it is well known that there is no universally optimal sentiment lexicon since the polarity of words is sensitive to the topic domain. Even worse, in the same domain the same word may indicate different polarities with respect to different aspects. For example, in a laptop review, "large" is negative for the battery aspect while being positive for the screen aspect. In this paper, we focus on the problem of learning a sentiment lexicon that is not only domain specific but also dependent on the aspect in context given an unlabeled opinionated text collection. We propose a novel optimization framework that provides a unified and principled way to combine different sources of information for learning such a context-dependent sentiment lexicon. Experiments on two data sets (hotel reviews and customer feedback surveys on printers) show that our approach can not only identify new sentiment words specific to the given domain but also determine the different polarities of a word depending on the aspect in context. In further quantitative evaluation, our method is proved to be effective in constructing a high quality lexicon by comparing with a human annotated gold standard. In addition, using the learned context-dependent sentiment lexicon improved the accuracy in an aspect-level sentiment classification task.