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- 54 creator jian-tao-sun.
- 54 creator qiang-yang.
- 54 creator sinno-pan.
- 54 creator xiaochuan-ni.
- 54 creator zheng-chen.
- 54 type InProceedings.
- 54 label "Cross-Domain Sentiment Classification via Spectral Feature Alignment".
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- 54 abstract "User sentiment data are widely spread on the Web. However, when users publish sentiment data (e.g., reviews, blogs), they may not explicitly indicate their sentiment polarity (e.g., positive or negative). Sentiment classification, which is used to automatically predict sentiment category, is critical in many applications particularly when user text is large in scale. Although traditional classification algorithms can be used to train sentiment classifiers from manually labeled text data, the labeling work can be time-consuming and expensive. Meanwhile, users often use some different words when they express sentiment in different domains. In this work, we develop a general solution for sentiment classification when we do not have any labels in a target domain. We solve this problem by leveraging the knowledge from a different domain with labels, which is what we call a source domain. In this cross-domain sentiment classification setting, if we directly apply a classification model trained in the source domain to the target domain, performance will be very low due to the differences between these domains. To bridge the gap between the domains, we propose a spectral feature alignment (SFA) algorithm to align the domain-specific words from different domains into unified clusters, with the help of domain-independent words as a bridge. In this way, the clusters can be used to reduce the gap between domain specific words of the two domains, which can be used to train sentiment classifiers in the target domain accurately. Compared to previous approaches to cross-domain sentiment classification, SFA is both general and optimized, since it discovers a robust representation for cross-domain data by fully exploiting the relationship between the domain-specific and domainindependent words. We perform extensive experiments on two real world datasets, and demonstrate that SFA outperforms previous approaches for cross-domain sentiment classification.".
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- 54 keyword "Opinion mining".
- 54 keyword "sentiment analysis".
- 54 title "Cross-Domain Sentiment Classification via Spectral Feature Alignment".
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- 542 creator jose-san-pedro.
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- 542 creator stefan-siersdorfer.
- 542 creator wolfgang-nejdl.
- 542 type InProceedings.
- 542 label "How useful are your comments? - Analyzing and Predicting YouTube Comments and Comment Ratings".
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- 542 abstract "An analysis of the leading social video sharing platform YouTube reveals a high amount of community feedback through comments for published videos as well as through meta ratings for these comments. In this paper, we present an in-depth study of commenting and comment rating behavior on a sample of more than 6 million comments from about 67,000 YouTube videos for which we analyzed various dependencies between comments, views, comment ratings and topic categories. Furthermore, we also study the influence of sentiment expressed in comments on the ratings for these comments using the SentiWordNet thesaurus, a lexical WordNet-based resource containing sentiment annotations. Finally, to predict community acceptance for comments not yet rated, we build different classifiers for the prediction of ratings for these comments. The results of our large-scale evaluations are promising and indicate that community feedback on already rated comments can help to filter new unrated comments or suggest particularly useful but still unrated comments.".
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- 542 keyword "Social data analysis".
- 542 keyword "analytics".
- 542 title "How useful are your comments? - Analyzing and Predicting YouTube Comments and Comment Ratings".
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