CRANK: A Hybrid Model for User and Content Sentiment Classification Using Social Context and Community Detection
Intelligent Systems Group, Universidad Politécnica de Madrid, 28040 Madrid, Spain
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Appl. Sci. 2020, 10(5), 1662; https://doi.org/10.3390/app10051662
Received: 4 February 2020
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Revised: 21 February 2020
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Accepted: 24 February 2020
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Published: 1 March 2020
(This article belongs to the Special Issue Sentiment Analysis for Social Media Ⅱ)
Recent works have shown that sentiment analysis on social media can be improved by fusing text with social context information. Social context is information such as relationships between users and interactions of users with content. Although existing works have already exploited the networked structure of social context by using graphical models or techniques such as label propagation, more advanced techniques from social network analysis remain unexplored. Our hypothesis is that these techniques can help reveal underlying features that could help with the analysis. In this work, we present a sentiment classification model (CRANK) that leverages community partitions to improve both user and content classification. We evaluated this model on existing datasets and compared it to other approaches.
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MDPI and ACS Style
Sánchez-Rada, J.F.; Iglesias, C.A. CRANK: A Hybrid Model for User and Content Sentiment Classification Using Social Context and Community Detection. Appl. Sci. 2020, 10, 1662. https://doi.org/10.3390/app10051662
AMA Style
Sánchez-Rada JF, Iglesias CA. CRANK: A Hybrid Model for User and Content Sentiment Classification Using Social Context and Community Detection. Applied Sciences. 2020; 10(5):1662. https://doi.org/10.3390/app10051662
Chicago/Turabian StyleSánchez-Rada, J. F., and Carlos A. Iglesias. 2020. "CRANK: A Hybrid Model for User and Content Sentiment Classification Using Social Context and Community Detection" Applied Sciences 10, no. 5: 1662. https://doi.org/10.3390/app10051662
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