Abstract
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.
Highlights
The state-of-the-art in the field of sentiment analysis has improved considerably in recent years, partly due to the advent of social media
A recent work [1] analyzed the use of social context in the sentiment analysis literature, and it showed that context-based approaches performed better than traditional analysis without social context
The sentiment classification task can be divided into two sub-tasks: user-level classification, which only focuses on predicting user labels (Lu ), and content-level classification, which focuses on content labels (Lc )
Summary
The state-of-the-art in the field of sentiment analysis has improved considerably in recent years, partly due to the advent of social media. Understanding a piece of content often requires following a conversation (i.e., a thread of replies) or the style and stance of the author of the content To solve these limitations, new approaches are starting to combine text with additional information from the social network, such as links between users and previous posts by each user. The classification model is based on an earlier model by Pozzi et al [2], which our model improves in two significant ways: (1) it can be used for content-level classification, and (2) in addition to using the raw relations from the social network, it can use community detection to find weak relations between users. The rest of the paper is structured as follows: Section 2 covers related works and concepts; Section 3 describes the classification model; Section 4 is dedicated to a description of the datasets used for evaluation and how they have been enriched with social context; Section 5 presents the evaluation of the model; Section 6 closes with our conclusions and future lines of work
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
