Utilizing tweet content for the detection of sentiment-based interaction communities on Twitter
Community detection is one way of extracting insights from voluminous Twitter data. Through this technique, Twitter users can be grouped into different types of communities such as those who interact a lot, or those who have similar sentiments about certain topics. However, most works do not utilize...
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| Main Authors: | , |
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| Format: | text |
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Animo Repository
2019
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| Online Access: | https://animorepository.dlsu.edu.ph/faculty_research/2838 |
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| Institution: | De La Salle University |
| Summary: | Community detection is one way of extracting insights from voluminous Twitter data. Through this technique, Twitter users can be grouped into different types of communities such as those who interact a lot, or those who have similar sentiments about certain topics. However, most works do not utilize tweet content and simply use directly available information like Twitter follows. Hence, this work explores the incorporation of hashtags and sentiment analysis (also taking into account conversational context) in the input graph for community detection through various schemes. Evaluation was performed by investigating the modularity score, topic similarity/variety, and sentiment homogeneity of the resulting communities. Results suggest that when compared to a baseline graph based on mentions, a scoring approach is more likely to yield a different set of communities compared to the more popular edge-weighting approach. Insights gleaned from the study show the importance of other evaluation methods (depending on the end-goal) aside from usual quantitative metrics of community network structure, and that community detection in conjunction with topic modeling can be a tool for analyzing Twitter discourse. © 2018 IEEE. |
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