Title

Utilizing tweet content for the detection of sentiment-based interaction communities on Twitter

College

College of Computer Studies

Department/Unit

Software Technology

Document Type

Conference Proceeding

Source Title

Proceedings - 2018 IEEE 5th International Conference on Data Science and Advanced Analytics, DSAA 2018

First Page

682

Last Page

691

Publication Date

1-31-2019

Abstract

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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Digitial Object Identifier (DOI)

10.1109/DSAA.2018.00088

Disciplines

Computer Sciences

Keywords

Sentiment analysis; Hashtags (Metadata); Twitter

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