Understanding Twitter Hashtags from Latent Themes Using Biterm Topic Model

Title:Understanding Twitter Hashtags from Latent Themes Using Biterm Topic Model

Volume: 14 Issue: 3

Author(s): Muzafar R. Bhat*, Burhan Bashir , Majid A. Kundroo and Naffi A. Ahanger

Affiliation:

• Department of Computer Sciences, Islamic University of Science and Technology, Awantipora 192122, Jammu and Kashmir,India

Keywords: Topic Modelling, LDA, BTM, Social Media Analysis (SMA), twitter analysis, #MeToo.

Abstract:

Social media, in general, and Twitter, in particular, provide a space for discourses, contemporary narratives besides a discussion about few specific social issues. People respond to these events by writing short text messages.

Background: Hashtag “#”, a specific way to respond to a given raised discourse, narrative or any contemporary issue is usual to social media. Netizens write a short message as their opinion about any given issue represented using a given Hashtag. These small messages generally tend to have a latent topic (theme) as one’s opinion about it.

Objective: This research is aimed to extract, represent and understand those hidden themes.

Method: Biterm Topic Model (BTM) has been used in this study given its ability to deal with the short messages unlike Latent Dirichlet Allocation that expects a document to have a significant length.

Results: Twitter Hashtag #M comments. Data has been modelled with ten (10) topic.

Conclusion: The experimental results show that the proposed approach to understand the twittter hashtages from latent themes using biterm topic modelling method is very effective as compared to other methods.

Cite this article as:

Bhat R. Muzafar *, Bashir Burhan, Kundroo A. Majid and Ahanger A. Naffi, Understanding Twitter Hashtags from Latent Themes Using Biterm Topic Model, Recent Patents on Engineering 2020; 14 (3) . https://dx.doi.org/10.2174/1872212113666190328183517