Sentiment Analysis in Social Networks through Topic modeling

Debashis Naskar, Sidahmed Mokaddem, Miguel Rebollo, Eva Onaindia


Abstract
In this paper, we analyze the sentiments derived from the conversations that occur in social networks. Our goal is to identify the sentiments of the users in the social network through their conversations. We conduct a study to determine whether users of social networks (twitter in particular) tend to gather together according to the likeness of their sentiments. In our proposed framework, (1) we use ANEW, a lexical dictionary to identify affective emotional feelings associated to a message according to the Russell’s model of affection; (2) we design a topic modeling mechanism called Sent_LDA, based on the Latent Dirichlet Allocation (LDA) generative model, which allows us to find the topic distribution in a general conversation and we associate topics with emotions; (3) we detect communities in the network according to the density and frequency of the messages among the users; and (4) we compare the sentiments of the communities by using the Russell’s model of affect versus polarity and we measure the extent to which topic distribution strengthen likeness in the sentiments of the users of a community. This works contributes with a topic modeling methodology to analyze the sentiments in conversations that take place in social networks.
Anthology ID:
L16-1008
Volume:
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)
Month:
May
Year:
2016
Address:
Portorož, Slovenia
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
46–53
Language:
URL:
https://aclanthology.org/L16-1008
DOI:
Bibkey:
Cite (ACL):
Debashis Naskar, Sidahmed Mokaddem, Miguel Rebollo, and Eva Onaindia. 2016. Sentiment Analysis in Social Networks through Topic modeling. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 46–53, Portorož, Slovenia. European Language Resources Association (ELRA).
Cite (Informal):
Sentiment Analysis in Social Networks through Topic modeling (Naskar et al., LREC 2016)
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PDF:
https://preview.aclanthology.org/update-css-js/L16-1008.pdf