Sentiment Analysis of Tweets using Heterogeneous Multi-layer Network Representation and Embedding

Loitongbam Gyanendro Singh, Anasua Mitra, Sanasam Ranbir Singh


Abstract
Sentiment classification on tweets often needs to deal with the problems of under-specificity, noise, and multilingual content. This study proposes a heterogeneous multi-layer network-based representation of tweets to generate multiple representations of a tweet and address the above issues. The generated representations are further ensembled and classified using a neural-based early fusion approach. Further, we propose a centrality aware random-walk for node embedding and tweet representations suitable for the multi-layer network. From various experimental analysis, it is evident that the proposed method can address the problem of under-specificity, noisy text, and multilingual content present in a tweet and provides better classification performance than the text-based counterparts. Further, the proposed centrality aware based random walk provides better representations than unbiased and other biased counterparts.
Anthology ID:
2020.emnlp-main.718
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
8932–8946
Language:
URL:
https://aclanthology.org/2020.emnlp-main.718
DOI:
10.18653/v1/2020.emnlp-main.718
Bibkey:
Cite (ACL):
Loitongbam Gyanendro Singh, Anasua Mitra, and Sanasam Ranbir Singh. 2020. Sentiment Analysis of Tweets using Heterogeneous Multi-layer Network Representation and Embedding. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 8932–8946, Online. Association for Computational Linguistics.
Cite (Informal):
Sentiment Analysis of Tweets using Heterogeneous Multi-layer Network Representation and Embedding (Gyanendro Singh et al., EMNLP 2020)
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