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
- 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)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.emnlp-main.718.pdf