@inproceedings{gyanendro-singh-etal-2020-sentiment,
title = "Sentiment Analysis of Tweets using Heterogeneous Multi-layer Network Representation and Embedding",
author = "Gyanendro Singh, Loitongbam and
Mitra, Anasua and
Ranbir Singh, Sanasam",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.718",
doi = "10.18653/v1/2020.emnlp-main.718",
pages = "8932--8946",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Sentiment Analysis of Tweets using Heterogeneous Multi-layer Network Representation and Embedding
%A Gyanendro Singh, Loitongbam
%A Mitra, Anasua
%A Ranbir Singh, Sanasam
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 nov
%I Association for Computational Linguistics
%C Online
%F gyanendro-singh-etal-2020-sentiment
%X 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.
%R 10.18653/v1/2020.emnlp-main.718
%U https://aclanthology.org/2020.emnlp-main.718
%U https://doi.org/10.18653/v1/2020.emnlp-main.718
%P 8932-8946
Markdown (Informal)
[Sentiment Analysis of Tweets using Heterogeneous Multi-layer Network Representation and Embedding](https://aclanthology.org/2020.emnlp-main.718) (Gyanendro Singh et al., EMNLP 2020)
ACL