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

Loitongbam Gyanendro Singh, Anasua Mitra, Sanasam Ranbir Singh

Sentiment classification on Twitter text often needs to deal with the problems of under-specificity, noise, and multilingual content. In this study, we propose 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 analyses, 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 counterpart. Further, the proposed centrality aware based random walk provides better representations than unbiased and other biased counterparts.

Proposed framework

Requirement

The software can run on CPU or GPU, dependency requirements are following:

• python
• tensorflow

pip install numpy
pip install keras
pip install pandas
pip install networkx
pip install gensim
pip install nltk
pip install editdistance
pip install emoji

This work is published in EMNLP 2020

@inproceedings{singh2020sentiment,
  title={Sentiment Analysis of Tweets using Heterogeneous Multi-layer Network Representation and Embedding},
  author={Singh, Loitongbam Gyanendro and Mitra, Anasua and Singh, Sanasam Ranbir},
  booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  pages={8932--8946},
  year={2020}
}