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Improving aspect-level sentiment analysis with aspect extraction

Abstract

Aspect-based sentiment analysis (ABSA), a popular research area in NLP, has two distinct parts—aspect extraction (AE) and labelling the aspects with sentiment polarity (ALSA). Although distinct, these two tasks are highly correlated. The work primarily hypothesizes that transferring knowledge from a pre-trained AE model can benefit the performance of ALSA models. Based on this hypothesis, word embeddings are obtained during AE and, subsequently, feed that to the ALSA model. Empirically, this work shows that the added information significantly improves the performance of three different baseline ALSA models on two distinct domains. This improvement also translates well across domains between AE and ALSA tasks.

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Notes

  1. https://nlp.stanford.edu/projects/glove/.

  2. http://alt.qcri.org/semeval2014/task4/.

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Acknowledgements

This research is supported by A*STAR under its RIE 2020 Advanced Manufacturing and Engineering (AME) programmatic grant, Award No. A19E2b0098, Project name: K-EMERGE: Knowledge Extraction, Modelling, and Explainable Reasoning for General Expertise.

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Correspondence to Soujanya Poria.

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Majumder, N., Bhardwaj, R., Poria, S. et al. Improving aspect-level sentiment analysis with aspect extraction. Neural Comput & Applic 34, 8333–8343 (2022). https://doi.org/10.1007/s00521-020-05287-7

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Keywords

  • ALSA
  • AE
  • Knowledge transfer