Improving Aspect-Level Sentiment Analysis with Aspect Extraction

• URL: http://arxiv.org/abs/2005.06607v1
• Date: Sun, 3 May 2020 06:25:16 GMT
• Title: Improving Aspect-Level Sentiment Analysis with Aspect Extraction
• Authors: Navonil Majumder, Rishabh Bhardwaj, Soujanya Poria, Amir Zadeh, Alexander Gelbukh, Amir Hussain, Louis-Philippe Morency
• Abstract summary: The work primarily hypothesizes that transferring knowledge from a pre-trained AE model can benefit the performance of ALSA models. Empirically, this work show that the added information significantly improves the performance of three different baseline ALSA models.
• Score: 104.3459510527776
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Aspect-based sentiment analysis (ABSA), a popular research area in NLP has two distinct parts -- aspect extraction (AE) and labeling the aspects with sentiment polarity (ALSA). Although distinct, these two tasks are highly correlated. The work primarily hypothesize 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 show 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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