Improving Aspect-Level Sentiment Analysis with Aspect Extraction
This work addresses aspect-based sentiment analysis for NLP applications, but it is incremental as it builds on existing methods by adding knowledge transfer.
The paper tackles the problem of improving aspect-level sentiment analysis by transferring knowledge from aspect extraction to sentiment labeling, showing that this approach significantly boosts the performance of three baseline models across two domains with concrete improvements.
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.