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W2VLDA: Almost unsupervised system for Aspect Based Sentiment Analysis
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DOI：10.1016/j.eswa.2017.08.049.png)
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Abstract
En 中文
With the increase of online customer opinions in specialised websites and social networks, automatic systems to help organise and classify customer reviews by domain-specific aspect categories and sentiment polarity are more needed than ever. Supervised approaches for Aspect Based Sentiment Analysis achieve good results for the domain and language they are trained on, but manually labelling data to train supervised systems for all domains and languages is very costly and time consuming. In this work, we describe W2VLDA, an almost unsupervised system based on topic modelling that, combined with some other unsupervised methods and a minimal configuration step, performs aspect category classification, aspect-term and opinion-word separation and sentiment polarity classification for any given domain and language. We evaluate its domain aspect and sentiment classification performance in the multilingual SemEval 2016 task 5 (ABSA) dataset. We show competitive results for several domains (hotels, restaurants, electronic devices) and languages (English, Spanish, French and Dutch). (C) 2017 Elsevier Ltd. All rights reserved.
Keywords:
Opinion mining
Aspect Based Sentiment Analysis
Almost unsupervised
Multilingual
Multidomain
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