Unsupervised Aspect Term Extraction with B-LSTM and CRF using Automatically Labelled Datasets

Athanasios Giannakopoulos,Andreea Hossmann,Michael Baeriswyl,Claudiu Musat

doi:10.18653/v1/w17-5224

Athanasios Giannakopoulos, Andreea Hossmann + Show 2 more

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https://doi.org/10.18653/v1/w17-5224

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Publication Date: Jan 1, 2017
Citations: 69	License type: cc-by

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Aspect Term Extraction (ATE) identifies opinionated aspect terms in texts and is one of the tasks in the SemEval Aspect Based Sentiment Analysis (ABSA) contest. The small amount of available datasets for supervised ATE and the costly human annotation for aspect term labelling give rise to the need for unsupervised ATE. In this paper, we introduce an architecture that achieves top-ranking performance for supervised ATE. Moreover, it can be used efficiently as feature extractor and classifier for unsupervised ATE. Our second contribution is a method to automatically construct datasets for ATE. We train a classifier on our automatically labelled datasets and evaluate it on the human annotated SemEval ABSA test sets. Compared to a strong rule-based baseline, we obtain a dramatically higher F-score and attain precision values above 80%. Our unsupervised method beats the supervised ABSA baseline from SemEval, while preserving high precision scores.

Highlights

For many years companies are offering users the possibility of adding reviews in the form of sentences or small paragraphs
With respect to unsupervised Aspect Term Extraction (ATE), our technique achieves (i) very high precision and (ii) an F-score that exceeds the supervised baseline of the SemEval Aspect Based Sentiment Analysis (ABSA)
We present a B-LSTM & Conditional Random Fields (CRF) classifier which we use for feature extraction and aspect term detection for both supervised and unsupervised ATE

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Companies are offering users the possibility of adding reviews in the form of sentences or small paragraphs. Companies are interested in understanding how and what customers think about their products, which helps in employing marketing solutions and correction strategies. To this end, performing an automated analysis of the user opinions becomes a crucial issue. Performing sentiment analysis to detect the overall polarity of a sentence or paragraph comes with two major disadvantages. Sentiment analysis on sentence (or paragraph) level does not fulfill the purpose of getting more accurate and precise information. Many sentences or paragraphs contain opposing polarities towards distinct targets, making it impossible to assign an accurate overall polarity

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