Multi-Aspect Sentiment Analysis with Latent Sentiment-Aspect Attribution

• URL: http://arxiv.org/abs/2012.08407v1
• Date: Tue, 15 Dec 2020 16:34:36 GMT
• Title: Multi-Aspect Sentiment Analysis with Latent Sentiment-Aspect Attribution
• Authors: Yifan Zhang, Fan Yang, Marjan Hosseinia, Arjun Mukherjee
• Abstract summary: We introduce a new framework called the sentiment-aspect attribution module (SAAM) The framework works by exploiting the correlations between sentence-level embedding features and variations of document-level aspect rating scores. Experiments on a hotel review dataset and a beer review dataset have shown SAAM can improve sentiment analysis performance.
• Score: 7.289918297809611
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In this paper, we introduce a new framework called the sentiment-aspect attribution module (SAAM). SAAM works on top of traditional neural networks and is designed to address the problem of multi-aspect sentiment classification and sentiment regression. The framework works by exploiting the correlations between sentence-level embedding features and variations of document-level aspect rating scores. We demonstrate several variations of our framework on top of CNN and RNN based models. Experiments on a hotel review dataset and a beer review dataset have shown SAAM can improve sentiment analysis performance over corresponding base models. Moreover, because of the way our framework intuitively combines sentence-level scores into document-level scores, it is able to provide a deeper insight into data (e.g., semi-supervised sentence aspect labeling). Hence, we end the paper with a detailed analysis that shows the potential of our models for other applications such as sentiment snippet extraction.

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