ATP: A holistic attention integrated approach to enhance ABSA

• URL: http://arxiv.org/abs/2208.02653v1
• Date: Thu, 4 Aug 2022 13:32:56 GMT
• Title: ATP: A holistic attention integrated approach to enhance ABSA
• Authors: Ashish Kumar (1), Vasundhra Dahiya (2), Aditi Sharan (1) ((1) Jawaharlal Nehru University, New Delhi, India, (2) Indian Institute of Technology, Jodhpur, India)
• Abstract summary: Aspect based sentiment analysis (ABSA) deals with the identification of the sentiment polarity of a review sentence towards a given aspect. We propose a method that captures the position based information using dependency parsing tree. We performed the experiments on SemEval'14 dataset to demonstrate the effect of dependency parsing relation-based attention for ABSA.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Aspect based sentiment analysis (ABSA) deals with the identification of the sentiment polarity of a review sentence towards a given aspect. Deep Learning sequential models like RNN, LSTM, and GRU are current state-of-the-art methods for inferring the sentiment polarity. These methods work well to capture the contextual relationship between the words of a review sentence. However, these methods are insignificant in capturing long-term dependencies. Attention mechanism plays a significant role by focusing only on the most crucial part of the sentence. In the case of ABSA, aspect position plays a vital role. Words near to aspect contribute more while determining the sentiment towards the aspect. Therefore, we propose a method that captures the position based information using dependency parsing tree and helps attention mechanism. Using this type of position information over a simple word-distance-based position enhances the deep learning model's performance. We performed the experiments on SemEval'14 dataset to demonstrate the effect of dependency parsing relation-based attention for ABSA.

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