ATP: A holistic attention integrated approach to enhance ABSA
Ashish Kumar (1), Vasundhra Dahiya (2), Aditi Sharan (1) ((1), Jawaharlal Nehru University, New Delhi, India, (2) Indian Institute of, Technology, Jodhpur, India)

TL;DR
This paper introduces ATP, a novel attention mechanism that incorporates dependency parsing to improve aspect-based sentiment analysis by better capturing the importance of words relative to aspects.
Contribution
It proposes a dependency parsing tree-based position encoding to enhance attention mechanisms in deep learning models for ABSA, outperforming simple distance-based methods.
Findings
Improved sentiment classification accuracy on SemEval'14 dataset.
Dependency parsing-based attention outperforms traditional word-distance approaches.
Demonstrated effectiveness of holistic attention in ABSA tasks.
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…
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Taxonomy
TopicsSentiment Analysis and Opinion Mining · Advanced Text Analysis Techniques · Text and Document Classification Technologies
MethodsSigmoid Activation · Tanh Activation · Gated Recurrent Unit · Long Short-Term Memory
