A Position Aware Decay Weighted Network for Aspect Based Sentiment Analysis
Autor: | Vijjini Anvesh Rao, Avinash Madasu |
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Rok vydání: | 2020 |
Předmět: |
business.industry
Computer science 05 social sciences Sentiment analysis 010501 environmental sciences computer.software_genre 01 natural sciences SemEval Term (time) Code segment 0502 economics and business Weighted network Artificial intelligence 050207 economics business computer Subnetwork Natural language processing Word (computer architecture) Sentence 0105 earth and related environmental sciences |
Zdroj: | Natural Language Processing and Information Systems ISBN: 9783030513092 NLDB |
Popis: | Aspect Based Sentiment Analysis (ABSA) is the task of identifying sentiment polarity of a text given another text segment or aspect. In ABSA, a text can have multiple sentiments depending upon each aspect. Aspect Term Sentiment Analysis (ATSA) is a subtask of ABSA, in which aspect terms are contained within the given sentence. Most of the existing approaches proposed for ATSA, incorporate aspect information through a different subnetwork thereby overlooking the advantage of aspect terms’ presence within the sentence. In this paper, we propose a model that leverages the positional information of the aspect. The proposed model introduces a decay mechanism based on position. This decay function mandates the contribution of input words for ABSA. The contribution of a word declines as farther it is positioned from the aspect terms in the sentence. The performance is measured on two standard datasets from SemEval 2014 Task 4. In comparison with recent architectures, the effectiveness of the proposed model is demonstrated. |
Databáze: | OpenAIRE |
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