Aspect-based sentiment analysis using adaptive aspect-based lexicons
Autor: | Mohammad Saniee Abadeh, Hamidreza Keshavarz, Mohammad Erfan Mowlaei |
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Rok vydání: | 2020 |
Předmět: |
Computer science
business.industry InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL Sentiment analysis General Engineering Context (language use) computer.software_genre Lexicon Computer Science Applications Artificial Intelligence Artificial intelligence business Baseline (configuration management) computer Natural language processing |
Zdroj: | Expert Systems with Applications. 148:113234 |
ISSN: | 0957-4174 |
DOI: | 10.1016/j.eswa.2020.113234 |
Popis: | Reviews expressed in e-commerce websites have formed an important source of information for both consumers and enterprises. Text sentiment analysis approaches aim to detect the sentiments of written reviews in order to achieve a better understanding of public opinion towards entities. Aspect-based sentiment analysis deals with capturing sentiments expressed towards each aspect of entities. A common approach in sentiment analysis problems is to take advantage of lexicons to generate features for classification of reviews. Existing aspect-based approaches fail to properly adapt general lexicons to the context of aspect-based datasets which results in reduced performance. To address this problem, this paper proposes extensions of two lexicon generation methods for aspect-based problems; one using statistical methods, and another using a genetic algorithm presented in our previous works. The aforementioned lexicons are then fused with prominent static lexicons to classify the aspects in reviews; this outperforms our previous works according to the t-test results (with a p-value of less than 0.001). Experimental results indicate that the proposed approach outperforms baseline methods in aspect-based polarity classification on Bing Liu's customer review datasets and improves precision, recall and F-measure by 6.0, 1.0, and 7.4 percentage points respectively. |
Databáze: | OpenAIRE |
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