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Estimating Importance From Web Reviews Through Textual Description and Metrics Extraction
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Author(s): Roney Lira de Sales Santos (University of Sao Paulo, Brazil), Carlos Augusto de Sa (Federal University of Piaui, Brazil), Rogerio Figueredo de Sousa (University of Sao Paulo, Brazil), Rafael Torres Anchiêta (University of Sao Paulo, Brazil), Ricardo de Andrade Lira Rabelo (Federal University of Piaui, Brazil)and Raimundo Santos Moura (Federal University of Piaui, Brazil)
Copyright: 2021
Pages: 26
Source title:
Natural Language Processing for Global and Local Business
Source Author(s)/Editor(s): Fatih Pinarbasi (Istanbul Medipol University, Turkey)and M. Nurdan Taskiran (Istanbul Medipol University, Turkey)
DOI: 10.4018/978-1-7998-4240-8.ch007
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Abstract
The evolution of e-commerce has contributed to the increase of the information available, making the task of analyzing the reviews manually almost impossible. Due to the amount of information, the creation of automatic methods of knowledge extraction and data mining has become necessary. Currently, to facilitate the analysis of reviews, some websites use filters such as votes by the utility or by stars. However, the use of these filters is not a good practice because they may exclude reviews that have recently been submitted to the voting process. One possible solution is to filter the reviews based on their textual descriptions, author information, and other measures. This chapter has a propose of approaches to estimate the importance of reviews about products and services using fuzzy systems and artificial neural networks. The results were encouraging, obtaining better results when detecting the most important reviews, achieving approximately 82% when f-measure is analyzed.
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