Estimating Importance From Web Reviews Through Textual Description and Metrics Extraction
Roney Lira de Sales Santos, Carlos Augusto de, Rogério Figueredo de Sousa, Rafael T. Anchiêta, Ricardo A. L. Rabêlo, Raimundo Santos Moura · Advances in business information systems and analytics book series · 2020
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.