Predicting Consumer Sentiment from Social Media by Text Mining
DOI:
https://doi.org/10.29304/jqcsm.2025.17.42551Keywords:
Feature Extraction, Machine learning, Sentiment Analysis, Sentiment Classification, Text MiningAbstract
Currently, social media has expanded and disseminated, and a vast quantity of information is available to individuals of all ages and is being disseminated over the Internet. This information is not only vast, but also rapidly disseminated and diverse. Consequently, conventional tools and methodologies are inadequate for managing this information. Given the rapid advancement of this field, it is imperative to cultivate capabilities and investigate solutions that enable the extraction of specific values from data sets and their accurate analysis. Data analysis is one of these solutions. For example, the classification of emotions through data mining, which employs machine learning, involves the treatment of information that is transmitted through a variety of communication channels as emotions and the development of analytical models. Using three text mining and machine learning techniques: k-means, Decision Tree, and Classification and Regression Tree (CART), a significant amount of information was collected in this study and transmitted to individuals via Twitter from the Amazon website (comments). The results indicated that the utmost accuracy was achieved by employing two methods to extract properties from non-structural data and convert them into usable numerical representations. It is obtained at a rate of 95% through the use of Bag of Word feature extraction in conjunction with CART. So, it outperformed to Decision Tree, while K-means, which the desired outcome did not get.
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