Citation :

Chetna Khaparde, 2025. "Sentiment Analysis Using SVM Classifier in Data Mining: A Machine Learning Approach" International Journal of Computer Science & Information Technology  Volume 1, Issue 1: 29-33.

Abstract :

An vital part of data mining and natural language processing, abstract sentiment analysis offers understanding of public opinion, customer happiness, and social trends. This work uses a machine learning framework to explore the sentiment classification tasks' application of the Support Vector Machine (SVM) classifier. On a dataset of 10,000 labeled product reviews, a strategy comprising text preprocessing, TF-IDF feature extraction, and SVM model training is used in an organized fashion. With increased accuracy and resilience in categorizing positive, negative, and neutral emotions, the SVM classifier shows better performance than Naive Bayes and logistic regression models. Common in text mining applications, high-dimensional and sparse data management is confirmed by the results to be effective using SVM. The usefulness of SVM in real-time sentiment analysis applications is underlined in this research also together with ideas for improving performance with hybrid or ensemble techniques. investigates the use of Support Vector Machine (SVM) classifiers in sentiment analysis, a main activity in natural language processing (NLP) and data mining. The purpose is to assess SVM's ability to categorize text data into positive, negative, or neutral attitudes. Preprocessing a dataset of text reviews, TF-IDF feature extraction, SVM model training, and performance evaluation of the SVM model follow the study. SVM's efficiency is validated by a comparison analysis including different classifiers.

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Keywords :

sentiment analysis; support vector machines; SVM; data mining; machine learning; text classification; opinion mining; TF-IDF; natural language processing; product reviews.