Sentiment Analysis of Customer Review Using NLP

Author(s): Md Zubair Alam, Muhammad Tarique, Sufi Bilal Ahmad and Shra Fatima

Abstract:

Sentiment evaluation has emerged as a important thing of natural language processing (NLP), permitting automatic interpretation of subjective statistics from textual data. This paper provides a complete study on sentiment evaluation using machine learning techniques to categorise text into superb, negative, and impartial classes. The proposed device employs preprocessing techniques inclusive of tokenization, stop word removal, stemming, and lemmatization to clean uncooked textual content statistics. Characteristic extraction is performed the usage of term Frequency-Inverse record Frequency (TF-IDF) and Bag-of-Words representations. A couple of type algorithms such as Logistic Regression, Naïve Bayes, support Vector Machines (SVM), and ensemble methods are evaluated. Experimental results reveal that the proposed approach achieves significant accuracy in sentiment classification, with SVM and Logistic Regression displaying superior performance. The device addresses demanding situations in managing noisy information, informal language, and area-particular expressions. This study contributes to the growing body of work in automated sentiment detection and provides insights for real-international applications in customer remarks analysis, social media monitoring, and emblem popularity control.

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Pages: 82-87     |    31 View     |    3 Download