Sentiment Analysis of Comments Received Through E- Consultation Module Software

Abstract: E-consultation platforms collect a large amount of feedback from users, but going through all these comments manually is difficult and time-consuming. This project focuses on building a sentiment analysis system that can automatically understand and classify user opinions as positive, negative, or neutral. It uses natural language processing (NLP) techniques such as text cleaning, tokenization, and removal of unnecessary words to prepare the data. Machine learning methods like Naïve Bayes and Support Vector Machine are applied to identify the sentiment, and advanced models can be added to improve accuracy when needed. The system also presents the results using simple charts and graphs, making it easier to understand overall user opinion and identify common issues. This helps organizations quickly take better decisions and improve their services. The project also highlights that many existing systems do not fully support real-time analysis, multiple languages, or easy interpretation of results, and aims to provide a more practical and efficient solution.

Keywords: Sentiment Analysis; Natural Language Processing (NLP); Machine Learning; Text Classification; Naïve Bayes; Support Vector Machine (SVM); Logistic Regression; Deep Learning; User Feedback Analysis; E-Consultation Systems; Opinion Mining; Data Visualization; Public Opinion Analysis; Predictive Analytics.