Volume VI Issue III July-September 2026

Name of Author : Hiral J. Meswania, Dr.Pankaj Dixit

Title of the paper : Sentiment-Driven Recommendation Systems: Exploring the Role of Machine Learning in Personalization

Abstract: Digital content and e-commerce have taken the centre stage. They have been able to enhance user experience using personalization. Most traditional recommendation systems rely on user preferences and behaviours to recommend items. However, these systems are known to overlook the influence of emotion and sentiment relevant to decision-making. This study investigates the use of sentiment analysis with machine learning to develop recommendation systems that combine the past data of the user and emotional sentiment to deliver better results. Sentiment analysis of user-generated content, reviews, social media, and feedback is incorporated in the system to predict and recommend products or services based on user sentiment using machine learning algorithms. A recommendation system using sentiment indicators, whether it is positive or negative, uses the mood, context and now of a user in a smart way. This technique uses a hybrid approach that combines collaborative filtering and content-based filtering for recommendations which leads to higher accuracy. This hybrid model uses Natural Language Processing NLP for sentiment extraction along with collaborative filtering and content-based filtering for generating recommendations. By using different supervised as well as unsupervised learning algorithms like deep learning and support vector machine the large data can be analyzed in addition to the betterment of the model’s prediction. To evaluate the proposed system, we will conduct several experiments in various fields, including e-commerce and social media, among others. Thus, we will check the proposed system in terms of recommendation efficiency, satisfaction and emotions.

Keywords : Sentiment Analysis, Recommendation Systems, Machine Learning, Personalization, Natural Language Processing

DOI :

Page No. : 26-34