Product Recommendation Using Sentiment Analysis

The corporate environment has gotten more competitive in recent years. Customer pleasure has shifted to the forefront of company strategy. Businesses spend a lot of money and human resources on different approaches to understand and satisfy their consumers’ needs. However, many firms fail to attain customer satisfaction as a result of poor manual analysis of a wide range of consumer demands. Because of this, they are shedding consumer commitment and incurring additional marketing costs. Sentiment Analysis may be used to solve the challenges. It combines Natural Language Processing (NLP) and Machine Learning techniques (ML). Sentiment Analysis is a technique for extracting information from public opinion on a wide range of subjects, goods, and services. We can accomplish that using any publicly available data online. On a look for a powerful strategy for Sentiment Analysis on a big, unbalanced, and multi-class dataset, we used two NLP approaches (Bag-of-Words and TF-IDF) and multiple ML classification algorithms (Support Vector Machine, Logistic Regression, Multinomial Naive Bayes, Random Forest). Sentimental analysis, or SA, is a continuing branch of research that examines customer feelings when purchasing things. This research report looks at a comprehensive mobile phone reviews study. Customer segmentation is an important part of every company's marketing strategy. In this machine learning project, we'll utilize nltk to cluster and manage unlabeled datasets. The findings reveal that the method works, with high accuracy in both mobile classification and user segmentation.