Objective: The objective of this project is to analyze customer sentiment towards various products through the analysis of product reviews. The sentiment analysis will be performed using Natural Language Processing (NLP) techniques and machine learning algorithms.
Data Collection: The data for this project will be collected from Amazon e-commerce websites. The data will consist of product reviews, ratings, and other relevant information such as the date of review and the reviewer's demographic information.
Data Pre-processing: The collected data will undergo data pre-processing to remove any irrelevant information, fill in missing values, and perform text normalization. This will help to improve the accuracy of the sentiment analysis.
Sentiment Analysis: The sentiment analysis will be performed using NLP techniques such as tokenization, stemming, and stop-word removal. The sentiment of the reviews will be classified into positive, negative, and neutral sentiments. Machine learning algorithms such as Naive Bayes, Support Vector Machines, and Neural Networks will be used to train the model.
Evaluation: The performance of the sentiment analysis model will be evaluated using metrics such as accuracy, precision, recall, and F1-score.
Visualization: The results of the sentiment analysis will be visualized using various data visualization tools such as bar graphs, pie charts, and heatmaps. This will help to provide insights into the overall customer sentiment towards different products and identify any trends or patterns.
Conclusion: The sentiment analysis of product reviews will provide valuable insights into customer opinions and preferences. This information can be used by companies to improve their products and customer satisfaction. The project will also demonstrate the application of NLP techniques and machine learning algorithms in sentiment analysis.