This project aims to perform sentiment analysis on Amazon product reviews in order to gain insights into customer opinions and satisfaction. Sentiment analysis, also known as opinion mining, is the use of natural language processing, text analysis, and computational linguistics to identify and extract subjective information from source materials. In the context of Amazon reviews, sentiment analysis can be used to determine the overall sentiment (positive, negative, neutral) of a review and gain insights into customer opinions and satisfaction towards specific products.
The Amazon review dataset provides a large number of reviews for various products, making it a valuable source of data for sentiment analysis. By analyzing the sentiment of these reviews, companies can gain a better understanding of customer satisfaction and make data-driven decisions to improve their products and customer service.
Additionally, sentiment analysis of Amazon reviews can also be useful for customers who are considering purchasing a specific product. By analyzing the sentiment of reviews, customers can gain a better understanding of the overall satisfaction of other customers and make more informed purchasing decisions.
In this project, we will be performing sentiment analysis on Amazon reviews using natural language processing and machine learning techniques. The goal is to gain insights into customer opinions and satisfaction towards specific products and provide valuable information for both companies and customers.
The dataset used in this project is a collection of Amazon product reviews, which can be found on the Amazon review dataset website.
OR it can be downloaded from the Kaggal for free
![Demo Screenshot]
The following libraries are required to run the code in this project:
- numpy
- nltk
- sklearn
- matplotlib
- seaborn
1. Clone the repository
git clone https://github.com/TABREZ-96/Voice-of-the-Customer.git
2. Install the required libraries
3. Run the jupyter notebook
The sentiment analysis is performed using a variety of techniques, including natural language processing and machine learning. The results of the analysis will be presented in the form of visualizations and metrics, such as accuracy and F1 score.
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The sentiment analysis of Amazon reviews provides valuable insights into customer opinions and satisfaction towards specific products. Through the use of natural language processing and machine learning techniques, we were able to analyze the sentiment of a large number of reviews and gain a better understanding of the overall sentiment towards specific products.
The results of this analysis can be used by companies to improve their products and customer service. For example, if a large number of negative reviews are found for a specific product, the company can take steps to address the issues raised in the reviews.
Additionally, this analysis can also be useful for customers who are considering purchasing a specific product. By analyzing the sentiment of reviews, customers can gain a better understanding of the overall satisfaction of other customers and make more informed purchasing decisions.
Overall, this project demonstrates the power of sentiment analysis in providing valuable insights and can be used in various industries to improve products, services and customer satisfaction..