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ABSTRACT

Now a day’s internet is most valuable source of learning, getting idea, reviews for a product. Sentiment analysis is a type of data mining that measures the user’s opinions through natural language processing(NLP). Sentiment analysis is also called as a opinion mining. It uses a data mining processes and techniques to extract and capture data for analysis the subjective opinion of a document or collection of documents like reviews, social media, e-commerce sites. In the field of sentiment analysis there are many algorithms have to tackle NLP problems to identify the positive and negative reviews of the user’s for your products on online market. Data used in this, we are study online product review collected from Amazon.com, Redif.com, Flipkart.com.

Abstract

In the era of e-commerce, customer reviews play a pivotal role in shaping the reputation and success of businesses. This postgraduate project, “Sentiment Analysis of Customer Reviews,” aims to harness the power of Python and web technologies to extract valuable insights from customer feedback. The project employs natural language processing (NLP) techniques for sentiment analysis, providing businesses with a comprehensive understanding of customer sentiments towards their products or services.

Existing System:

Current approaches to analyzing customer reviews often involve manual inspection, making it time-consuming and subjective. Basic sentiment analysis tools lack contextual understanding and fail to capture nuanced opinions.

Proposed System:

The proposed system introduces a sophisticated sentiment analysis tool that utilizes machine learning algorithms to decipher the sentiments expressed in customer reviews. This tool is integrated into a user-friendly web interface, providing businesses with actionable insights to improve customer satisfaction and product offerings.

Problem Statement:

Businesses face challenges in understanding and categorizing the sentiments expressed in vast amounts of customer reviews. The lack of an automated and accurate sentiment analysis system hinders their ability to respond promptly to customer concerns and capitalize on positive feedback.

Motivation:

This project is motivated by the need for an advanced sentiment analysis tool that can automate the processing of large volumes of customer reviews. By leveraging Python’s robust NLP libraries and modern web technologies, the project seeks to empower businesses to make data-driven decisions and enhance customer relations.

Modules Explanation:

1. Data Collection:

• Scraping and collecting customer reviews from various online platforms.

1. Text Preprocessing:

• Cleaning and preprocessing raw text data for analysis.

1. Sentiment Analysis:

• Utilizing machine learning models (e.g., Natural Language Toolkit – NLTK, scikit-learn) to analyze sentiments expressed in reviews.

1. Result Visualization:

• Generating visualizations to present sentiment analysis results, aiding in easy interpretation.

System Requirements:

• Frontend:
• HTML5, CSS3, JavaScript for the web interface.
• React.js for dynamic and responsive user interactions.
• Backend:
• Python for sentiment analysis algorithms.
• Flask or Django for web server development.
• Database:
• MongoDB or MySQL for storing preprocessed review data.

Algorithms:

• Sentiment Analysis Algorithm:
• Implementing machine learning models, such as Support Vector Machines (SVM) or Recurrent Neural Networks (RNN), for sentiment classification.

Hardware and Software Requirements:

• Hardware:
• Standard computing devices with internet connectivity.
• Software:
• Modern web browsers (Google Chrome, Mozilla Firefox).
• Python environment with necessary libraries (NLTK, scikit-learn).
• Flask or Django for backend development.

Architecture:

The system follows a client-server architecture where the React.js-based frontend communicates with the Python backend for sentiment analysis. Preprocessed data is stored in a relational or NoSQL database for efficient retrieval.

Technologies Used:

• Frontend:
• React.js, HTML5, CSS3, JavaScript.
• Backend:
• Python, Flask or Django.
• Database:
• MongoDB or MySQL.
• Data Analysis:
• NLTK, scikit-learn for sentiment analysis.

Web User Interface:

The web interface is designed for ease of use, featuring an intuitive design with interactive visualizations of sentiment analysis results. Users can explore insights derived from customer reviews, allowing businesses to make informed decisions and enhance customer satisfaction.

This project aims not only to automate sentiment analysis but also to provide businesses with a tool that translates customer sentiments into actionable strategies, fostering a customer-centric approach to product and service improvement.

UML DIAGRAMS

Collaboration Diagram

Architecture diagram

class diagram

sequence diagram

use case diagram

activity diagram

component diagram

Deployment Diagram

Flow chart Diagram