Customer_Setntiment_Analysis_LAST_YEAR_PROJECT.pptx

It is a customer sentiment analysis ppt for my final year project this is used by any of the one who need something about Customer Sentiment Analysis

1. Presented By: Vipush Manga (2000681540049) Shivang Sharma (2000681540039) Shashank Chaudhary ( 2000681540038) Under the Guidance of: Mr. Rohit Aggarwal Department of Data Science (CSE) Meerut Institute of Engineering & Technology, Meerut (U.P.) CUSTOMER SENTIMENT ANALYSIS

2. Introduction Research/Study Gap Problem Statement Objectives of the study Methodology DFD/Flow chart related to study Hardware & Software to be used References 2 Presentation Outlines

3. Introduction 3 In today's dynamic and highly competitive business landscape, understanding and responding to customer sentiments have become paramount for organizations striving to excel in the market. Customer feedback, whether gleaned from product reviews, surveys, or social media interactions, represents a valuable source of insights that can drive product enhancements, customer satisfaction, and ultimately, business success. However, the sheer volume and unstructured nature of this data pose significant challenges for organizations seeking to harness its potential. The "Customer Sentiment Analysis" project is a visionary endeavor aimed at addressing these challenges through the development of a sophisticated software system. This system leverages cutting-edge technologies, including Natural Language Processing (NLP) and machine learning, to transform raw customer feedback into actionable intelligence. By applying advanced sentiment analysis techniques, the project seeks to empower businesses across diverse industries to gain deep insights into customer sentiments, preferences, and pain points.

4. Introduction 4 The "Customer Sentiment Analysis" project is a visionary endeavor aimed at addressing these challenges through the development of a sophisticated software system. This system leverages cutting-edge technologies, including Natural Language Processing (NLP) and machine learning, to transform raw customer feedback into actionable intelligence. By applying advanced sentiment analysis techniques, the project seeks to empower businesses across diverse industries to gain deep insights into customer sentiments, preferences, and pain points.

5. Research Gap 5 While there is a substantial body of research and practical applications in sentiment analysis, the existing literature often lacks a comprehensive approach that seamlessly integrates data collection, preprocessing, sentiment analysis, data visualization, and reporting within a single, user-friendly platform tailored for businesses

6. Problem Statement 6 In a world inundated with data, organizations often struggle to distill meaningful information from the vast sea of customer feedback. Traditional methods of manual analysis are time-consuming, error-prone, and limited in scalability. Furthermore, without an automated sentiment analysis system, businesses risk overlooking critical patterns, trends, and opportunities buried within customer comments.

7. Objectives of the study 7 The primary objectives of the "Customer Sentiment Analysis" project include: 1 . Developing a robust system capable of collecting, processing, and analyzing customer feedback data from various sources. 2. Implementing state-of-the-art NLP and machine learning algorithms to accurately categorize customer sentiments. 3 . Providing users with intuitive data visualization tools to explore sentiment trends and patterns.

8. Objectives of the study 8 4 . Enabling businesses to generate comprehensive sentiment analysis reports for specific products, services, or time periods. 5 . Ensuring secure and role-based access to the system through user management.

9. Methodology 9 The methodology employed for the "Customer Sentiment Analysis" project entails a structured approach encompassing data collection, preprocessing, sentiment analysis, data visualization, reporting, and user management. This systematic process ensures that customer feedback is transformed into actionable insights efficiently and accurately. .

10. DFD/Flow chart of the study 10

11. Hardware/Software to be used 11 Hardware : The project requires server infrastructure with sufficient computational resources for data processing and storage . Software : Key software components include Python for data preprocessing and sentiment analysis, a web application framework for the user interface, a database management system for data storage, and data visualization libraries for graphical representation.

12. References 12 Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends® in Information Retrieval, 2(1-2), 1-135. Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 5(1), 1-167. Hu , M., & Liu, B. (2004). Mining and summarizing customer reviews. In Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '04), 168-177.