Abstract

Sentiment Analysis is a rapidly growing field in Natural Language Processing (NLP) that aims to extract opinions, emotions, and attitudes expressed in text. It has a wide range of applications, including opinion mining, market research, customer service, and social media analysis. Python, with its vast libraries and frameworks, has become one of the most popular programming languages for NLP tasks, including Sentiment Analysis. The combination of Python and NLP provides a powerful toolset for performing sentiment analysis on text data. This book is a comprehensive guide to Sentiment Analysis using Python. It covers the basics of NLP and Sentiment Analysis, as well as advanced techniques for sentiment classification and sentiment visualization. The book also provides practical examples and case studies to help you understand the concepts and techniques. The book starts with an introduction to NLP and the different techniques used in Sentiment Analysis. It then moves on to cover the basics of sentiment classification, including feature extraction, text preprocessing, and model training. The book also provides a detailed explanation of various machine learning algorithms and deep learning models used in Sentiment Analysis. In addition to the theoretical concepts, the book includes hands-on examples and case studies that demonstrate the implementation of Sentiment Analysis using Python. The examples are accompanied by clear and concise code snippets that are easy to follow. The book is structured in a way that makes it easy for readers to follow, with each chapter building upon the previous one. By the end of the book, readers will have a solid understanding of Sentiment Analysis and will be able to apply their knowledge to real-world problems. In addition to the core concepts, the book also covers advanced topics such as sentiment analysis on different languages, sentiment analysis of social media data, and sentiment analysis of financial data. These topics will help readers to expand their knowledge and explore new applications of Sentiment Analysis. One of the key features of the book is its focus on practical applications. Throughout the book, readers will be encouraged to apply the concepts and techniques covered to their own data. The book includes a number of datasets and code snippets that can be easily modified and used for your own projects. viii Whether you are a beginner or an experienced data scientist, this book will provide you with the knowledge and skills you need to perform Sentiment Analysis using Python. So, whether you want to analyze customer feedback, measure public opinion, or analyze social media trends, this book will be an indispensable resource for you. In conclusion, Sentiment Analysis using Python is a must-read for anyone interested in NLP and Sentiment Analysis. Whether you are a beginner or an experienced data scientist, this book will provide you with the knowledge and skills you need to perform Sentiment Analysis effectively. So, get ready to dive into the world of NLP and Sentiment Analysis, and discover the power of Python.

Keywords: Sentiment Analysis, Natural Language Processing (NLP), opinions, emotions, attitudes, text, opinion mining, market research, customer service, social media analysis, Python, programming languages, NLP tasks, comprehensive guide, basics, advanced techniques, sentiment classification, sentiment visualization, practical examples, case studies, introduction, techniques, feature extraction, text preprocessing, model training, machine learning algorithms, deep learning models, theoretical concepts, hands-on examples, code snippets, chapter building, solid understanding, real-world problems, different languages, social media data, financial data, practical applications, data scientist, customer feedback, public opinion, social media trends, indispensable resource, beginner, experienced, NLP, must-read, dive, world, power.

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Book Citation

Uma Devi. G.,(2023). Sentiment Analysis with Python: A Hands-on Approach(1st ed., Vol. 1). Magestic Technology Solutions (P) Ltd. ISBN: : 978-93-92090-11-0. DOI: https://doi.org/10.47716/MTS.B.978-93-92090-11-0