"Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning" by Palash Goyal and Sumit Pandey is a book that provides a comprehensive guide to text analysis and natural language processing (NLP) using Python. The book is aimed at developers and data scientists who want to learn how to use Python to analyze and extract insights from text data.
The book covers a wide range of topics in text analysis and NLP, including data preprocessing, feature extraction, topic modeling, sentiment analysis, and text classification. Each chapter provides a detailed overview of the problem and the data, as well as step-by-step instructions for implementing machine learning models in Python.
One of the strengths of the book is its practical focus. The authors provide clear explanations of the machine learning techniques used in each application, along with the Python code needed to implement them. The code is well-organized and easy to follow, making it easy for readers to adapt and customize the models for their own applications.
Another strength of the book is its emphasis on data preprocessing and feature engineering. The authors explain the importance of these steps in the text analysis process and provide practical guidance on how to perform them effectively. This is a valuable resource for professionals who are new to text analysis and NLP and want to learn how to work with messy or complex text data.
Overall, "Applied Text Analysis with Python" is a great resource for developers and data scientists who want to learn how to use Python to analyze and extract insights from text data. The book covers a wide range of topics in text analysis and NLP and provides practical solutions to real-world problems, making it a valuable reference for anyone interested in text analysis with Python.
