Practical Natural Language Processing with Python

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Work with natural language tools and techniques to address real-world challenges through a focus on natural language processing (NLP) across various industries. Each chapter outlines specific problems and solution strategies, providing intuitive explanations of algorithms alongside in-depth code and output examples in Python. This case study-based approach dedicates each chapter to a particular industry or use case, tackling real business issues and exploring diverse solutions. Beginning with different types of text data, the book delves into the customer service sector, examining available data and common NLP challenges, including the bag-of-words model and supervised learning techniques applied to case studies. Similar thoroughness is applied to other sectors such as online reviews, bots, and finance. Key topics include sentiment analysis, named entity recognition, word2vec, word similarities, topic modeling, deep learning, and sequence-to-sequence modeling. By the conclusion, readers will be equipped to independently manage various NLP problems and develop innovative solutions. The book is designed for analytics and data science professionals eager to start with NLP, as well as NLP experts seeking fresh ideas for problem-solving.