Text Analytics with Python

Více o knize

Derive valuable insights from your data using Python with a focus on natural language processing and text analytics. This guide covers both basic and advanced concepts, including text and language syntax, structure, and semantics. You will explore algorithms and techniques such as text classification, clustering, topic modeling, and text summarization. The structured approach ensures that even readers with minimal experience can follow along without feeling overwhelmed. Beginning with the fundamentals of natural language and Python, you will progress to advanced analytical and machine learning concepts. Each technique and algorithm is examined from both a broad perspective and a detailed, mathematical viewpoint, allowing you to implement solutions for your own challenges. Key features include comprehensive coverage of major NLP and text analytics concepts, practical real-world examples like building a text classification system for news articles, analyzing app reviews through topic modeling, and clustering movie synopses while assessing sentiment in reviews. The implementations utilize Python and popular open-source libraries such as NLTK, Gensim, Scikit-learn, spaCy, and Pattern.