Text Analytics with Python: A Practical Real-World Approach to Gaining Actionable Insights from your Data
暫譯: 使用 Python 進行文本分析:從數據中獲取可行見解的實用現實方法

Dipanjan Sarkar

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商品描述


Derive useful insights from your data using Python. You will learn both basic and advanced concepts, including text and language syntax, structure, and semantics. You will focus on algorithms and techniques, such as text classification, clustering, topic modeling, and text summarization.

Text Analytics with Python teaches you the techniques related to natural language processing and text analytics, and you will gain the skills to know which technique is best suited to solve a particular problem. You will look at each technique and algorithm with both a bird's eye view to understand how it can be used as well as with a microscopic view to understand the mathematical concepts and to implement them to solve your own problems.

What You Will Learn:

  • Understand the major concepts and techniques of natural language processing (NLP) and text analytics, including syntax and structure
  • Build a text classification system to categorize news articles, analyze app or game reviews using topic modeling and text summarization, and cluster popular movie synopses and analyze the sentiment of movie reviews
  • Implement Python and popular open source libraries in NLP and text analytics, such as the natural language toolkit (nltk), gensim, scikit-learn, spaCy and Pattern


Who This Book Is For :
IT professionals, analysts, developers, linguistic experts, data scientists, and anyone with a keen interest in linguistics, analytics, and generating insights from textual data

商品描述(中文翻譯)

從您的數據中使用 Python 獲取有用的見解。您將學習基本和進階概念,包括文本和語言的語法、結構和語義。您將專注於算法和技術,例如文本分類、聚類、主題建模和文本摘要。

《使用 Python 進行文本分析》教您與自然語言處理和文本分析相關的技術,並讓您掌握選擇最適合解決特定問題的技術的技能。您將從宏觀的角度了解每種技術和算法的使用方式,同時也會從微觀的角度理解數學概念並實施它們以解決自己的問題。

您將學到的內容:

- 理解自然語言處理 (NLP) 和文本分析的主要概念和技術,包括語法和結構
- 建立一個文本分類系統來對新聞文章進行分類,使用主題建模和文本摘要分析應用程式或遊戲評論,並聚類熱門電影的簡介及分析電影評論的情感
- 實施 Python 及流行的開源庫於 NLP 和文本分析,例如自然語言工具包 (nltk)、gensim、scikit-learn、spaCy 和 Pattern

本書適合的讀者:

IT 專業人士、分析師、開發人員、語言專家、數據科學家,以及對語言學、分析和從文本數據中生成見解有濃厚興趣的任何人。