Text Analytics with Python: A Practitioner's Guide to Natural Language Processing, 2/e
暫譯: 使用 Python 進行文本分析：自然語言處理實務指南（第二版）

Sarkar, Dipanjan

Text Analytics with Python: A Practitioner's Guide to Natural Language Processing, 2/e

買這商品的人也買了...

商品描述

Leverage Natural Language Processing (NLP) in Python and learn how to set up your own robust environment for performing text analytics. This second edition has gone through a major revamp and introduces several significant changes and new topics based on the recent trends in NLP.

You'll see how to use the latest state-of-the-art frameworks in NLP, coupled with machine learning and deep learning models for supervised sentiment analysis powered by Python to solve actual case studies. Start by reviewing Python for NLP fundamentals on strings and text data and move on to engineering representation methods for text data, including both traditional statistical models and newer deep learning-based embedding models. Improved techniques and new methods around parsing and processing text are discussed as well.

Text summarization and topic models have been overhauled so the book showcases how to build, tune, and interpret topic models in the context of an interest dataset on NIPS conference papers. Additionally, the book covers text similarity techniques with a real-world example of movie recommenders, along with sentiment analysis using supervised and unsupervised techniques.

 

There is also a chapter dedicated to semantic analysis where you'll see how to build your own named entity recognition (NER) system from scratch. While the overall structure of the book remains the same, the entire code base, modules, and chapters has been updated to the latest Python 3.x release.


What You'll Learn

-Understand NLP and text syntax, semantics and structure-Discover text cleaning and feature engineering-Review text classification and text clustering - Assess text summarization and topic models- Study deep learning for NLP
Who This Book Is For
IT professionals, data analysts, developers, linguistic experts, data scientists and engineers and basically anyone with a keen interest in linguistics, analytics and generating insights from textual data.

商品描述(中文翻譯)

利用 Python 中的自然語言處理 (NLP)，學習如何建立自己的穩健環境以進行文本分析。本書第二版經過重大改版，根據 NLP 的最新趨勢引入了幾個重要的變更和新主題。

您將看到如何使用最新的 NLP 最先進框架，結合機器學習和深度學習模型，利用 Python 進行有監督的情感分析，以解決實際案例研究。首先回顧 Python 在 NLP 中的基礎知識，了解字符串和文本數據，然後進一步研究文本數據的表示方法，包括傳統的統計模型和較新的基於深度學習的嵌入模型。書中還討論了改進的技術和新的文本解析及處理方法。

文本摘要和主題模型已經過全面改造，因此本書展示了如何在 NIPS 會議論文的興趣數據集背景下構建、調整和解釋主題模型。此外，本書還涵蓋了文本相似性技術，並提供了電影推薦系統的實際範例，以及使用有監督和無監督技術的情感分析。

本書還有一章專門介紹語義分析，您將看到如何從零開始構建自己的命名實體識別 (NER) 系統。雖然本書的整體結構保持不變，但整個代碼庫、模組和章節已更新至最新的 Python 3.x 版本。

您將學到的內容：

- 了解 NLP 及文本的語法、語義和結構
- 探索文本清理和特徵工程
- 回顧文本分類和文本聚類
- 評估文本摘要和主題模型
- 研究 NLP 的深度學習

本書適合對象：

IT 專業人士、數據分析師、開發人員、語言專家、數據科學家和工程師，以及任何對語言學、分析和從文本數據中生成見解有濃厚興趣的人士。

作者簡介

Dipanjan Sarkar is a Data Scientist at Intel, the world's largest silicon company which is on a mission to make the world more connected and productive. He primarily works on Analytics, Business Intelligence, Application Development and building large scale Intelligent Systems. He received his master's degree in Information Technology from the International Institute of Information Technology, Bangalore with a focus on Data Science and Software Engineering. He is also an avid supporter of self-learning, especially Massive Open Online Courses and holds a Data Science Specialisation from Johns Hopkins University on Coursera.

He has been an analytics practitioner for over six years, specializing in statistical, predictive and text analytics. He has also authored a books on R and Machine Learning and occasionally reviews technical books and acts as a course beta tester for Coursera. Dipanjan's interests include learning about new technology, financial markets, disruptive start-ups, data science and more recently, artificial intelligence and deep learning. In his spare time he loves reading, gaming and watching popular sitcoms and football.

作者簡介(中文翻譯)

Dipanjan Sarkar 是英特爾（Intel）的數據科學家，英特爾是全球最大的矽公司，致力於讓世界變得更加互聯和高效。他主要從事分析、商業智慧、應用開發以及構建大規模智能系統的工作。他在班加羅爾的國際資訊技術學院獲得了資訊技術碩士學位，專注於數據科學和軟體工程。他也是自學的熱心支持者，特別是大規模開放在線課程（Massive Open Online Courses），並在Coursera上獲得了約翰霍普金斯大學的數據科學專業證書。

他在分析領域已有超過六年的實踐經驗，專注於統計、預測和文本分析。他還撰寫過有關R語言和機器學習的書籍，並偶爾審閱技術書籍，擔任Coursera的課程測試者。Dipanjan的興趣包括學習新技術、金融市場、顛覆性初創企業、數據科學，以及最近的人工智慧和深度學習。在空閒時間，他喜歡閱讀、玩遊戲和觀看熱門情境喜劇及足球比賽。

類似商品

最後瀏覽商品 (20)