Applied Text Mining
Qamar, Usman, Raza, Muhammad Summair
- 出版商: Springer
- 出版日期: 2024-06-11
- 售價: $3,240
- 貴賓價: 9.5 折 $3,078
- 語言: 英文
- 頁數: 494
- 裝訂: Quality Paper - also called trade paper
- ISBN: 3031519167
- ISBN-13: 9783031519161
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相關分類:
Text-mining
海外代購書籍(需單獨結帳)
相關主題
商品描述
This textbook covers the concepts, theories, and implementations of text mining and natural language processing (NLP). It covers both the theory and the practical implementation, and every concept is explained with simple and easy-to-understand examples.
It consists of three parts. In Part 1 which consists of three chapters details about basic concepts and applications of text mining are provided, including eg sentiment analysis and opinion mining. It builds a strong foundation for the reader in order to understand the remaining parts. In the five chapters of Part 2, all the core concepts of text analytics like feature engineering, text classification, text clustering, text summarization, topic mapping, and text visualization are covered. Finally, in Part 3 there are three chapters covering deep-learning-based text mining, which is the dominating method applied to practically all text mining tasks nowadays. Various deep learning approaches to text mining are covered, includingmodels for processing and parsing text, for lexical analysis, and for machine translation. All three parts include large parts of Python code that shows the implementation of the described concepts and approaches.
The textbook was specifically written to enable the teaching of both basic and advanced concepts from one single book. The implementation of every text mining task is carefully explained, based Python as the programming language and Spacy and NLTK as Natural Language Processing libraries. The book is suitable for both undergraduate and graduate students in computer science and engineering.
商品描述(中文翻譯)
這本教科書涵蓋了文本挖掘和自然語言處理(NLP)的概念、理論和實現。它既包含理論又包含實際實現,每個概念都通過簡單易懂的例子進行解釋。
教科書分為三個部分。第一部分包含三章,詳細介紹了文本挖掘的基本概念和應用,包括情感分析和意見挖掘等。它為讀者打下堅實的基礎,以便理解後面的部分。第二部分包含五章,涵蓋了文本分析的所有核心概念,如特徵工程、文本分類、文本聚類、文本摘要、主題映射和文本可視化。最後,在第三部分中,有三章介紹基於深度學習的文本挖掘,這是當今幾乎所有文本挖掘任務中應用最廣泛的方法。涵蓋了各種深度學習方法,包括處理和解析文本的模型,詞法分析的模型和機器翻譯的模型。所有三個部分都包含大量的Python代碼,展示了所描述的概念和方法的實現。
這本教科書特別編寫,旨在通過一本書教授基礎和高級概念。每個文本挖掘任務的實現都有詳細的解釋,使用Python作為編程語言,Spacy和NLTK作為自然語言處理庫。這本書適合計算機科學和工程學的本科生和研究生使用。
作者簡介
Usman Qamar has over 15 years of experience in data engineering and decision sciences both in academia and industry. He is currently Tenured Professor of Data Sciences at the National University of Sciences and Technology (NUST) Pakistan and director of Knowledge and Data Science Research Centre, a Centre of Excellence at NUST, Pakistan. He has authored nearly 200 peer-reviewed publications and has also received multiple research awards.
Muhammad Summair Raza is currently associated with the Virtual University of Pakistan as an assistant professor. He has published various papers in international-level journals and conferences with a focus on rough set theory. His research interests include feature selection, rough set theory, trend analysis, software design, software architecture, and non-functional requirements.
作者簡介(中文翻譯)
Usman Qamar在學術界和工業界擁有超過15年的數據工程和決策科學經驗。他目前是巴基斯坦國立科學與技術大學(NUST)的數據科學終身教授,也是NUST的知識與數據科學研究中心的主任,該中心是NUST的卓越中心。他已經發表了近200篇同行評審的論文,並且也獲得了多個研究獎項。
Muhammad Summair Raza目前是巴基斯坦虛擬大學的助理教授。他在國際級期刊和會議上發表了多篇論文,專注於粗糙集理論。他的研究興趣包括特徵選擇、粗糙集理論、趨勢分析、軟件設計、軟件架構和非功能需求。