| Description |
1 online resource (268 p.) |
| Contents |
Cover -- Title -- Copyright -- End User License Agreement -- Contents -- Preface -- CONSENT FOR PUBLICATION -- CONFLICT OF INTEREST -- ACKNOWLEDGEMENT -- Introduction -- 1.1. INTRODUCTION -- 1.2. NATURAL LANGUAGE -- 1.2.1. From Linguistics to Natural Language Processing (NLP) -- 1.2.2. Natural Language Processing (NLP) -- 1.3. TEXT ANALYSIS -- 1.3.1. Advantages -- 1.3.2. Methods & Techniques -- 1.3.3. Sentiment Analysis (SA) -- 1.3.4. Topic Modelling -- 1.3.5. Intent Identification -- 1.3.6. Keyword Extraction -- 1.3.7. Entity Recognition -- 1.3.8. Text Analysis Functionality |
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1.4. TEXT SUMMARIZATION -- 1.4.1. Extraction -- 1.4.2. Abstractive Summarization -- 1.5. TEXT MINING AND WORKFLOW -- 1.5.1. Data Recovery -- 1.5.2. Data Extraction -- 1.5.3. Data Mining -- CONCLUSION -- REFERENCES -- Introduction to Python -- 2.1. INTRODUCTION -- 2.2. WORKING ENVIRONMENTS OF PYTHON -- Google Colab -- Features of Google Collaboratory (COLAB) -- 2.3.WORKING WITH ANACONDA -- Steps to Anaconda Installation -- 2.4. CREATING THE FIRST PROJECT IN GOOGLE COLAB CREATING THE FIRST PROJECT IN GOOGLE COLAB CREATING THE FIRST PROJECT IN GOOGLE COLAB CREATING THE FIRST PROJECT IN GOOGLE COLAB |
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2.5. MATHEMATICAL OPERATIONS -- 2.6. PYTHON LIBRARIES AND CONCEPTS -- Libraries -- a). Math and CMath Libraries -- b). SciPy Library -- c). ScikitLearn Library -- d). NumPy Library -- 2.7.BASIC CONCEPTS IN PYTHON -- a). Arrays -- b). Data Frames -- c). Loops -- For loop -- While Loop and the Else Branch -- Program: -- CONCLUSION -- REFERENCES -- Data Loading and Pre-Processing -- 3.1. INTRODUCTION -- 3.1. IMPORTING DATASETS -- 3.2. DATA RESHAPING -- 3.3. PIVOT AND MELT FUNCTIONS -- 3.4. STACKING AND UNSTACKING -- 3.5. DATA PRE-PROCESSING -- Outliers -- Missing Value Imputation |
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Handling of Missing Data -- Mean Calculation -- Deleting of Specific Row -- Dummy Variables -- One Hot Encoding -- 3.6. DATA VISUALIZATION -- Matplotlib -- ggplot Visualization -- Geoplot Visualization -- Regression Plots -- CONCLUSION -- REFERENCES -- Text Mining -- INTRODUCTION -- The Steps Followed for Text Mining are: -- Why Should we use Text Mining? -- Benefits of Text Mining -- Text Analysis in Real-Time -- Text Mining Applications -- Issues in Text Mining -- 4.1. TEXT MINING WITH PYTHON -- Program: -- Program: -- Program: -- Gensim Library -- Program: -- Output: -- Program |
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Output -- 4.2. DATA GATHERING -- Reading a Text File -- Steps for Reading a Text File in Python -- Open() Function -- Syntax -- Reading Text File -- Close () -- Syntax:close() -- Reading a CSV File -- Steps -- Reading Text from a PDF File -- import PyPDF2 -- Program -- 4.3. TEXT MINING PRE-PROCESSING TECHNIQUES -- Program: -- Output: -- Program: -- Output -- Program: -- Program: -- Program: -- Output -- Program: -- Output: -- Program: -- Program: -- 4.4. FEATURE SELECTION IN TEXT MINING -- Program -- Output: -- 4.5. TEXT SUMMARIZATION -- Program -- Program: -- 4.6. TEXT EXTRACTION |
| Summary |
Text Analysis with Python: A Research-Oriented Guide is a quick and comprehensive reference on text mining using python code. The main objective of the book is to equip the reader with the knowledge to apply various machine learning and deep learning techniques to text data. The book is organized into eight chapters which present the topic in a structured and progressive way. Key Features · Introduces the reader to Python programming and data processing · Intr |
| Notes |
Description based upon print version of record |
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4.6.1. Bag of Words |
| Form |
Electronic book
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| Author |
Battineni, Gopi
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Usharani, Bhimavarapu
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| ISBN |
9789815049602 |
|
9815049607 |
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