{"product_id":"154475","title":"Korean Text Analysis for Everyone with Python ","description":"\u003ccenter\u003e\u003cdiv style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/tmgdisk01.cafe24.com\/images\/vs\/4172\/sv\/3jYDPLS5peXrkqG7K8mDHIX1qB5mzT.png?v=1765080268\" style=\"max-width:100%;max-height:10px\"\u003e\u003c\/div\u003e\u003c\/center\u003e\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\u003ccenter\u003e\n\n\u003cdiv style=\"width:95%\"\u003e\n\n\u003cdiv style=\"text-align:center;font-size:30px;font-weight:bolder;line-height:1.6em\"\u003e Korean Text Analysis for Everyone with Python \u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"border-bottom:1px;border-bottom-style:dotted;border-color:;padding-bottom:20px\"\u003e\u003ccenter\u003e\u003ctable align=\"center\" width=\"100%\"\u003e\u003ctbody style=\"border:0px\"\u003e\n\n\u003ctr\u003e\u003ctd align=\"center\" style=\"line-height:1.2em;text-align:center;font-size:18px;color:black;font-weight:bold;padding-bottom:20px;\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\n\u003ctr\u003e\u003ctd style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/image.yes24.com\/goods\/119117002\/XL\" style=\"max-width:100%;height:auto\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\n\n\u003c\/tbody\u003e\u003c\/table\u003e\u003c\/center\u003e\u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"width:95%;{split_style6}padding-top:20px;padding-bottom:20px\"\u003e\n\n\u003cdiv style=\"text-align:left;font-size:16px;font-weight:bold;padding-bottom:20px\"\u003e Description \u003c\/div\u003e\n\n\u003cdiv style=\"text-align:left;word-break:break-all;font-size:14px;line-height:1.6em;\"\u003e\n\n\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eBook Introduction\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e\u003cdiv\u003e \u003cb\u003eEven the giant models of the distant future start from small models!\u003cbr\u003e Let's implement a small and simple model with our own hands right now!\u003cbr\u003e Includes 4 practical projects that anyone can easily follow!\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e How can computers understand Korean? What does it take to process Korean text with a computer? Whether you're starting text analysis from scratch or want to solidify and refine your fundamentals in text analysis and natural language processing, this book contains essential information.\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e First, before starting a full-fledged project, we will learn the basic Python concepts required for text analysis and the basic usage of essential Python libraries, and learn the functions and methods of text data preprocessing. \u003cbr\u003eNext, we will learn about the bag-of-words model and TF-IDF, which are basic concepts of text analysis and vectorization methods for converting text into numerical data.\u003cbr\u003e Next, we will proceed with an actual project using four different Korean data.\u003cbr\u003e The project covers the entire process from data downloading to preprocessing and visualization, and the hands-on training has been prepared as a colab so that you can easily do it anywhere right now.\u003cbr\u003e\n\n\u003c\/div\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cul\u003e\u003cli\u003e You can preview some of the book's contents.\u003cbr\u003e \u003cspan\u003ePreview\u003c\/span\u003e\n\n\u003c\/li\u003e\u003c\/ul\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eindex\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e \u003cb\u003eChapter 1: Getting Started with Colab\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e Entering LESSON OT\u003cbr\u003e LESSON 01 Running Colab\u003cbr\u003e ____1 Comment\u003cbr\u003e ____2 shortcut keys\u003cbr\u003e LESSON 02 Opening the Practice Code in Colab\u003cbr\u003e ____1 Colab Theme\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 2: Handling Strings in Python\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e Entering LESSON OT\u003cbr\u003e LESSON 01 Before String Practice\u003cbr\u003e LESSON 02 String Practice\u003cbr\u003e ____1 string representation\u003cbr\u003e ____2 Error Handling\u003cbr\u003e ____3 Expression method + error handling\u003cbr\u003e LESSON O3: Several Ways to Handle Strings\u003cbr\u003e ____1 variable \u003cbr\u003e____2 Indexing\u003cbr\u003e ____3 Slicing\u003cbr\u003e ____4 Length of string, number of words\u003cbr\u003e ____5 string functions\u003cbr\u003e ____6 repetitions\u003cbr\u003e ____7 function\u003cbr\u003e ____8 List of string built-in methods\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 3: Working with Libraries\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e Entering LESSON OT\u003cbr\u003e LESSON 01 Pandas\u003cbr\u003e ____1 Understanding Data Frames and Series\u003cbr\u003e ____2 Handling strings with str accessor\u003cbr\u003e LESSON 02 NumPy\u003cbr\u003e ____1 Understanding NumPy Arrays\u003cbr\u003e ____2 Visualizing NumPy arrays with matplotlib\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 4: Bag of Words Model and TF-IDF\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e Entering LESSON OT\u003cbr\u003e LESSON 01 Word Bag Model\u003cbr\u003e How to make a ____1 word bag model\u003cbr\u003e ____2 Create a word bag model\u003cbr\u003e ____3 n-gram: Used to group words before and after\u003cbr\u003e ____4 min_df and max_df: Setting the frequency\u003cbr\u003e ____5 max_features: Limit the number of learning words\u003cbr\u003e ____6 stop_words: Remove stop words\u003cbr\u003e ____7 analyzer: Set by character or word\u003cbr\u003e LESSON 02 TF-IDF\u003cbr\u003e ____1 How to apply TF-IDF weights\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 5 Yonhap News Title Topic Classification\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e Entering LESSON OT\u003cbr\u003e LESSON 01 Selecting Data\u003cbr\u003e LESSON 02 Classification Process  \u003cbr\u003eLESSON 03 Basic Settings for Classification\u003cbr\u003e ____1 Importing the library\u003cbr\u003e ____2 Font settings for visualization\u003cbr\u003e LESSON 04 Loading Data\u003cbr\u003e LESSON 05 Data Preprocessing\u003cbr\u003e ____1 Data Merge for Data Preprocessing\u003cbr\u003e ____2 Check the frequency of correct answers\u003cbr\u003e ____3 Check character length\u003cbr\u003e ____4 Visualizing histograms using matplotlib and seaborn\u003cbr\u003e ____5 Check the frequency of letters and words by topic\u003cbr\u003e LESSON 06 Preprocessing Text\u003cbr\u003e Remove ____1 number\u003cbr\u003e ____2 Change all English letters to lowercase\u003cbr\u003e ____3 Remove particles, endings, and punctuation with morphological analyzer\u003cbr\u003e ____4 Remove stop words\u003cbr\u003e LESSON 07 Separating Training and Test Data Sets\u003cbr\u003e LESSON 08 Vectorizing Words\u003cbr\u003e LESSON 09 Learning and Predicting\u003cbr\u003e ____1 Random Forest Classifier\u003cbr\u003e ____2 cross validation\u003cbr\u003e ____3 Learning\u003cbr\u003e LESSON 10 Loading the Answer Sheet\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 6: Visualizing and Classifying National Petition Data\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e Entering LESSON OT\u003cbr\u003e LESSON 01 Analysis Process\u003cbr\u003e LESSON 02 Basic Settings for Analysis\u003cbr\u003e ____1 Importing the library \u003cbr\u003eLESSON 03: Loading Data with Pandas\u003cbr\u003e ____1 Download files to Google Drive\u003cbr\u003e ____2 Review the downloaded data\u003cbr\u003e ____3 Check if there are any missing values\u003cbr\u003e LESSON 04 Pandas Data Analysis and Visualization\u003cbr\u003e ____1 Add a petition column for response\u003cbr\u003e ____2 Analysis by petition period\u003cbr\u003e ____3 Petition Period and Analysis by Sector\u003cbr\u003e ____4 Visualization\u003cbr\u003e LESSON 05: Drawing a Word Cloud with Soynlp\u003cbr\u003e ____1 Libraries and Data\u003cbr\u003e ____2 Tokenization\u003cbr\u003e ____3 Text data preprocessing\u003cbr\u003e ____4 Draw a word cloud\u003cbr\u003e ____5 Extract only nouns and visualize them\u003cbr\u003e LESSON 06: Classifying National Petition Data into Binary Formats Using Machine Learning\u003cbr\u003e ____1 Supervised learning and data set separation\u003cbr\u003e ____2 Determine the binary classification target\u003cbr\u003e ____3 Predict votes based on average\u003cbr\u003e ____4 Preprocessing\u003cbr\u003e ____5 Creating a training set and a test set\u003cbr\u003e ____6 Vectorizing words\u003cbr\u003e ____7 Applying TF-IDF weights\u003cbr\u003e ____8 Training with LightGBM\u003cbr\u003e Rate ____9\u003cbr\u003e Predict ____10\u003cbr\u003e ____11 Evaluating the accuracy of prediction results\u003cbr\u003e \u003cbr\u003e\u003cb\u003eChapter 7: Topic Modeling, RNNs, and LSTMs for the \"120 Dasan Call Foundation\"\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e Entering LESSON OT\u003cbr\u003e LESSON 01 Analysis Process\u003cbr\u003e LESSON 02: Classifying Topics with Latent Dirichlet Allocation\u003cbr\u003e ____1 Installing the library and loading data\u003cbr\u003e Vectorizing ____2 words\u003cbr\u003e ____3 Applying latent Dirichlet allocation\u003cbr\u003e ____4 Visualizing with pyLDAvis\u003cbr\u003e ____5 Similarity Analysis\u003cbr\u003e LESSON 03: Classifying Text with Recurrent Neural Networks\u003cbr\u003e ____1 Importing libraries and data\u003cbr\u003e ____2 Separate training\/test data sets\u003cbr\u003e ____3 Create label values ​​in matrix form\u003cbr\u003e ____4 Vectorize\u003cbr\u003e ____5 padding\u003cbr\u003e LESSON 04 Creating a Model\u003cbr\u003e ____1 Bidirectional LSTM\u003cbr\u003e Compile the ____2 model\u003cbr\u003e ____3 Learning\u003cbr\u003e ____4 Predict\u003cbr\u003e ____5 Rate it\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 8: Infraon Event Comment Analysis\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e Entering LESSON OT\u003cbr\u003e LESSON 01 Analysis Process\u003cbr\u003e LESSON 02 Basic Settings for Analysis\u003cbr\u003e ____1 Importing the library\u003cbr\u003e ____2 Loading data\u003cbr\u003e LESSON 03 Data Preprocessing\u003cbr\u003e ____1 Remove duplicate posts \u003cbr\u003e____2 Convert to lowercase\u003cbr\u003e LESSON 04: Separating \"Interest Lectures\" by String Splitting\u003cbr\u003e LESSON 05 Vectorizing\u003cbr\u003e LESSON 06 Vectorizing with TF-IDF Weights\u003cbr\u003e LESSON 07 Clustering\u003cbr\u003e ____1 KMeans\u003cbr\u003e ____2 MiniBatchKMeans\u003cbr\u003e ____3 Evaluate cluster predictions\u003cbr\u003e ____4 Analyzing silhouette coefficients\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 9: Automating Sentence Generation with ChatGPT\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e Entering LESSON OT\u003cbr\u003e LESSON 01 The concept of generative models\u003cbr\u003e LESSON 02 Parameter size and type of generative model\u003cbr\u003e LESSON 03 Using ChatGPT\u003cbr\u003e LESSON 04 Korean Generation Service: Rutton\u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eDetailed image\u003c\/b\u003e \u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cdiv\u003e\u003cimg src=\"https:\/\/image.yes24.com\/momo\/TopCate4187\/MidCate001\/418601973.jpg\" border=\"0\" alt=\"Detailed Image 1\"\u003e\u003c\/div\u003e\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003ePublisher's Review\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e \u003cb\u003eAnyone can easily analyze various Korean text data!\u003cbr\u003e\u003cbr\u003e Preparation: Colab environment and Python basics\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e The examples and projects in this book can be run directly in Colab with just a click, without installation. \u003cbr\u003eBefore starting a full-fledged project, we will learn the basic Python concepts required for text analysis and the basic usage of essential Python libraries such as Pandas, NumPy, and Scikit-learn, and learn the functions and methods of text data preprocessing.\u003cbr\u003e\u003cbr\u003e \u003cb\u003eConcept: Basic concepts of text analysis methods\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e Understand the basic concepts of text analysis methods, such as how computers understand Korean and how they analyze text data.\u003cbr\u003e And we learn the word bag model and TF-IDF as vectorization methods to convert text into numerical data for use with machine learning\/deep learning libraries.\u003cbr\u003e\u003cbr\u003e \u003cb\u003eProjects: Analyzing Four Real-World Projects\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e We will proceed with an actual project using four different Korean data. \u003cbr\u003e(1) Yonhap News title subject classification (2) National petition text analysis (3) 120 Dasan Call Foundation data topic modeling and similarity analysis (4) Infraon event comment text cluster analysis\u003cbr\u003e\u003cbr\u003e \u003cb\u003e[Author's Preface]\u003cbr\u003e\u003cbr\u003e “When will I be able to build a massive model after learning how to process trivial text?”\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e When you see the incredible performance of super-large models, you wonder what can be done with relatively little data and simple tasks.\u003cbr\u003e But even the largest models likely started from small attempts.\u003cbr\u003e Recent models can generate images or text, or answer questions like a human, with just a line or two of API code.\u003cbr\u003e But if you want to implement even a small and simple model directly in text, it can be difficult to know where to start. \u003cbr\u003eThis book is the result of my experiences analyzing texts and meeting with experts from various domains, and thinking about how to easily convey the skills and content.\u003cbr\u003e In a time when new research is pouring out every day, I hope this will serve as a good starting point for learning the basics of text analysis.\u003cbr\u003e - From the preface by author Park Jo-eun\u003cbr\u003e\u003cbr\u003e \u003cb\u003e“If there is no Korean text analysis book, why not just analyze English text in the same way and change the letters to Korean?”\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e But beginners don't know that if Korean characters appear broken, they should search for the words 'UTF-8 encoding'.\u003cbr\u003e Also, it's hard to know what data to start with, and it's hard to always ask someone why code that works fine in books or lectures causes errors when I try to do it myself.\u003cbr\u003e Everyone is like that. \u003cbr\u003eIf someone who knows looks at it, it may seem like a very low ledge, but if someone who doesn't know looks at it, it's difficult to get over.\u003cbr\u003e\u003cbr\u003e This book covers a variety of texts in Korean, and has been carefully structured to allow readers to learn NumPy, Pandas, and Scikit-learn naturally by learning the parts that change when changing data and repeating the same parts.\u003cbr\u003e However, you will feel the difficulty jump as you move from Chapter 3 to Chapter 4.\u003cbr\u003e But I hope that after Chapter 6, I will finally be able to feel like a second-year employee and say, “Ah! It’s similar!”\u003cbr\u003e\u003cbr\u003e If there are parts that don't work because the versions don't match, it will be very helpful for your studies if you fix them yourself and upload them to GitHub.\u003cbr\u003e Even if things don't go well, don't give up. Leave an inquiry, resolve it, and continue studying.\u003cbr\u003e I hope that you will join us as active partners rather than passive readers, while also challenging yourself creatively. \u003cbr\u003eAfter that, text analysis methods will also help to reveal the black box of deep learning.\u003cbr\u003e - From the preface by author Song Young-sook \u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"width:95%;padding-top:20px;padding-bottom:20px\"\u003e\n\n\u003cdiv style=\"text-align:left;font-size:16px;font-weight:bold;padding-bottom:20px\"\u003e GOODS SPECIFICS \u003c\/div\u003e\n\n\u003cdiv style=\"text-align:left;font-size:14px;line-height:1.6em;\"\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eDate of issue:\u003c\/strong\u003e May 29, 2023\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003ePage count, weight, size:\u003c\/strong\u003e 316 pages | 714g | 183*235*18mm\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eISBN13:\u003c\/strong\u003e 9791140704521 \u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\u003ccenter\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cspan\u003e\u003c\/span\u003e\n\n\u003c\/center\u003e\n\n\n\u003c\/center\u003e","brand":"LIBRAIRIE COREENNE","offers":[{"title":"Default Title","offer_id":43893429239850,"sku":"154475","price":37.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0683\/2750\/5962\/files\/463859eaf4765f2422b2f8cecb4ea0d5.jpg?v=1765401636","url":"https:\/\/librairie.coreenne.fr\/en\/products\/154475","provider":"LIBRAIRIE COREENNE","version":"1.0","type":"link"}