{"product_id":"154668","title":"The Complete Guide to Python Text Mining ","description":"\u003ccenter\u003e\u003cdiv style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/tmgdisk01.cafe24.com\/images\/vs\/4172\/sv\/3jYDPLPVKCxekjBFZN2w85TezojvkY.png?v=1765081263\" 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 The Complete Guide to Python Text Mining \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\/117596408\/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\u003eText mining requires extensive knowledge of natural language processing, statistics, and deep learning techniques, but if you get caught up in the theoretical aspects, implementation can easily become a distant dream.\u003cbr\u003e This book explains text mining through practical examples that can be applied directly to real-world situations.\u003cbr\u003e Especially for beginners, it explains the concept of text preprocessing and various detailed applications from the basics.\u003cbr\u003e We demonstrate examples of using various machine learning techniques for text mining tasks such as document classification and sentiment analysis, and explain how to reduce dimensionality and visualize the results, perform topic modeling, and obtain and visualize topic trends.\u003cbr\u003e\u003cbr\u003e In addition to document classification using basic deep learning techniques, it also includes fine-tuning learning using BERT, which is widely used these days. \u003cbr\u003eIn addition, as interest in pre-trained language models increases, the theoretical content of pre-trained language models and various transformer transformation models are explained.\u003cbr\u003e We also added practical training and fine-tuning learning for document summarization and question-answering using the transformer model.\u003cbr\u003e Additionally, most chapters are covered with sufficient examples to help you gain confidence in analyzing Korean documents.\u003cbr\u003e\n\u003c\/div\u003e\n\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\u003e[Part 1] Text Mining Basics\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e Chapter 1: Text Mining Basics\u003cbr\u003e 1.1 Definition of Text Mining\u003cbr\u003e 1.2 Shifts in the Text Mining Paradigm\u003cbr\u003e ___1.2.1 Count-based document representation\u003cbr\u003e ___1.2.2 Sequence-based document representation\u003cbr\u003e 1.3 Knowledge and tools required for text mining\u003cbr\u003e ___1.3.1 Natural Language Processing Techniques\u003cbr\u003e ___1.3.2 Statistics and Linear Algebra\u003cbr\u003e ___1.3.3 Visualization Techniques\u003cbr\u003e ___1.3.4 Machine Learning\u003cbr\u003e ___1.3.5 Deep Learning\u003cbr\u003e 1.4 Main Applications of Text Mining\u003cbr\u003e ___1.4.1 Document Classification \u003cbr\u003e___1.4.2 Document Creation\u003cbr\u003e ___1.4.3 Document Summary\u003cbr\u003e ___1.4.4 Questions and Answers\u003cbr\u003e ___1.4.5 Machine Translation\u003cbr\u003e ___1.4.6 Topic Modeling\u003cbr\u003e 1.5 Practice environment and software used in this book\u003cbr\u003e ___1.5.1 Basic Practice Environment\u003cbr\u003e ___1.5.2 Natural language processing related libraries\u003cbr\u003e ___1.5.3 Machine Learning Related Libraries\u003cbr\u003e ___1.5.4 Deep Learning Related Libraries\u003cbr\u003e\u003cbr\u003e Chapter 2: Text Preprocessing\u003cbr\u003e 2.1 Concept of text preprocessing\u003cbr\u003e ___2.1.1 Why is preprocessing necessary?\u003cbr\u003e ___2.1.2 Preprocessing steps\u003cbr\u003e ___2.1.3 Practice Configuration\u003cbr\u003e 2.2 Tokenization\u003cbr\u003e ___2.2.1 Sentence Tokenization\u003cbr\u003e ___2.2.2 Word Tokenization\u003cbr\u003e ___2.2.3 Tokenization using regular expressions\u003cbr\u003e ___2.2.4 Removing noise and stop words\u003cbr\u003e 2.3 Normalization\u003cbr\u003e ___2.3.1 Stem Extraction\u003cbr\u003e ___2.3.2 Headword Extraction\u003cbr\u003e 2.4 Part of Speech Tagging\u003cbr\u003e ___2.4.1 Understanding Parts of Speech\u003cbr\u003e ___2.4.2 Part-of-speech tagging using NLTK\u003cbr\u003e ___2.4.3 Korean Morphological Analysis and Part-of-Speech Tagging\u003cbr\u003e ___2.4.4 References\u003cbr\u003e\u003cbr\u003e Chapter 3: Graphs and Word Clouds\u003cbr\u003e 3.1 Word Frequency Graph - What are the most frequently used words?\u003cbr\u003e 3.2 See the content at a glance with a word cloud  \u003cbr\u003e3.3 Graphs and word clouds for Korean documents\u003cbr\u003e\u003cbr\u003e \u003cb\u003e[Part 2] BOW-based Text Mining\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e Chapter 4: Count-Based Document Representation\u003cbr\u003e 4.1 Concept of count-based document representation\u003cbr\u003e 4.2 BOW-based count vector generation\u003cbr\u003e 4.3 Creating a count vector with scikit-learn\u003cbr\u003e 4.4 Count vector conversion of Korean text\u003cbr\u003e ___4.4.1 Data Download\u003cbr\u003e 4.5 Using count vectors\u003cbr\u003e 4.6 Let's improve performance with TF-IDF\u003cbr\u003e\u003cbr\u003e Chapter 5: BOW-based Document Classification\u003cbr\u003e 5.1 20 Newsgroup Data Preparation and Feature Extraction\u003cbr\u003e ___5.1.1 Checking and Separating Datasets\u003cbr\u003e ___5.1.2 Count-based feature extraction\u003cbr\u003e 5.2 Understanding Machine Learning and Document Classification Processes\u003cbr\u003e 5.3 Document classification using the Naive Bayes classifier\u003cbr\u003e 5.4 Document Classification Using Logistic Regression Analysis\u003cbr\u003e ___5.4.1 Preventing Overfitting Using Ridge Regression\u003cbr\u003e ___5.4.2 Feature Selection Using Lasso Regression\u003cbr\u003e 5.5 Other document classification methods using decision trees, etc.\u003cbr\u003e 5.6 How to improve performance \u003cbr\u003e5.7 Count-based problems and solutions using N-grams\u003cbr\u003e ___5.7.1 Contextual information not available through statistics\u003cbr\u003e ___5.7.2 Understanding N-grams\u003cbr\u003e ___5.7.3 Document classification using N-grams\u003cbr\u003e 5.8 Classification of Korean documents\u003cbr\u003e ___5.8.1 Predicting movie titles for the next movie review\u003cbr\u003e ___5.8.2 Efforts to improve performance\u003cbr\u003e\u003cbr\u003e Chapter 6: Dimensionality Reduction\u003cbr\u003e 6.1 The Curse of Dimensionality and the Reasons for Dimensionality Reduction\u003cbr\u003e 6.2 Dimensionality Reduction Using PCA\u003cbr\u003e 6.3 Dimensionality Reduction and Semantic Understanding Using LSA\u003cbr\u003e ___6.3.1 Dimensionality Reduction and Performance Using LSA\u003cbr\u003e ___6.3.2 Computing semantic-based document similarity using LSA\u003cbr\u003e ___6.3.3 Analysis of latent topics\u003cbr\u003e ___6.3.4 Analysis of semantic similarity between words\u003cbr\u003e 6.4 Visualization and Dimensionality Reduction Using tSNE\u003cbr\u003e\u003cbr\u003e Chapter 7: Finding Topics with Topic Modeling\u003cbr\u003e 7.1 Understanding Topic Modeling and LDA\u003cbr\u003e ___7.1.1 What is Topic Modeling?\u003cbr\u003e ___7.1.2 Structure of the LDA model\u003cbr\u003e ___7.1.3 Model Evaluation and Determination of Appropriate Number of Topics\u003cbr\u003e 7.2 Topic Modeling with Scikit-Learn\u003cbr\u003e ___7.2.1 Data Preparation  \u003cbr\u003e___7.2.2 Running LDA Topic Modeling\u003cbr\u003e ___7.2.3 Choosing the optimal number of topics\u003cbr\u003e 7.3 Topic Modeling Using Gensim\u003cbr\u003e ___7.3.1 Gensim Usage and Visualization\u003cbr\u003e ___7.3.2 Optimal Value Selection Using Confusion and Topic Cohesion\u003cbr\u003e 7.4 Finding out how topics change over time with topic trends\u003cbr\u003e 7.5 Dynamic Topic Modeling\u003cbr\u003e\u003cbr\u003e Chapter 8: Sentiment Analysis\u003cbr\u003e 8.1 Understanding Sentiment Analysis\u003cbr\u003e ___8.1.1 Vocabulary-based sentiment analysis\u003cbr\u003e ___8.1.2 Machine Learning-Based Sentiment Analysis\u003cbr\u003e 8.2 Sentiment Analysis of Movie Reviews Using a Sentiment Dictionary\u003cbr\u003e ___8.2.1 Preparing NLTK Movie Review Data\u003cbr\u003e ___8.2.2 Sentiment Analysis Using TextBlob\u003cbr\u003e ___8.2.3 Sentiment Analysis Using AFINN\u003cbr\u003e ___8.2.4 Sentiment Analysis Using VADER\u003cbr\u003e 8.3 Machine Learning-Based Sentiment Analysis through Learning\u003cbr\u003e ___8.3.1 Machine Learning-Based Sentiment Analysis of NLTK Movie Reviews\u003cbr\u003e ___8.3.2 Machine Learning-Based Sentiment Analysis of the Next Movie Reviews\u003cbr\u003e 8.4 References\u003cbr\u003e\u003cbr\u003e Chapter 9: Understanding Artificial Neural Networks and Deep Learning\u003cbr\u003e 9.1 Understanding Artificial Neural Networks \u003cbr\u003e___9.1.1 Structure and components of artificial neural networks\u003cbr\u003e ___9.1.2 Learning in Artificial Neural Networks\u003cbr\u003e ___9.1.3 Understanding the Loss Function\u003cbr\u003e ___9.1.4 Gradient descent\u003cbr\u003e 9.2 Understanding Deep Learning\u003cbr\u003e ___9.2.1 What is Deep Learning?\u003cbr\u003e ___9.2.2 Problems with Deep Neural Networks\u003cbr\u003e ___9.2.3 Solutions in Deep Learning\u003cbr\u003e ___9.2.4 Various Deep Learning Algorithms\u003cbr\u003e ___9.2.5 Deep Learning Development and Utilization Environment\u003cbr\u003e\u003cbr\u003e \u003cb\u003e[Part 3] Deep Learning Techniques for Text Mining\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e Chapter 10: RNNs - Document Classification Using Deep Learning\u003cbr\u003e 10.1 Why RNN?\u003cbr\u003e ___10.1.1 Understanding RNNs\u003cbr\u003e ___10.1.2 Why RNNs are Suitable for Document Classification\u003cbr\u003e ___10.1.3 Application of RNN to Document Classification\u003cbr\u003e 10.2 Understanding Word Embeddings\u003cbr\u003e ___10.2.1 What is a word embedding?\u003cbr\u003e ___10.2.2 BOW and Document Embedding\u003cbr\u003e ___10.2.3 Word Embeddings and Deep Learning\u003cbr\u003e 10.3 Document Classification Using RNNs - NLTK Movie Review Sentiment Analysis\u003cbr\u003e ___10.3.1 Preparing Data for Word Embedding\u003cbr\u003e ___10.3.2 Classification using general neural network models other than RNNs\u003cbr\u003e ___10.3.3 RNN-based document classification using document order information \u003cbr\u003e10.4 Performance Improvement Using LSTM, Bi-LSTM, and GRU\u003cbr\u003e\u003cbr\u003e Chapter 11: Understanding Word2Vec, ELMo, and Doc2Vec\u003cbr\u003e 11.1 Word2Vec - A representative word embedding technique\u003cbr\u003e ___11.1.1 Principles of Word2Vec Learning\u003cbr\u003e ___11.1.2 Using Word2Vec - Importing the Trained Model\u003cbr\u003e ___11.1.3 FastText - Applying N-grams to Word Embeddings\u003cbr\u003e 11.2 ELMo - Distinguishing word meanings based on context\u003cbr\u003e ___11.2.1 Problems with Word2Vec\u003cbr\u003e ___11.2.2 ELMo Structure\u003cbr\u003e 11.3 Doc2Vec - Context-Aware Document Embedding\u003cbr\u003e\u003cbr\u003e Chapter 12: CNN - Document Classification Using Image Classification\u003cbr\u003e 12.1 The emergence and operation of CNN\u003cbr\u003e 12.2 Document Classification Using CNN\u003cbr\u003e __12.2.1 Principles of document classification using CNN\u003cbr\u003e __12.2.2 Classifying Movie Reviews in NLTK Using CNN\u003cbr\u003e\u003cbr\u003e Chapter 13: Attention and Transformers\u003cbr\u003e 13.1 Seq2seq: A Deep Learning Technique Starting from Translation\u003cbr\u003e 13.2 Improving Performance Using Attention\u003cbr\u003e 13.3 Self-attention and Transformers\u003cbr\u003e ___13.3.1 Understanding Self-Attention\u003cbr\u003e ___13.3.2 Structure of the transformer\u003cbr\u003e ___13.3.3 Self-Attention Principle of Encoder \u003cbr\u003e___13.3.4 How the decoder works\u003cbr\u003e\u003cbr\u003e Chapter 14: Understanding and Simple Applications of BERT\u003cbr\u003e 14.1 Why are language models important?\u003cbr\u003e 14.2 Theoretical Understanding of Pre-Learning Language Models\u003cbr\u003e 14.3 BERT Structure\u003cbr\u003e 14.4 Pretraining and Fine-tuning Using Language Models\u003cbr\u003e 14.5 Direct Use of Pretrained BERT Models\u003cbr\u003e 14.6 Using tokenizers and models with automatic classes\u003cbr\u003e\u003cbr\u003e Chapter 15: Fine-tuning the BERT Pretrained Model\u003cbr\u003e 15.1 Preprocessing for BERT Training\u003cbr\u003e 15.2 Fine-tuning learning using a Transformer trainer\u003cbr\u003e 15.3 Fine-tuning Learning with PyTorch\u003cbr\u003e\u003cbr\u003e Chapter 16: Using BERT on Korean Documents\u003cbr\u003e 16.1 Fine-tuning the multilingual BERT pretraining model\u003cbr\u003e 16.2 PyTorch Fine-Tuning for the KoBERT Pretrained Model\u003cbr\u003e\u003cbr\u003e Chapter 17: Current Status of Transformer Transformation Models\u003cbr\u003e 17.1 Various tokenizers for transformer variant models\u003cbr\u003e __17.1.1 BPE (Byte-Pair Encoding) Tokenizer\u003cbr\u003e __17.1.2 WordPiece Tokenizer  \u003cbr\u003e__17.1.3 SentencePiece Unigram Tokenizer\u003cbr\u003e __17.2 GPT-based transformer deformation model\u003cbr\u003e __17.2.1 GPT-2\u003cbr\u003e __17.2.2 GPT-3\u003cbr\u003e __17.2.3 ChatGPT\u003cbr\u003e 17.3 BERT-based transformer variant model\u003cbr\u003e __17.3.1 RoBERTa(Robustly Optimized BERT Pretraining Approach)\u003cbr\u003e __17.3.2 ALBERT (A Lite BERT)\u003cbr\u003e __17.3.3 ELECTRA(Efficiently Learning an Encoder that Classifies Token Replacements Accurately)\u003cbr\u003e 17.4 Transformer variant model using both encoder and decoder\u003cbr\u003e __17.4.1 BART (Bidirectional and Auto-Regressive Transformers)\u003cbr\u003e __17.4.2 T5 (Text-to-Text Transfer Transformer)\u003cbr\u003e 17.5 Current Status of Domestic Transformer Modification Models\u003cbr\u003e\u003cbr\u003e Chapter 18: Document Summary Using the Transformer Model\u003cbr\u003e 18.1 Understanding Document Summaries\u003cbr\u003e __18.1.1 Document Summary Performance Metric: ROUGE\u003cbr\u003e __18.1.2 Document Summary Dataset and Transformer Transformation Model\u003cbr\u003e 18.2 Document Summarization Using Pipelines\u003cbr\u003e 18.3 Document Summarization Using the T5 Model and Autoclasses\u003cbr\u003e 18.4 Fine-tuning Learning Using the T5 Model and Trainer  \u003cbr\u003e18.5 Summary of Korean Documents\u003cbr\u003e\u003cbr\u003e Chapter 19: Question Answering Using the Transformer Model\u003cbr\u003e 19.1 Understanding the Question-Answering System\u003cbr\u003e 19.2 Query Answering Using Pipelines\u003cbr\u003e 19.3 Question Answering Using Autoclasses\u003cbr\u003e 19.4 Fine-tuning question-answering learning using a trainer\u003cbr\u003e 19.5 Korean Q\u0026amp;A\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\/TopCate4113\/MidCate007\/411262317(1).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\u003eWhat this book covers\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e - Text preprocessing techniques such as tokenization, stemming, word extraction, stopword processing, and part-of-speech tagging\u003cbr\u003e\u003cbr\u003e - Draw word frequency graphs and word clouds\u003cbr\u003e\u003cbr\u003e - Convert documents into count vectors and TF-IDF vectors, and find similarity between documents.\u003cbr\u003e\u003cbr\u003e - Perform document classification and sentiment analysis using various machine learning\/deep learning techniques.\u003cbr\u003e\u003cbr\u003e - Convert Korean documents using KoNLPy and analyze them with various machine learning algorithms.\u003cbr\u003e\u003cbr\u003e - Dimensionality reduction of document vectors, LDA topic modeling, dynamic topic modeling, and finding and visualizing topic trends.\u003cbr\u003e \u003cbr\u003e- Understanding word embedding techniques such as Word2Vec, ELMo, and Doc2Vec\u003cbr\u003e\u003cbr\u003e - Understanding and utilizing BERT, practicing fine-tuning learning using PyTorch, and practicing using BERT on Korean documents\u003cbr\u003e\u003cbr\u003e - Understanding of pre-trained language models and various transformer variants such as GPT-2, GPT-3, chatGPT, RoBERTa, ALBERT, ELECTRA, BART, and T5.\u003cbr\u003e\u003cbr\u003e - Document summary and question-and-answer practice using transformer models such as T5, KoBART, DistilBERT, and KoELECTRA \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 February 28, 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 424 pages | 175*235*22mm\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 9791158394226\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eISBN10:\u003c\/strong\u003e 1158394225 \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":43893441953834,"sku":"154668","price":40.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0683\/2750\/5962\/files\/5c009f265d40bcc665562891abb98e1a.jpg?v=1765402286","url":"https:\/\/librairie.coreenne.fr\/en\/products\/154668","provider":"LIBRAIRIE COREENNE","version":"1.0","type":"link"}