Machine Learning for Emotion Analysis in Python: Build AI-powered tools for analyzing emotion using natural language processing and machine learning (Paperback)

Kickstart your emotion analysis journey with this step-by-step guide to data science success

Key Features

• Discover the inner workings of the end-to-end emotional analysis workflow
• Explore the use of various ML models to derive meaningful insights from data
• Hone your craft by building and tweaking complex emotion analysis models with practical projects
• Purchase of the print or Kindle book includes a free PDF eBook

Book Description

Artificial intelligence and machine learning are the technologies of the future, and this is the perfect time to tap into their potential and add value to your business. Machine Learning for Emotion Analysis in Python helps you employ these cutting-edge technologies in your customer feedback system and in turn grow your business exponentially.

With this book, you’ll take your foundational data science skills and grow them in the exciting realm of emotion analysis. By following a practical approach, you’ll turn customer feedback into meaningful insights assisting you in making smart and data-driven business decisions.

The book will help you understand how to preprocess data, build a serviceable dataset, and ensure top-notch data quality. Once you’re set up for success, you’ll explore complex ML techniques, uncovering the concepts of deep neural networks, support vector machines, conditional probabilities, and more. Finally, you’ll acquire practical knowledge using in-depth use cases showing how the experimental results can be transformed into real-life examples and how emotion mining can help track short- and long-term changes in public opinion.

By the end of this book, you’ll be well-equipped to use emotion mining and analysis to drive business decisions.

What you will learn

• Distinguish between sentiment analysis and emotion analysis
• Master data preprocessing and ensure high-quality input
• Expand the use of data sources through data transformation
• Design models that employ cutting-edge deep learning techniques
• Discover how to tune your models’ hyperparameters
• Explore the use of naive Bayes, SVMs, DNNs, and transformers for advanced use cases
• Practice your newly acquired skills by working on real-world scenarios

Who this book is for

This book is for data scientists and Python developers looking to gain insights into the customer feedback for their product, company, brand, governorship, and more. Basic knowledge of machine learning and Python programming is a must.

開始你的情感分析之旅，這本逐步指南將引導你成功踏上資料科學之路。

主要特點：
- 探索從頭到尾的情感分析工作流程的內部運作
- 使用各種機器學習模型從數據中獲取有意義的洞察力
- 通過實際項目建立和調整複雜的情感分析模型，提升你的技能
- 購買印刷版或Kindle電子書，即可獲得免費的PDF電子書

書籍描述：
人工智慧和機器學習是未來的技術，現在正是利用它們的潛力為你的業務增值的絕佳時機。《Python情感分析機器學習》將幫助你在客戶反饋系統中應用這些尖端技術，從而使你的業務成長迅猛。

通過這本書，你將把你的基礎數據科學技能應用到令人興奮的情感分析領域。通過實踐方法，你將把客戶反饋轉化為有意義的洞察力，幫助你做出明智且數據驅動的業務決策。

本書將幫助你了解如何預處理數據、建立可用的數據集並確保數據質量。一旦你準備好成功，你將探索複雜的機器學習技術，揭示深度神經網絡、支持向量機、條件概率等概念。最後，你將獲得實際知識，通過深入的使用案例將實驗結果轉化為現實生活中的例子，並了解情感挖掘如何幫助追踪公眾意見的短期和長期變化。

通過閱讀本書，你將具備使用情感挖掘和分析來推動業務決策的能力。

你將學到什麼：
- 區分情感分析和情感分析
- 掌握數據預處理，確保高質量的輸入
- 通過數據轉換擴展數據來源的使用
- 設計運用尖端深度學習技術的模型
- 發現如何調整模型的超參數
- 探索使用朴素貝葉斯、支持向量機、深度神經網絡和轉換器進行高級應用案例
- 通過處理真實場景來練習你新獲得的技能

本書適合對於產品、公司、品牌、政府等的客戶反饋感興趣的數據科學家和Python開發人員。需要具備機器學習和Python編程的基礎知識。

1. Foundations
2. Building and Using a Dataset
3. Labelling Data
4. Preprocessing - Stemming, Tagging, and Parsing
5. Sentiment Lexicons and Vector-Space Models
6. Naïve Bayes
7. Support Vector Machines
8. Neural Networks and Deep Neural Networks
9. Exploring Transformers
10. Multiclassifiers
11. Case Study - The Qatar Blockade

1. 基礎知識
2. 建立和使用資料集
3. 標記資料
4. 預處理 - 詞幹提取、標記和解析
5. 情感詞庫和向量空間模型
6. 朴素貝葉斯
7. 支持向量機
8. 神經網絡和深度神經網絡
9. 探索轉換器
10. 多分類器
11. 案例研究 - 卡塔爾封鎖