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Machine Learning Algorithms

You're reading from   Machine Learning Algorithms A reference guide to popular algorithms for data science and machine learning

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781785889622
Length 360 pages
Edition 1st Edition
Languages
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Author (1):
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Giuseppe Bonaccorso Giuseppe Bonaccorso
Author Profile Icon Giuseppe Bonaccorso
Giuseppe Bonaccorso
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Table of Contents (16) Chapters Close

Preface 1. A Gentle Introduction to Machine Learning FREE CHAPTER 2. Important Elements in Machine Learning 3. Feature Selection and Feature Engineering 4. Linear Regression 5. Logistic Regression 6. Naive Bayes 7. Support Vector Machines 8. Decision Trees and Ensemble Learning 9. Clustering Fundamentals 10. Hierarchical Clustering 11. Introduction to Recommendation Systems 12. Introduction to Natural Language Processing 13. Topic Modeling and Sentiment Analysis in NLP 14. A Brief Introduction to Deep Learning and TensorFlow 15. Creating a Machine Learning Architecture

Sentiment analysis

One the most widespread applications of NLP is sentiment analysis of short texts (tweets, posts, comments, reviews, and so on). From a marketing viewpoint, it's very important to understand the semantics of these pieces of information in terms of the sentiment expressed. As you can understand, this task can be very easy when the comment is precise and contains only a set of positive/negative words, but it becomes more complex when in the same sentence there are different propositions that can conflict with each other. For example, I loved that hotel. It was a wonderful experience is clearly a positive comment, while The hotel is good, however, the restaurant was bad and, even if the waiters were kind, I had to fight with a receptionist to have another pillow. In this case, the situation is more difficult to manage, because there are both...

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