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[ FreeCourseWeb com ] PluralSight - Building Sentiment Analysis Systems in Python
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1-Course Overview
1 -Course Overview.mp4
2-Identifying Applications of Sentiment Analysis
2 -Introducing Sentiment Analysis.mp4
3 -Two Case Studies.mp4
4 -Polarity Detection for Sentiment Analysis.mp4
5 -Setting up a Binary Classification Problem.mp4
6 -Rule-based and ML-based Binary Classifiers.mp4
3-Solving Sentiment Analysis with a Rule-based Approach
10 -Making It (Slightly More) Real.mp4
11 -Building Is Hard, Using Is Easy.mp4
7 -Starting Simple, Starting Simplistic.mp4
8 -Limitations of a Simplistic Approach.mp4
9 -A More Realistic Rule-based Algorithm.mp4
4-Implementing? Sentiment Analysis with a Rule-based Approach
12 -Introducing VADER.mp4
13 -Punctuation, Negation, Emphasis, and Contrast.mp4
14 -Classifying Movie Reviews with VADER.mp4
15 -Introducing Sentiwordnet.mp4
16 -Classifying Movie Reviews with Sentiwordnet.mp4
5-Solving Sentiment Analysis with an ML Based Approach
17 -Exploring ML-based Approaches.mp4
18 -The Intuition Behind Bayes Theorem.mp4
19 -Naive Bayes for Classification Problems.mp4
20 -Applying Bayes Theorem.mp4
21 -Support Vector Machines.mp4
6-Implementing Sentiment Analysis with an ML Based Approach
22 -The Importance of Feature Extraction.mp4
23 -An Outline of Implementing Naive Bayes.mp4
24 -Data Transformation for nltk.mp4
25 -Python Implementation of Naive Bayes.mp4
26 -Comparing VADER, Sentiwordnet, and Naive Bayes.mp4
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building-sentiment-analysis-systems-python.zip
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