Sentiment Analysis, Beginner To Expert

Sentiment Analysis

What you'll learn
Sentiment Analysis
Requirements
basic programming in python
Description
In this course, we introduce sentiment analysis and its commercial use. More and more businesses are interested in sentiment analysis to know about the wants of their customers and to overcome the limitations they have identified. It has a massive impact on any business these days, as people consult online reviews before making a purchase. In this course will go from a beiginners to expert approach covering the following,i) Sentiment classificationii) Dictionary based sentiment analysis iii) Aspect based sentiment analysisiv) Deep learning based sentiment classification

Overview

Section 1: Introduction

Lecture 1 Course Outline

Lecture 2 Instructor

Section 2: Theoretical Background

Lecture 3 2.1 Subjective and Objective Content Analysis

Lecture 4 2.2 Sentiment Analysis and Opinion Mining

Lecture 5 2.3 Opinion Mining Quintuple

Lecture 6 2.4 Sources of Data

Lecture 7 2.5 Sentiment Analysis Techniques

Lecture 8 2.6 Outcome of Sentiment Analysis

Lecture 9 2.7 Subdomains and Challenges

Section 3: Text preprocessing

Lecture 10 3.1.1 Normalizing Text

Lecture 11 3.2.1 Filtering Whitespaces and Punctuations and Normalizing Case

Lecture 12 3.2.2 Removing Stopwords

Lecture 13 3.1.2 Regular Expressions to Filter Unwanted Tokens

Lecture 14 3.2.4 Using Reguar Expressions to Filter Domain Specific Stopwords

Lecture 15 3.2.3 Stemming and Lemmatization

Lecture 16 3.2.5 Using Parts-Of-Speech (POS) Tagging to Filter Unwanted Word Types

Lecture 17 3.2.6 Text Segmentation and Tokenization with NLTK

Lecture 18 3.2.7 Data Acquisition from Webpages Through Requests Library

Section 4: Text Representation

Lecture 19 4.1.1 Text Representation Schemes

Lecture 20 4.1.2 Bag of Words (BoW) Approach

Lecture 21 4.1.3 Binary and TF-IDF Representation

Lecture 22 4.2.1 Representing One Doc Corpus

Lecture 23 4.2.2 Representing Multi Doc Corpus

Lecture 24 4.2.3 Tuning Parameters

Lecture 25 4.2.4 TfIdf Vectorizer

Lecture 26 4.2.5 Structuring Dummy Dataset

Lecture 27 4.2.6 Structuring Dataset from UCI Repository

Section 5: Machine Learning Theory

Lecture 28 Inductive Learning (learning from Data)

Lecture 29 The Learning in Machine Learning

Lecture 30 Multi-disciplinary Nature of ML

Lecture 31 Types of ML Techniques

Lecture 32 Pattern Recognition

Lecture 33 Machine Learning Project Pipeline

Section 6: Sentiment Classification

Lecture 34 5.1.1 Machine Learning Overview

Lecture 35 5.1.2 Classifiers

Lecture 36 5.1.3 Simple Classification Algorithms

Lecture 37 5.2.1 Implementation of Document Classification

Lecture 38 5.2.2 Tuning Parameters for Classifiers

Lecture 39 5.2.3 Classification with UCI Repository Dataset

Lecture 40 5.2.4 Sentiment Classification

Section 7: Validation and Evaluation

Lecture 41 6.1.1 Validation and Evaluation

Lecture 42 6.1.2 Cross Validation

Lecture 43 6.2.1 Implementation of Dataset Validation

Lecture 44 6.2.2 Implementing K-Fold Cross Validation

Lecture 45 6.2.3 Implementing Leave-One-Out Validation

Lecture 46 6.1.3 Accuracy, Precision, Recall, F1-score

Lecture 47 6.2.4 Implementing Precision, Recall and F1-score

Lecture 48 6.1.4 Confusion Matrix

Lecture 49 6.2.5 Implementing Confusion Matrix

Lecture 50 6.2.6 Putting It All Together

Section 8: Lexicon based Sentiment Analysis

Lecture 51 7.1.1 WordNet Dictionary

Lecture 52 7.2.1 Implementation of SA with WordNet

Lecture 53 7.2.2 Implementation of SA with SentiWordNet

Lecture 54 7.1.2 TextBlob Library

Lecture 55 7.2.3 Implementation of TextBlob

Lecture 56 7.2.4 Implementation of SA with VADER

Section 9: Aspect-Based Sentiment Analysis (ABSA)

Lecture 57 8.1.1 Topic Modeling Introduction

Lecture 58 8.1.2 Working of Topic Models

Lecture 59 8.1.3 Tuning Hyperparameters for Topic Models

Lecture 60 8.2.1 Implementing Topic Models

Lecture 61 8.2.2 Impelementing Topic Models with UCI repository Dataset

Lecture 62 8.2.3 Implementing Topic Modeling with Hyperparameters Tunining

Lecture 63 8.2.4 Implementing Online Topic Modeling

Lecture 64 8.2.5 ABSA with Topic Modeling and Lexicon based SA

Section 10: Deep Sentiment Classification

Lecture 65 9.1.1 Neural Networks and Deep Neural Networks

Lecture 66 9.1.2 Propagation Function and Hyperparameters

Lecture 67 9.1.3 Backpropagation and Cost Function

Lecture 68 9.2.1 Implementation of Neural Networks

Lecture 69 9.2.2 Implemention of Model Training and Input Shape

Lecture 70 9.2.3 Model Results and Evaluation

Lecture 71 9.2.4 Implementation of NN with IMDB Dataset

Lecture 72 9.1.4 Vanishing Gradient Problem in DNN

Lecture 73 9.2.5 Implementing Deep NN

Lecture 74 9.2.6 Implementing Dropout and RMSPROP Optimizer

Lecture 75 9.1.5 Convolutional and Pooling Layers

Lecture 76 9.2.7 CNN Implementation

Section 11: Deep Sequence Models

Lecture 77 10.1.1 Sequence Models

Lecture 78 10.1.2 Integer Encoding of Text

Lecture 79 10.2.1 Implementing Text to Padded Sequences

beginner python programmers curious about data science and text processing in particular