Emotion & Sentiment Analysis with/without NLTK using Python

Analyze Emotions ( happy, jealousy, etc ) using NLP Python & Text Mining. Includes twitter sentiment analysis with NLTK

4.70 (57 reviews)
Udemy
platform
English
language
Data Science
category
instructor
Emotion & Sentiment Analysis with/without NLTK using Python
241
students
1 hour
content
Mar 2020
last update
$49.99
regular price

What you will learn

Find out Emotions in a text ( happiness, sadness, jealousy etc. )

Positive and Negative - Sentiment Analysis

Scrap Tweets from Twitter and find out the emotion and sentiment of those tweets

Learn Natural Language Processing Techniques

Cleaning Text and Data for Language Processing ( NLP )

Learn to create graphs using Matplotlib and plot the emotions graph

Learn NLTK for Sentiment Analysis and Natural Language Processing

Description

Welcome to this course on Sentiment and Emotion/Mood analysis using Python

Have you ever thought about how Politicians use Sentiment Analysis? They use to find which topics to talk about in public. A topic can have different sentiments (positive or negative) and varying emotions associated with it. Politicians analyze tweets/internet content to find out these topics and use them to find holes in the opposition.

How Google Maps classifies millions of locations like Restaurants by analyzing the Reviews

How Amazon shows products which evoke Positive Sentiments/Emotions for the buyers

How KFC use it to do Market Research and Competitor Analysis

If you want to know Technology running behind, this is the Sentiment Analysis/Mood Analysis course which is going to use Natural Language Processing ( NLP ) and Text Mining to analyze different moods in a text ( example - Sadness, Excitement, Loneliness etc)

Content

Introduction

Introduction
Installing Python and Pycharm
Cleaning Text for Natural Language Processing (NLP)

Sentiment/Emotion Analysis

Tokenization and Stop Words (NLP)
The Emotion Algorithm
Classifying Emotions
Display Emotions in a Graph using Matplotlib

Sentiment Analysis using NLTK

Twitter Sentiment Analysis
Installing NLTK | Tokenization and Stop words
Positive or Negative Sentiments | NLTK

Reviews

Arindam
July 7, 2021
Very easy to comprehend and follow along. Was possible to understand a topic like sentiment analysis easily.
Nikita
December 7, 2020
Yes ,probably till now its amazing , with a very well explanation and lovely voice of the instructor.
Onubi
November 30, 2020
It's great course for beginners. It does break down all the concepts so anyone can understand Emotion and Sentiment Analysis. He's a great teacher.
Henry
November 27, 2020
Good intro course. Very easy to follow. Module 8 needs to be fixed, due to an apparent change in the Twitter API. However, I feel that this course was quick and to the point, and well worth my time.
Eduardo
July 24, 2020
This course was great, it focuses specifically on what the title says which is great. It was a great match for me, the only reason why I gave it 4 stars instead of 5 is that in some parts I felt that more explanation was required, I was able to do everything but in multiple occasions when implementing a function or modifying a parameter the instructor didn't explain why or how that affected and would have been great if he explained how those things worked so we could have a better understanding. It is not a big problem, you can go and do online research but that takes a reasonable amount of time, even more, when you're a beginner so it would've been great if the instructor did the explanation himself. Besides that, it was great. Straight to the point, if you want to learn how to do emotion & sentiment analysis while understanding some principles of NLP this course is for you, it takes a very small amount of time to learn this incredible tool that has a lot of potential. I highly recommend it.
John
May 31, 2020
Quick and to the point was able to learn and adjust knowledge learned to my real world use as part of recommender system. Usually I go for the longer coarse. So this was pleasant surprise.

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2836782
udemy ID
2/26/2020
course created date
3/31/2020
course indexed date
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