Sentiment Analysis

Understanding the sentiment of your spouse may be difficult, but the rest of the world – not so much. Learn all about predicting and assessing the Sentiment of the users.

Language: English

Instructors: AI Monks

Validity Period: 365 days

₹499 50.1% OFF

₹249

40% Cashback as Credits

 

Why this course?

Description

Do you know that The Obama administration used sentiment analysis to gauge public opinion on policy announcements and campaign messages ahead of the 2012 presidential election? This was the first time sentiment analysis was used to on such a large scale. Since then it has become part of every impact political campaign and marketing campaign. It is the most crucial part of crisis management. Brand monitoring and brand perception have become synonymous with sentiment analysis.

With the penetration of high-speed internet to the masses, people are expressing themselves on social media, blogs, vlogs, etc. It has become of utmost importance for companies to gauge their customer’s sentiment. Sentiment Analysis is one of the most popular applications of text analytics and Natural Language Processing (NLP). This is also one of the most popular problems to be asked in the data analytics and data scientist job interviews.

In this course, we will assess sentiment by two methods. One where we will have historic data and we will try to predict the user’s sentiment based on this historic data. Here, we will use Support Vector Machine to predict the sentiment of the customer. This will be an example of supervised machine learning.

Secondly, we will try to assess the sentiment of the user based pre-defined rules. This will be an example of unsupervised machine learning. In this, we will take data from Twitter and assess the sentiment of these tweets.

Who should opt for this course?

  • Anyone who does not want to go deep into mathematics of the machine learning algorithm but wants to understand basic concepts and real-world application of Sentiment Analysis
  • Anyone who wishes to develop a practical understanding of Support Vector Machine, Natural Language Processing and Basics of text analytics
  • Any student or professional preparing for a job interview and wish to understand and perform sentiment analysis using machine learning techniques
  • Any professional who wants to implement sentiment analysis and understand user’s behavior
  • Any student willing to work on academic projects using Support Vector Machine

Comprehensive Course Coverage

This course covers the implementation of Support Vector Machine and Vader for a Sentiment Analysis problem, in great depth. It covers the following aspects:

  • Basics of the sentiment analysis
  • Real-world applications of sentiment analysis
  • Introduction to machine learning techniques can be used for sentiment analysis
  • Basics of Natural Language Processing and terminologies associated with it
  • Basics of Support Vector Machine and terminologies associated with it
  • Steps to extract data from Twitter and clean the text data to make it ready for analysis
  • In-depth understanding of the fundamentals of Support Vector Machine
  • Sentiment Analysis using Support Vector Machine and Vader model in Python

In case of any query, please reach out to us at info@aimonks.com

Course Curriculum

Introduction to Sentiment Analysis
Introduction to Sentiment Analysis (2 pages)
Basics of Natural Language Processing (NLP) (2 pages)
Machine Learning Techniques for Sentiment Analysis (3 pages)
Application of Sentiment Analysis (2 pages)
Supervised Sentiment Analysis using SVM
Brief Introduction of SVM Model (6 pages)
Process and Steps to Clean the Text Data (4 pages)
Data Preparation (5 pages)
Training and Evaluating the Model (4 pages)
Creating a Twitter App for Extracting Twitter Data
Creating a Twitter App and Integrating R with Twitter API (4 pages)
Unsupervised Sentiment Analysis using Twitter Data
Extracting data using Python with Twitter API (1 pages)
Extracting data using Python with Twitter API (3 pages)
Building the Model - Defining Tweet-polarities with VADER (3 pages)
Analyzing the results (4 pages)

How to Use

After successful purchase, this item would be added to your courses.You can access your courses in the following ways :

  • From the computer, you can access your courses after successful login
  • For other devices, you can access your library using this web app through browser of your device.

Reviews

Powered By Graphy