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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.
Instructors: AI Monks
Validity Period: 365 days
40% Cashback as Credits
Why this course?
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?
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:
In case of any query, please reach out to us at email@example.com
|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)
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