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Learn steps to build a successful sentiment analysis model
The web is full of apps that are driven by data. All the e-commerce apps and websites are based on data in the complete sense. There is database behind a web front end and middleware that talks to a number of other databases and data services. But the mere use of data is not what comprises of data science. A data application gets its value from data and in the process creates value for itself. This means that data science enables the creation of products that are based on data. This course includes real-world projects on Sentiment analysis which are used by data scientists or people who inspire to be the data scientist.
Every company on the face of the earth wants to know what its customers feel about its products and services and sentiment analysis is the easiest way and most accurate way of finding out the answer to this question. By learning to do sentiment analysis, you would be making yourself invaluable to any company, especially those which are interested in quality assurance of their products and those working with business intelligence.
Sentiment analysis refers to the use of natural language processing(NLP), text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment analysis is widely applied to voice of the customer materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from marketing to customer service to clinical medicine.
The tutorials will include the following;
1-Explaining what is sentiment analysis and why we need it
2- A Brief explanation on the steps that we will take to build sentiment analysis models
3-Calling the libraries and explaining the libraries used for sentiment analysis
4-Coding the steps to build a successful sentiment analysis model