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Sentiment Analysis with Python

Learn steps to build a successful sentiment analysis model

3.85 / 5.0
12032 students1 hours 10 minutes

Created by Exam Turf, offered on Udemy

bestcourses score™

Student feedback

5.6/10

To make sure that we score courses properly, we pay a lot of attention to the reviews students leave on courses and how many students are taking a course in the first place. This course has a total of 12032 students which left 34 reviews at an average rating of 3.85, which is average.

Course length

8.4/10

We analyze course length to see if courses cover all important aspects of a topic, taking into account how long the course is compared to the category average. This course has a length of 1 hours 10 minutes, which is pretty short. This might not be a bad thing, but we've found that longer courses are often more detailed & comprehensive. The average course length for this entire category is 7 hours 54 minutes.

Overall score

6.1/10

This course currently has a bestcourses score of 6.1/10, which makes it an average course. Overall, there are probably better courses available for this topic on our platform.

Description

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



What you will learn

  • Learn about sentiment analysis and why we need it
  • Brief explanation on the steps that we will take to build sentiment analysis models
  • Calling the libraries and explaining the libraries used for sentiment analysis
  • Coding the steps to build a successful sentiment analysis model

Requirements

  • No prior knowledge of machine learning required
  • Basic knowledge of Python programming language will be an added advantage
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Frequently asked questions

  • Price: $94.99
  • Platform: Udemy
  • Language: English
  • 1 hours 10 minutes
Sentiment Analysis with Python thumbnail

bestcourses score: 6.1/10

There might be better courses available for this topic.