Sentimental Analysis on COVID-19 Tweets using python

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In this Guided Project, you will:

Learn how to Preprocess text data for Sentimental Analysis

Learn how to Label text data with positive, negative and neutral sentiments

Learn to visualize the result of sentiment Analysis

1 hour
Intermediate
No download needed
Split-screen video
English
Desktop only

By the end of this project you will learn how to preprocess your text data for sentimental analysis. So in this project we are going to use a Dataset consisting of data related to the tweets from the 24th of July, 2020 to the 30th of August 2020 with COVID19 hashtags. We are going to use python to apply sentimental analysis on the tweets to see people's reactions to the pandemic during the mentioned period. We are going to label the tweets as Positive, Negative, and neutral. After that, we are going to visualize the result to see the people's reactions on Twitter. Note: This project works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Skills you will develop

  • lambda

  • Python Programming

  • Plotly

  • Seaborn

  • Sentimental Analysis

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  1. importing our dataset

  2. preprocess and prepare our text data for Sentimental Analysis

  3. visualizing most common words using a bar chart.

  4. using NLTK module to produce Polarity scores for each tweet

  5. visualizing the result of our analysis using line chart

How Guided Projects work

Your workspace is a cloud desktop right in your browser, no download required

In a split-screen video, your instructor guides you step-by-step

Reviews

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You'll learn by doing through completing tasks in a split-screen environment directly in your browser. On the left side of the screen, you'll complete the task in your workspace. On the right side of the screen, you'll watch an instructor walk you through the project, step-by-step.