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
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.
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
importing our dataset
preprocess and prepare our text data for Sentimental Analysis
visualizing most common words using a bar chart.
using NLTK module to produce Polarity scores for each tweet
visualizing the result of our analysis using line chart
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
Excellent overview of the topic of analyzing twitter data for creating a basic sentiment dataset and creation of related visualizations using Seaborn, and Plotly Express.
It was a good focused exercise on solving the problem statement
Great idea! Loved to follow you along on this project!
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