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Voltar para Sentimental Analysis on COVID-19 Tweets using python

Comentários e feedback de alunos de Sentimental Analysis on COVID-19 Tweets using python da instituição Coursera Project Network

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8 avaliações

Sobre o curso

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....

Melhores avaliações

YH
12 de Nov de 2020

It was a good focused exercise on solving the problem statement

SR
2 de Dez de 2020

Great idea! Loved to follow you along on this project!

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1 — 8 de 8 Avaliações para o Sentimental Analysis on COVID-19 Tweets using python

por Yamini H

13 de Nov de 2020

It was a good focused exercise on solving the problem statement

por Sara R R

3 de Dez de 2020

Great idea! Loved to follow you along on this project!

por Serhii D

10 de Jan de 2021

Good starting project for data visualizing field.

por Meer M A

5 de Fev de 2021

Great and helpful.

por Aniruddh M

19 de Jan de 2021

Good one!

por Robert B

25 de Fev de 2021

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.

por Şifa T

22 de Jun de 2021

It was good and efficient to work on it. thanks

por Chung M

7 de Mar de 2021

I love the step-by-step approach to conduct sentiment analysis in this course. The instructor guided me through all the code and therefore let me experiment on my own. However, I would appreciate if he could give more explanation on the theory (polarity score) and code concept (''join). Apart from the sentiment result of 'positive', 'negative' and 'neutral', everything else is the same as the Twitter Sentiment Analysis beginner course. I hope it would offer at least the concept of polarity score. Thank you for offering the course anyway!