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Отзывы учащихся о курсе Sentimental Analysis on COVID-19 Tweets using python от партнера Coursera Project Network

4.6
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Оценки: 35
Рецензии: 8

О курсе

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....

Лучшие рецензии

YH
12 нояб. 2020 г.

It was a good focused exercise on solving the problem statement

SR
2 дек. 2020 г.

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

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1–8 из 8 отзывов о курсе Sentimental Analysis on COVID-19 Tweets using python

автор: Yamini H

13 нояб. 2020 г.

It was a good focused exercise on solving the problem statement

автор: Sara R R

3 дек. 2020 г.

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

автор: Serhii D

10 янв. 2021 г.

Good starting project for data visualizing field.

автор: Meer M A

5 февр. 2021 г.

Great and helpful.

автор: Aniruddh M

19 янв. 2021 г.

Good one!

автор: Robert B

25 февр. 2021 г.

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.

автор: Şifa T

22 июня 2021 г.

It was good and efficient to work on it. thanks

автор: Chung M

7 мар. 2021 г.

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!