https://github.com/manikantasanjay/youtube_exploratory_data_analytics
This is a Mini Project made using Python Libraries like Numpy, Pandas, Matplotlib etc to perform Data Analytics on US Video and Comments dataset.
https://github.com/manikantasanjay/youtube_exploratory_data_analytics
jupyter-notebook matplotlib numpy pandas plotly python seaborn wordcloud
Last synced: about 2 months ago
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This is a Mini Project made using Python Libraries like Numpy, Pandas, Matplotlib etc to perform Data Analytics on US Video and Comments dataset.
- Host: GitHub
- URL: https://github.com/manikantasanjay/youtube_exploratory_data_analytics
- Owner: ManikantaSanjay
- Created: 2020-10-06T06:41:51.000Z (over 5 years ago)
- Default Branch: main
- Last Pushed: 2020-10-27T07:03:26.000Z (over 5 years ago)
- Last Synced: 2025-04-08T18:51:59.420Z (about 1 year ago)
- Topics: jupyter-notebook, matplotlib, numpy, pandas, plotly, python, seaborn, wordcloud
- Language: Jupyter Notebook
- Homepage:
- Size: 29 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Youtube_Exploratory_Data_Analytics
This is a Mini Project made using Python Libraries to perform Data Analytics on US Youtube Videos and Comments dataset.
### Libraries Used :
i> Pandas
ii> NumPy
iii> Matplotlib
iv> Seaborn
v> Warnings
vi> WordCloud
vii> TextBlob
viii> Emoji
ix> PlotLy
### Description :
This Project mainly contains performing three tasks namely :
##### -> Sentiment Analysis of Youtube Comments :
By making use of the sentiment polarity function of the TextBlob , we classsify the sentences into positive sentences (polarity is equal to 1) and negative sentences (polarity is equal to 0) and we make use of wordcloud module to generate a wordcloud denoting
the most frequency words in the particular sentence.
##### -> Analysing Tags Column :
We try to find the most trending tags on youtube by making use of the wordcloud module and generating a wordcloud depicting the most occuring tags in the videos dataset.
We also try to find the correlation between likes v/s views and the dislikes v/s views and finally finding the correlation between all the three, i.e likes,dislikes and views by using a correlation matrix.
##### -> Analysing Emojis in Comments :
We create a dictionary having the emojis along with its frequency and create a dataframe for the 20 most used emojis in the comments dataset. Now we make use of the Plotly module and plot a graph for the same relation i.e Emoji v/s Frequency of the same.