This is a project presentation of YouTube Sentiment analysis

1. SENTIMENT ANALYSIS OF YOUTUBE’S COMMENTS
• Project by
Vishal Patil

2. PROJECT DESCRIPTION
• Project mainly focused on analyzing random 1650 YouTube videos for
their comments
• Performing sentiment analysis on positive and negative comments to
determine the most popular words used in those comments
• Determining the most popular emojis in comments section

3. PACKAGES USED FOR ANALYSIS
• The analysis was done using following packages in python:
1. Pandas – For data manipulation and exploratory data analysis (EDA)
2. Plotly – For data visualization
3. Regular expression – For cleaning the noise in the data
4. TextBlob – For sentiment analysis (Determining the polarity of comments)
5. WordCloud – For determining the most frequent word in comments
6. Emoji – For separating the emojis from the comments
7. Plotly – To determine the most popular emojis in comments

4. FLOW CHART
DATALOADING
DETECT
TARGET
VARIABLE
DATA CLEANING
HANDLING
MISSING
VALUES
SENTIMENT
ANALYSIS
DATA
VISUALIZATION
DATA
INSIGHTS
EDA PROCESS FOLLOWED

5. DATA LOADING
• The data was collected from Kaggle
• The pandas library was used to load the data and it was stored
in variable called data

6. SENTIMENT ANALYSIS
• The comments column comment_text was used from data
• This column was divided into two comments based on the polarity of comments
1. Positive comments – Comments with +1 polarity
2. Negative comments – Comments with -1 polarity

7. DATA VISUALIZATION 1
• Most popular positive comments

8. DATA VISUALIZATION 2
• Most popular negative comments

9. EXTRACTING THE EMOJIS FROM THE COMMENTS
• The emoji library aid us in extracting the emojis from the comments

10. DATA VISUALIZATION 3
• Most popular emojis

11. THANK YOU!
• For detail code please contact : 8237910124