YouTube Data Analysis Project

Overview

This project involves the analysis of YouTube data, focusing on various aspects such as sentiment analysis, word cloud analysis, emoji analysis, and more. The dataset used for this analysis contains information about trending videos on YouTube and is in the project directory.

Table of Contents

• Installation

• Analysis Steps

  • Sentiment Analysis
  • Wordcloud Analysis
  • Emoji Analysis
  • Exporting Data
  • Most Liked Category
  • Audience Engagement
  • Trending Videos by Channel
  • Relationship between Views & Likes
  • Punctuation Analysis

• Usage

• License

  Installation

  To run this project, you need the following dependencies installed:

• pandas

• seaborn

• numpy

• matplotlib

• emoji

• plotly

  Analysis Steps

  Sentiment Analysis

  Utilized TextBlob to perform sentiment analysis on comments, categorizing them as positive (polarity 1) or negative (polarity -1)

  !pip install textblob
  from textblob import TextBlob

  Wordcloud Analysis

  Generated word clouds to visualize the most frequently occurring words in the dataset. This provides a visually appealing representation of the most frequently occurring words in a dataset and understanding the audience mindset

  Positive Comments

  

  Negative Comments

  

  Emoji Analysis

  Identified and analyzed the usage of emojis in comments to gain insights into audience reactions. Also, it provides insights into audience emotions, enhancing understanding of sentiment and engagement in content and how frequently and common the emojis are used.

  Exporting Data

  Exported the cleaned data into CSV, JSON, and a database for further use.

  To CSV:

  full_df[0:1000].to_csv(r'youtube_sample.csv' , index=False)

  To JSON:

  full_df[0:1000].to_json(r'youtube_sample.json')

  To Sqlite DB:

  from sqlalchemy import create_engine
  engine = create_engine(r'sqlite:///youtube_sample.sqlite')
  full_df.to_sql('Users' , con=engine , if_exists='append')

  Most Liked Category

  Determined the category that received the highest number of likes, providing insights into audience preferences. What category in Youtube has more engagement? and Which has more likes with it? These questions are answered using this boxplot.

  Audience Engagement

  Explored the engagement metrics to understand how viewers interacted with the content and answered using this boxplot

  Relationship between Views & Likes

  Analyzed the correlation between views and likes to uncover patterns in audience behavior is visualised in this regression plot and Heatmap

  Regression plot

  

  HeatMap

  

  Trending Videos by Channel

  Identified channels with the largest number of trending videos, revealing trends in content creators' success, using this BarGraph

  Punctuation Analysis

  Investigated whether the presence of punctuations in titles and tags had any correlation with views, likes, dislikes, and comments using these boxplot

  Relation between Punctutations and Views

  

  Relation between Punctutations and likes

  

  Usage

  Clone the repository:

  git clone https://github.com/your-username/your-repository.git
  cd your-repository

  Latest Version of Anaconda software environment is required!

License

This project is licensed under the MIT License.