Going Big (Data) on YouTube

The main aim of this project is to test the following hypotheses.
A. “A positive sentiment title in a YouTube video will affect views”
B. “Having humans in the thumbnail of a YouTube video will affect views”

Project Resources

1. Project Report
2. Project Slides
3. Files Used

Project Directory Structure

├── data
│   ├── socialblade_df.csv
│   ├── channel_df.csv
│   ├── videos_df.csv
│   ├── sentiment_analysis_df.csv
│   ├── pos_tagging_df.csv
│   ├── object_detection_df.csv
│   └── features_df.csv
├── images
│   └── ...
├── models
│   └── object_detection
│       ├── coco.names
│       ├── yolov3.cfg
│       └── yolov3.weights
├── extract_channel.ipnyb
├── sentiment_analysis.ipnyb
├── pos_tagging.ipnyb
├── object_detection.ipnyb
├── data_exploration.ipnyb
├── feature_selection.ipnyb
├── model_polynomial_regression.ipnyb
├── model_random_forest.ipnyb
├── model_gradient_boosting.ipnyb
└── model_neural_networks.ipnyb

1. Data Collection

In this section, we will be extracting information of YouTube channels of interest. All data used in this project can be downloaded from Link (3) under the Links section above.

YouTube Channels selected

The list of 160 channels the team have selected by hand is saved in data/socialblade_df.csv which includes 3 columns: channel's name, channel id and category.

Channel and Video Data

Run extract_channel.ipnyb to collect channel and video information for channels listed in data/socialblade_df.csv. The output is saved as data/channel_df.csv and data/videos_df.csv.

As YouTube API as a limit, we are only able to retrieve the latest 20,000 videos from each channel. We have also chosen to retrieve videos published from 2015 onwards.

Aggregated Video Data

Run aggregate_video.ipnyb to obtain aggregated metrics for all videos listed in data/videos_df.csv. The output is saved as data/agg_videos_df.csv.

2. Feature Extraction

Sentiment Analysis

Run sentiment_analysis.ipnyb for all videos listed in data/videos_df.csv. The output is saved as data/sentiment_analysis_df.csv.

Part-of-Speech (PoS) Tagging

Run pos_tagging.ipnyb for all videos listed in data/videos_df.csv. The output is saved as data/pos_tagging_df.csv.

Object Detection

Run object_detection.ipnyb to save all thumbnail images of videos in data/videos_df.csv into the images/ folder. Then, object detection is executed for all thumbnail images downloaded. The output is saved as data/object_detection_df.csv. Alternatively, code for performing this step on Amazon Web Service (AWS) S3 and SageMaker has been provided to facilitate efficiency.

3. Data Exploration

Run data_exploration.ipnyb to see visualisations generated from the data collected.

4. Feature Selection

Run feature_selection.ipnyb to select features used for model building in the next stage, the finalised files are saved as data/features_df.csv.

5. Model Building and Evaluation

For all 4 models built, they were split into different files as model_[model name].ipnyb. Run the respective files to build the models and the results can be seen in the table below.

Model	RMSE	MSE	MAE
Polynomial Regression	24,486,700	599,598,500,178,391	2,410,707
Random Forest	29,491,373	869,741,069,564,456	2,369,201
Gradient Boosting	29,839,713	890,408,446,899,621	2,363,009
Artificial Neural Network	20,330,611	413,333,730,000,000	2,338,276

Contributors

• Chua Kai Bing - @kaibinggg
• Tan Zen Wei - @zenweiii
• Goh Jia Yi - @gohjiayi
• Nguyen Ngoc Linh Chi - @nguyenngoclinhchi