the-youtube-scraper

Download YouTube video description and video comments without using the YouTube API.

Setup

(1) You do not need to any API Credentials to use this.

(2) The final output will be stored in a JSON file.

Clone and use the Python script

$ git clone https://github.com/hridaydutta123/the-youtube-scraper.git
$ cd the-youtube-scraper
$ sudo pip install -r requirements.txt

Star History

Usage

Finally you can run the script by entering one YouTube video ID and output directory path at the command line:

1. Download video metadata and video comments from a single YouTube video

$ python3 main.py --video_id <enter-youtube-video-id> --out_dir <enter-output-directory-path>

2. Download video metadata and video comments from a list of YouTube videos parallely (x num of cores times faster)

$ python3 main_parallel.py --input_file <enter-file-name-with-list-of-video-ids(one-video-id-per-line)> --out_dir <enter-output-directory-path>

Output Format

{
  "comment_response": [
    {
      "author": "Uday Simha",
      "text": "Vanishing gradient is due to large sequences or dude to more nodes in a layer or due to deep networks?",
      "ensure_ascii": false,
      "cid": "UgyxmBEJrQS0To2Fo5R4AaABAg",
      "time": "1 week ago"
    },
    {
      "author": "Sandeep Kumar",
      "text": "For a 200 channel(bands) satellite image of dimension 145x145 has 16 \nclasses.I have a groundtruth of dimension 145x145 (containing values \nfrom 1 to 16 that represent 16 different classes).I need to perform LSTM\n on a small patch of image (say of dimension 5x5x200) because \nneighboring pixels are similar.Can you tell  me how to give the data to @t mail id is sandeep.Ladi@@t",
      "ensure_ascii": false,
      "cid": "Ugy_5NTrbFIPLzNOtYt4AaABAg",
      "time": "1 month ago"
    },
    {
      "author": "Ömer Yalçın",
      "text": "Hi that is great video , thanks for sharing. You can use \"keras.layers.CuDNNLSTM()\" for uping speed.",
      "ensure_ascii": false,
      "cid": "Ugyc_biVWUJooIhFZWl4AaABAg",
      "time": "3 months ago (edited)"
    },
    {
      "author": "Saravana Kumar",
      "text": "Are you British",
      "ensure_ascii": false,
      "cid": "UgyOC9tQ3SBMfN4G_7F4AaABAg",
      "time": "4 months ago"
    },
    {
      "author": "Machine Learning at Microsoft",
      "text": "Yes!",
      "ensure_ascii": false,
      "cid": "UgyOC9tQ3SBMfN4G_7F4AaABAg.8mwZjbe5R6C8mzrRtq8hwX",
      "time": "4 months ago"
    },
    {
      "author": "Dinesh Mane",
      "text": "Fantastic session Tim, and content is really good. \nYou have explained return_sequence = True parameter with two examples which helped me to understand clearly. \nThanks for sharing the video :)",
      "ensure_ascii": false,
      "cid": "UgyQ1m5GjlpKsKfMg954AaABAg",
      "time": "7 months ago"
    },
    {
      "author": "Tomás Emilio Silva Ebensperger",
      "text": "awesome video sir. i will read the book.",
      "ensure_ascii": false,
      "cid": "UgxyoZk56yUpqiZmxhF4AaABAg",
      "time": "7 months ago"
    }
  ],
  "video_description": {
    "thumbnail_url": "https://i.ytimg.com/vi/ZmCzrPVzDQI/maxresdefault.jpg",
    "uploader": {
      "thumbnail_url": "https://yt3.ggpht.com/a-/AAuE7mBlSX-JPA4rXmz0LhppdTWsCiD8rM9ZjSY9Fg=s48-c-k-c0xffffffff-no-rj-mo",
      "channel_id": "UCXvHuBMbgJw67i5vrMBBobA",
      "is_verified": false,
      "name": "Machine Learning at Microsoft"
    },
    "is_family_friendly": true,
    "genre": "Science & Technology",
    "duration": "PT57M36S",
    "description": "Tim Scarfe takes you on a whirlwind tour of sequence modelling in deep learning using Keras! \n\n• Intro \n•  Outline 2:03\n•  What is a neural network 2:38\n• Concepts of deep learning 3:32\n• What is a sequence? 8:34\n• What is sequence processing? 9:28\n• Tokenization 10:35\n• word vectors vs word embeddings 12:06\n• More about word embeddings 13:26\n• Recurrent neural networks (RNNs) 15:26\n• LSTMs 17:04\n• GRUs vs LSTMs 18:31\n• Bi-directional RNNs 19:28\n• 1d CNNs and tour of convolutional filtering in MATLAB 20:22\n• Stacking RNNs+CNNs 25:42\n• Universal machine learning process 25:56\n• Demo-1 hot encoding 29:17\n• Demo-Defining RNNs in Keras 31:17\n• Demo-IMDB in Keras 32:30\n• Performance/scoring/eval of deep learning models 35:40\n• Question on material and sigmoid activation 38:39\n• Temperature forecasting problem (cover GRU, LSTM, regularisation, bidirectional, stacking) 41:55\n• 1D CNNs 49.49\n• Questions 52:00\n\nSlides; https://github.com/ecsplendid/deep-le...\n\nMake sure you buy yourself a copy of Francois Chollet's book https://www.manning.com/books/deep-le...",
    "title": "Sequence Modelling and NLP With Deep Learning (Keras)",
    "upload_date": "2018-03-04",
    "is_paid": false,
    "id": "ZmCzrPVzDQI",
    "is_unlisted": false,
    "statistics": {
      "dislikes": 2,
      "views": 5118,
      "likes": 105
    }
  }
}

References

[1] https://github.com/egbertbouman/youtube-comment-downloader

[2] https://github.com/faheel/youtube-scraper-api