Use Google cloud's Natural Language Processing API to automatically analyze the webpage from articles saved in your Pocket list, derive tags/keywords based on the content of the page, and add tags to the articles in Pocket list for free.
Pocket has suggested tags service for their paid premium plans. You can find more about it here. This still requires manual work of adding the tags to each article one-by-one. This package automates all of it for free.
- Uses Python wrapper for Pocket API to retrieve articles in the
My List - Uses Beautiful Soup to scrape webpages
- Uses Google Cloud's Natural Language Processing API to generate list of categories and entities from the content of the webpage
- Uses Pocket API to add tags to articles in your
My List
$ pip install pocket-tagger$ pip install git+https://github.com/sanghviharshit/pocket-taggerThis package relies on Google cloud natural language processing API, which requires billing enabled on your project. You can find the quickstart instructions here Options:
- Create a service account and download the credentials file - https://cloud.google.com/video-intelligence/docs/common/auth
tagger = PocketTagger(gcloud_credentials_file="gcloud_credentials_file.json")- or Configure gloud locally - https://cloud.google.com/sdk/gcloud/reference/init
tagger = PocketTagger()To fetch the articles list and add tags, you need a developer key from here
Create a new Application with modify and retrieve permissions. Save the Consumer Key and Access Token.
tagger = PocketTagger(consumer_key='your-consumer-key',
access_token='your-access-token')# Initialize PocketTagger with GCloud and Pocket API Credentials
tagger = PocketTagger(gcloud_credentials_file="gcloud_credentials_file.json",
consumer_key='pocket-consumer-key',
access_token='pocket-access-token')
# Check https://getpocket.com/developer/docs/v3/retrieve for additional list of options you can pass for retrieving pocket list
articles = tagger.get_articles_from_api(count=10, offset=10, detailType='complete')
# Alternatively you can load the articles from file if you saved them previously using save_articles_to_file
# articles = tagger.get_articles_from_file("20190621.json")
# Generate tags for each article
articles_with_tags = tagger.get_tags_for_articles(articles)
# Save the articles with tags to file. You can use this file to verify it looks good before running the final step to tag the articles.
tagger.save_articles_to_file(today.strftime('%Y%m%d-with-tags.json'), articles_with_tags)
# You can skip this step if you want to do a dry run. Verify the tags in the file we generated in the previous step.
tagger.add_tags_to_articles(articles_with_tags)You can override the default thresholds for entity salience and category confidence
thresholds = {
'entity_salience_threshold': 0.7
'category_confidence_threshold': 0.3
}
articles_with_tags = tagger.get_tags_for_articles(articles, thresholds)Sample output from running it for my 490 items long Pocket list
Xunder Entities or Categories denotes the NLP client returned those as potential candidates, but we skipped them because it didn't meet the threshold. You can see the last lineTags: abc, xyzfor list of tags pocket-tagger added for each URL.
(1/490) https://www.reddit.com/r/explainlikeimfive/comments/bvweym/eli5_why_do_coffee_drinkers_feel_more_clear/?utm_source=share&utm_medium=ios_app
Title: ELI5: Why do coffee drinkers feel more clear headed after consuming caffeine? Why do some get a headache without it? Does caffeine cause any permanent brain changes and can the brain go back to 'normal' after years of caffeine use? : explainlikeimfive
Description: r/explainlikeimfive: **Explain Like I'm Five is the best forum and archive on the internet for layperson-friendly explanations.** Don't Panic!
Entities:
X Coffee Drinkers: 0.2438652664422989
X Eli5: 0.14941969513893127
X Caffeine: 0.12065556645393372
X Caffeine: 0.0874909833073616
X Some: 0.06917785853147507
X Headache: 0.0606028214097023
X Brain: 0.03606536239385605
X Explainlikeimfive: 0.033727116882801056
X Brain Changes: 0.03211209550499916
X Caffeine Use: 0.029848895967006683
X R: 0.02966366335749626
X Forum: 0.028598546981811523
X Internet: 0.022404097020626068
X Archive: 0.022404097020626068
X Explainlikeimfive: 0.017647551372647285
X Don'T Panic: 0.009302889928221703
X Five: 0.007013489492237568
X Five: 0.0
Categories:
/Food & Drink/Beverages/Coffee & Tea: 0.6700000166893005
Tags: Food & Drink, Beverages, Coffee & Tea
(2/490) https://www.reddit.com/r/television/comments/bnpwe3/enjoy_three_full_minutes_of_the_cast_of_game_of/?utm_source=share&utm_medium=ios_app
Title: Enjoy three full minutes of the cast of 'Game of Thrones' expressing disappointment in Season 8. : television
Description: r/television:
Entities:
X Cast: 0.31218624114990234
X Disappointment: 0.20341947674751282
X Season: 0.20341947674751282
X Game Of Thrones: 0.13265934586524963
X Television: 0.08712445199489594
X Television: 0.06119102984666824
X 8: 0.0
X Three: 0.0
Categories:
/Arts & Entertainment/TV & Video/TV Shows & Programs: 0.75
Tags: Arts & Entertainment, TV & Video, TV Shows & Programs
(3/490) https://www.reddit.com/r/homeautomation/comments/awvf5r/local_realtime_person_detection_for_rtsp_cameras/
Title: Local realtime person detection for RTSP cameras : homeautomation
Description: r/homeautomation: A subreddit focused on automating your home, housework or household activity. Sensors, switches, cameras, locks, etc. Any …
Entities:
X Realtime Person Detection: 0.3057926297187805
X Homeautomation: 0.15315502882003784
X Cameras: 0.14035314321517944
X Rtsp: 0.07461880147457123
X Homeautomation: 0.051411159336566925
X Home: 0.047811269760131836
X Housework: 0.04366889223456383
X Subreddit: 0.04183248057961464
X R: 0.04132793843746185
X Cameras: 0.032860007137060165
X Locks: 0.028899790719151497
X Household Activity: 0.012798599898815155
X Switches: 0.012735127471387386
X Sensors: 0.012735127471387386
Categories:
/Computers & Electronics: 0.7900000214576721
Tags: Computers & Electronics
- Pocket API Wrapper for Python
- Pocket API Docs
- Google Cloud Natural Language Processing
- Beautiful Soup
- Complete list of content categories from Google Natural Language API