feconroses

Very cool project! Quick question: is the underlying Pushshift dataset updated with new Reddit data on any regular cadence (daily/weekly/monthly), or is this essentially a fixed historical snapshot up to a certain date? Just want to understand if self-hosters would need to periodically re-download for fresh content or if it's archival-only.

Very useful! If you could also add note sounds, that would be amazing!

Hello HN!

We have been seeing quite a lot of conversations in customer support teams around tagging tickets (used as part of triggers, macros, analytics, etc). And we know how its hard and time consuming process.

This is why we built a MonkeyLearn extension for Zendesk (that we are releasing today) to help this tagging process with machine learning.

With this integration, MonkeyLearn will automatically tag and categorize incoming tickets into Zendesk. It will predict the value of a given field based on the subject and content of a ticket (it uses your historical data to train the machine learning model).

You can find it in the marketplace here: https://www.zendesk.com/apps/support/monkeylearn-ticket-clas...

This is an initial version and it’s free to use (at least for most cs teams).

We are trying to understand the value and if it helps support teams in this process, so any type of feedback is greatly appreciated. Also, if you need any kind of help to fine tune the model, more than happy to help.

previous step before Waze + Waymo?

As an alternative, you can try Anafora https://github.com/weitechen/anafora

A good alternative to Mturk is ScaleAPI: https://www.scaleapi.com/

Hi /r/dataisbeautiful/!

For creating Tarsier, we used Tweepy for extracing tweets using the Twitter Public API, we used MonkeyLearn for analyzing the tweets and finally used Plotly for creating the visualizations.

You can see some of the insights we got using Tarsier here: https://blog.monkeylearn.com/donald-trump-vs-hillary-clinton...

Hi HN!

For creating Tarsier, we used Tweepy for extracing tweets using the Twitter Public API, we used MonkeyLearn for analyzing the tweets and finally used Plotly for creating the visualizations.

You can see some of the insights we got by using Tarsier here: https://blog.monkeylearn.com/donald-trump-vs-hillary-clinton...

You can check out the tool here: http://tarsier.monkeylearn.com/

Thanks Maëlle Salmon for this awesome analysis!

Awesome post! And great example on how you can use Machine Learning to makes salespeople life easier! Have you tried MonkeyLearn? You can easily create machine learning models on the fly, have great tools to improve your models (like explore which samples are creating confusions) and y0u don't have to worry about deploying the model in your servers, maintenance, etc.

Done! We added a button to hide / show the chart. It will remember your settings for future sessions.

The X-axis is the hour of the day. The graph is to see which categories are the most popular today, according to the hour published. We have added this legend to the graph.

Done! We added RSS feeds for each category. You can see the different feeds at the right of each category. For example programming feed: http://hackernews.demos.monkeylearn.com/feed.xml?c=programmi...

fixed

Cool idea! We will work on adding RSS feeds for each category page. Probably between today and tomorrow we will have it ready. Will let you know :)

Thanks! Will fix it!

We agree with you that there is room for improvement. We can make it more accurate by adding more training samples to the MonkeyLearn classifier. We plan to keep working on this to make it better.

Any ideas on how to make My Hacker News better? Please let us know!

Fantastic idea! Will implement this today too! Will let you know when its ready :)

Great idea! We will add a way to disable the chart today! Will let you know when its ready

Thanks for the feedback!

Thanks for reporting!

oops! fixed

Federico here from MonkeyLearn. If you have any questions about the product or any feedback, please let me know, I am here to help.