EDIT: Fixed the UI: I'm assuming you were looking at a topic, the "probable mood" used to render as it relates to the topic being searched, not current mood.

Highly recommend reading the "how it works" section (I assume you already have):

https://hnprofile.com/learn-more

That section has a few examples and details. I'll add further details below.

The system ONLY looks at comments sent to it. It, kind of looks like:

    { Author, Comment }

From there, everything else is derived. Nothing else, no profiles, no voting, nothing. That's it.

The system identifies:

  - Expertise
  - Knowledge
  - Interests
  - Relevant content
  - Promoter score (an improved net promoter score)
  - Trends
  - Mood
  - Related Topics
  - etc.

To your question, there are multiple algorithms at work determining mood, most are pretty standard models. These are ones you can find in NLTK, spaCy, CoreNLP, etc. There's also a couple home-baked ones. These models than "vote" for the mood, with an averaged score. Typically these scores are 3 - 7 models voting at any one time. The aggregate score is roughly 80% accurate (based on my tags), and it tags the topics, sentence, and overall comment all separately. Finally, the models are much more accurate at the extremes: Very Positive and Very Negative, but in the middle, it's much fuzzier. This is in part, just how language works, it's subjective.

All NLP models are based on a combination of open datasets with the tagged sentiment, as well as my own manually created dataset, and finally, a dataset that was manually created via looking at following comments (for instance, "no need to be negative", is used to tag the prior comment.

The probable current mood is a prediction of your prior moods. I believe right now, it looks at the last time you discussed the given topic you searched (if you searched for a user, it is the most recent mood). If there is enough data, it'll also predict your mood, based on the time of day + topic being discussed.

Finally, I should add - there are definitely improvements to be made, and I'm currently working on mood. Primarily, the focus was on expertise, knowledge, and interests. That is what enables the "related content" section and is now very accurate (given a decent number of data samples). Mood, as stated, is ~80% accurate based on my tags. I think I can do better (probably >90%), especially with more labeled data for this particular dataset.

The goal of this is it can drop into any company and instantly can search for who and what is relevant to your company.

What else is karma for? :D I have seen this technique used to great effect inside large companies too. People will spend a lot of effort telling you why you are dead wrong.

Just to raise the visibility of this: lettergram's HNProfile project is awesome. It identified both of my alt accounts: https://news.ycombinator.com/item?id=17944348

Just now it was "Happy". Probably because https://news.ycombinator.com/item?id=17944992 contains "grateful".

Sentiment analysis is a finicky thing. (Maybe categories like "polite" or "argumentative" would be easier to detect?)

wife, example, data, service, github, phone, bit, sort, hah, area

I should show this to my wife (:

You're an expert at everything!

For reference, this started as a "different kind of search engine", utilizing conversation.

Mood wasn't exactly the focus, so it's robustness is limited - i.e. lots of room for improvement. The mood detection (based on my manual tagging) is roughly 80% accurate, which IMO was "good enough". It's more accurate as you get to the edges (very positive or very negative), the middle is kinda fuzzy, but that's kind of the nature of language.

That's because it doesn't blacklist words which are commonly used in English without them being technical terms, nor doesn't it whitelist terms which are topics of interest. There's also no difference being made between referring to Wikipedia and being an expert about Wikipedia. If you regularly reference to Wikipedia (something I do) you then become an expert on the topic "wikipedia". As an example, check the expert topics from user tptacek [1] ("security, argument, fact, hn, thread, software, comment, story, google, sense").

The probable mood was rather... entertaining.

The notion this can be used to find sockpuppets or throwaways does not surprise me. Without sufficient effort you still use certain words, in certain order, consistently. Such can even be used to check out what your native language is. So if you are e.g. a blackhat who wrote ransomware it makes sense to have someone who never wrote on the internet to paraphrase or deal with the communication, or perhaps use tools such as translators.

Also, if you use delay (e.g. delay 10) it is going to appear on HN after you wrote it meaning it could appear in the next hour despite being written in the previous.

Despite the above, I did find this an interesting project; I just very much doubt its accuracy and usefulness.

[1] https://hnprofile.com/author_profiles?utf8=%E2%9C%93&search=...

The opt out currently, for privacy moving forward, is to cease contribution to the digital noosphere.

I suppose if you desired you could consolidate your doxx point to Google and ISP's by running all your comments through a couple rounds of Google translate.

There is no privacy possible in the global village. Expectation of such is technically naive.

I went from Happy to Unhappy while looking at it. I am so confused.