Table of Contents

1. Background
2. Features
   1. New ideas from readings
3. Dataset

Background

Pillar is creating machine learning models to detect great content during twitch streams. We have two models in mind: 1) predict which clips in a stream have potential to go viral 2) predict which clips (and in what order) could be used in a youtube video.

All code and documentation so far is focused on 1) predict which clips in a stream have potential to viral.

Features

1. Algorithm 1: Find the best moments in clips based on where the most users participated. Most is defined as the ratio of unique users during a 2 min section to unique users for the entire session.
2. Algorithm 2 Find the best moments in clips based on when rate of messages per user peaked. This involves answering the question "at which 2 min segment do the most users send the most messages?". If users X, Y, and Z all send 60% of their messages at timestamp range delta, then that timestamp might qualify as a "best moment"
   1. NOTE: Currently answers the question "at which 2 min segment do users send the most messages fastest"
3. Algorithm 3 (WIP) Weigh each user by their chat rate, account age, etc. Heavier users predicted to chat more often at "best moment" timestamps
   1. STATUS: current weight determined by (num_words_of_user/num_words_of_top_user)
   2. Algorithm 3.5 Weighs clips based on most number of words/emojis/both used in chat
   3. Algo 3.6: ranks algorithms based mean emoji use by users, calculated as "number of emojis used at timestamp divided by number of unique users at that timestamp"
4. Algo 4: NLP sentiment per clip segment
   1. Weighs each timestamp by what percent of sentiment was positive, negative, or neutral

New ideas from readings

1. Timestamp with highest percentage of sub users participating
2. Cluster chat via brown clustering or something like that
3. This is basically algo 3_5: Repeated Lines This simple comparison is also a running percentage of the number of repeated lines in a segment. Emotes excluded again this is a very simple text comparison. In future iterations I would hope to be able to take advantage of a better (and cheaper :)) text analysis tool to better compare text lines for similarity.
4. This is basically algo 3_6: Emote Spam This simple calculation shows the percentage of chat lines that were emote only and contained more than one emote. No hard calculations here as I am able to grab the emote tags to determine if emotes are present and how many and this becomes my counter.

Dataset:

ML_Infra created a dataset from the top 100 CCCs for the top 50 games, and pulled the chat logs for each videoId that the CCCs came from.

Currently its in https://github.com/pillargg/timestamps/tree/add-datasetcreator-lambda/lambdas/datasetcreator, but it will be moved to the ML_Infra repo soon.