Create gsoc-ideas.md

Signed-off-by: Sean P. Goggins <outdoors@acm.org>

gsoc-ideas.md

@@ -0,0 +1,56 @@
+
+## Idea: Enhance Conversational Topic Modelling Capabilities in CHAOSS Software
+
+**Hours: 350**
+
+[Micro-tasks and place for questions](https://github.com/chaoss/augur/issues/1640)
+
+This project will add GenSIM logic, and other capabilities to the Clustering Worker inside of Augur Software, and be extended into a generalized Open Source Software Conversational Topic Modeling Instrument. 
+
+CHOASS/augur has several workers that store machine learning information derived from computational linguistic analysis of data in the `message` table. The message table includes messages from issue, pull request, pull request review, and email messages. They are related to their origin with bridge tables like `pull_request_message_ref`. The ML/CL workers are all run against all the messages, regardless of origin. 
+
+1. Clustering Worker (clusters created and topics modeled)
+2. message analysis worker  (sentiment and novelty analysis)
+3. discourse analysis worker (speech act classification (question, answer, approval, etc.)
+
+Clustering Worker Notes: 
+
+Clustering Worker: 2 Models.
+ - Models: 
+  - Topic modeling, but it needs a better way of estimating number of topics.
+   - Tables
+    - repo_topic
+    - topic_words
+  - Computational linguistic clustering
+   - Tables
+    - repo_cluster_messages
+ - Key Needs
+    - Add GenSim algorithms to topic modeling section https://github.com/chaoss/augur/issues/1199
+  - The topics, and associated topic words need to be persisted after each run. At the moment, the topic words get overwritten for each topic modeling run. 
+  - Description/optimization of the parameters used to create the computational linguistic clusters.
+  - Periodic deletion of models (heuristic: If 3 months pass, OR there’s a 10% increase in the messages, issues, or PRs in a repo, rebuild the models)
+  - Establish some kind of model archiving with appropriate metadata (lower priority)
+
+Discourse Analysis Worker Notes: 
+
+discourse_insights table (select max(data_collection_date) for each msg_id)
+ - sequence is reassembled from the timestamp in the message table (look at msg_timestamp)
+ - issues_msg_ref, pull_request_message_ref, pull_request_review_msg_ref
+
+Message Analysis Worker
+ - message_analysis 
+ - message_analysis_summary
+
+<img width="1159" alt="augur-tech" src="https://user-images.githubusercontent.com/379847/124799236-f440dc80-df19-11eb-84ce-302cf274884f.png">
+
+The aims of the project are as follows:
+  - Advance topic modeling of open source software conversations captured in GitHub.
+  - Integrate this information into clearer, more parsimonious CHAOSS metrics.
+  - Automate the management machine learning insights, and topic models over time. 
+  - (Stretch Goal) Improve the operation of the overall machine learning insights pipeline in CHAOSS/augur, and generalize these capabilities. 
+
+
+* _Difficulty:_ Medium
+* _Requirements:_ Interest in software analytics. Python programming. Conceptual understanding of machine learning, and an eagerness to learn maching learning, and SQL knowledge.
+* _Recommended:_ Experience with Python
+* _Mentors:_ Sean Goggins, Andrew Brain, Isaac Milarsky