OfficeBot: joint work of NLPC MU and Konica Minolta

student R. Sabol

a Slack bot that supports teamwork

Project web: ​https://nlp.fi.muni.cz/projects/officebot/
Project repository: /nlp/projekty/officebot/trac-git

tasks:

• detect channel activity
  • active/non-active (displayed as bold/normal typeface)
  • type of activity (conversation = several mutually related posts, individual posts)
  • language of the activity (Czech/English?, short messages/long posts ...)
  • sentiment of the activity (possible flamewar, emoji-based classification?)
• summarize activity for a particular channel
  • from a particular moment track all messages
  • cluster conversations (based on time span and/or topic)
  • detect keywords
  • highlight most important messages or part of messages
• summarize activity for all channels
• suggest joining a conversation based on user preferences (favorite topics, languages, sentiment ...)
• suggest moving part of a conversation into external applications
  • transfer to calendar, trello, private channel (talk to myself)

data:

• publicly available dataset ​https://github.com/houstondatavis/slack-export
• KM data (to be provided after filtering out sensitive information + solving legal issues)

language:

• English (needed to filter out non-English texts)
• probably needed tools for "internet language" (e.g. abbreviation expansion, emojis)

Plan

Time period	Student Activity	KM Activity
M1	study the data study Slack functionalities bring ideas about what can be done	resolve legal issues concerning KM conversations data
M2	create the basic chatbot: listen on different channels detect channel activity quantify channel activity	run the chatbot on KMLE Slack start collecting feedback
M3	study NLP techniques/implementations that can support smart features for the chatbot extend the chatbot with language detection propose a evaluation for the new feature cluster conversations based on time span
M4-M5	extend the chatbot with keyword detection using TF-IDF or another "simple" technique study the specifics of the Slack conversations cluster conversations based on keywords
M5-M6	study techniques for text summarization extend the chatbot with summarization	provide feedback for keyword detection
M7	extend the chatbot with sentiment analysis based on words/emojis suggest joining a conversation based on keywords/topics/participants/sentiment...	provide feedback for text summarization
M4-M8	extend the chatbot with connectors to applications such as Trello, calendar etc.	provide feedback for sentiment analysis
M9	evaluate and deliver the application