This is nlp sandbox with using slack messages. There are some functions bellow.

• Generate Wordcloud image each user
• Generate Wordcloud weekly or monthly image
• Vectorize the content of each user's post and save as KVS
• Recommend users who are interested in a given word

• Get messages via Slack API (require: API key)
• Basic preparation
  • cleaning
  • morphological analysis
  • normalization
  • stop words removal
• Visualization
  • Wordcloud
• Feature embedding
  • Word2vec
  • Doc2vec
• Weighting factor
  • tf-idf
• Similarity search
  • with Word2vec
  • with Doc2vec
  • with Elasticsearch
  • ... etc

• install Docker
• Get Slack API key
  • channels api
  • users api

Save as nlpslack/data/conf/credentials.json

{
    "channel_api_key": "YOUR_CHANNELS_API_KEY",
    "user_api_key": "YOUR_USERS_API_KEY"
}

$ docker-compose up -d

$ docker exec -it nlpslack python nlpslack/main.py [opt]

$ docker exec -it nlpslack bash

$ docker exec -it nlpslack python nlpslack/main.py wc u

Then you can find wordcloud images in (docker)dev/nlpslack/data/wc_by_usr

# Generate Wordcloud weekly image
$ docker exec -it nlpslack python nlpslack/main.py wc t --term w
# Generate Wordcloud monthly image
$ docker exec -it nlpslack python nlpslack/main.py wc t --term m

Then you can find wordcloud images in (docker)dev/nlpslack/data/wc_by_term

$ docker exec -it nlpslack python nlpslack/main.py vec -o KVS_PATH

Then you can find KVS file ( *.json ) in KVS_PATH
default KVS_PATH : (docker)dev/nlpslack/data/content_features.json

require: (3) is done

If execute, I show recommended user's name and similarity score.

$ docker exec -it nlpslack python nlpslack/main.py search --word SAMPLE
$ Recommended users are bellow ...
$ Key word: "SAMPLE"
$ 1. User03: 0.8799
$ 2. User11: 0.7653
$ 3. User05: 0.3442

$ docker exec -it nlpslack python nlpslack/main.py -h