API project

The goal of this project is to analyze the conversations of my team to ensure they are happy 😃

Main goal: Analyze the conversations coming from a chat like slack

How ?

• (L1🧐) Writing an API in bottle just to store chat messages in mySQL

• (L2🥳) Extracting sentiment from chat messages and perform a report over a whole conversation

• (L4🤭) Recommending friends to a user based on the contents from chat documents using a recommender system with NLP analysis

• (L3😎) Creating an image of the API with Docker

API endpoints

@route('/hello/')

- Gives you the chance to say hello to your own self, very useful if you are just testing the API works 

@get('/idUser/user')

- Returns every user in DB, UserId and their names

@get('/idUser/')

- Returns every message a user has sent, given an UserId

@get('/mensajes/')

- Returns the user name and how many messages has ever sent, given an UserId

@get('/chat/<chat_id>/list')

- Returns a list of all the messages in a chat, given an ChatId

@post('/user/create')

- Create a user and save into DB
- Returns the UserId

Params: UserName

@post('/create/chat')

- Create a conversation in the DB, to load messages
- Returns the ChatId

@post('/chat/addmessage')

- Add a message to an existing conversation into DB
- Returns the MessageId

Params: Text, ChatId and UserId

@get('/user/<user_id>/sentiment')

- Returns a JSON array with the results of analyzing all messages from a User, given a UserId

{'neg': 0.1, 'neu': 0.776, 'pos': 0.125, 'compound': 0.501} used nltk for this task

@get('/chat/<chat_id>/sentiment')

- Returns a JSON array with the results of analyzing all messages from a Chat, given a ChatId

{'neg': 0.209, 'neu': 0.69, 'pos': 0.101, 'compound': -0.834} used nltk for this task

@get('/findafriend/')

- Given a UserName, it recommends you a friend based on similarities with other Chat's users

Used Sklearn for this task

Links - API dev in python

• [https://bottlepy.org/docs/dev/]

• [https://www.getpostman.com/]

  Links - NLP & Text Sentiment Analysis

• [https://www.nltk.org/]

• [https://towardsdatascience.com/basic-binary-sentiment-analysis-using-nltk-c94ba17ae386]

• [https://www.digitalocean.com/community/tutorials/how-to-perform-sentiment-analysis-in-python-3-using-the-natural-language-toolkit-nltk]

  Links - Heroku & Docker & Cloud Databases

• [https://docs.docker.com/engine/reference/builder/]

• [https://runnable.com/docker/python/dockerize-your-python-application]

• [https://devcenter.heroku.com/articles/container-registry-and-runtime]

• [https://devcenter.heroku.com/categories/deploying-with-docker]

• MySQL ClearDB [https://devcenter.heroku.com/articles/cleardb]