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sentiment-analyser's Introduction

DurHack 2022: Sentiment Analyser

What is this project?

  • A platform that receives SMS customer queries and responds to them accordingly in order of priority.

What inspired us to come up with Sentiment Analyser?

  • The problem introduced by Atom Bank on ordering customer queries
  • The initial demonstration of building an automated SMS messaging app using Twilio

Main features of Sentiment Analyser

  • Twilio
  • Twilio (AI) Autopilot
  • Firebase
  • MonkeyLearn
  • Fetch API
  • Rest API

What does Sentiment Analyser do?

  • Receives SMS messages sent to a phone number attached to a Twilio account
  • Send SMS replies using a chatbot
  • Save the SMS query to Firebase
  • Provide a REST API that's queried from a frontend website using the fetch API
  • Display customer queries on the website
  • Order the queries based on ML semantic analysis, keyword frequency detection and date created

How is Sentiment Analyser linked to the theme Unity?

  • We unify companies and customers who have specific queries through the use of Twilio
  • We unify companies with customer information
  • The text, and Semantic Machine Learning model (from the website) unifies the APIs and websites

How did we develop Sentiment Analyser?

  • Interactions via SMS (SMS chats) are done using Twilio
  • The REST API was built with NodeJS
  • The fetch API and frontend website was built with Javascript, HTML and CSS
  • ML semantic analysis was done using a third party API called MonkeyLearn

What challenges did we run into when building Sentiment Analyser?

  • We were briefly only able to receive SMS messages from one phone number
  • We ran into issues querying MonkeyLearn with the fetch API due to a lack of documentation
  • We decided to use a cloud hosting platform as we ran into issues running the Twilio endpoints locally
  • Initially we used a JSON file and the cloud hosting platform deleted it every time the server went down. To overcome this, instead of storing the data locally, we stored it in Firebase
  • We used ngrok to solve an error that we were facing in the Terminal. ngrok allowed us to expose a web server running on our local machine to the Internet.
  • We had to import the module dotenv in order to protect the authentication token and account SID from getting hacked.

What accomplishments are we proud of as a result of Sentiment Analyser?

  • Developing a useful web application within 24 hours that solves a real world problem efficiently
  • Understanding how to use and get Twilio to work without any prior knowledge
  • Developing a usable platform that provides useful functionalities
  • Learning Firebase without prior knowledge

What did we learn from building Sentiment Analyser?

  • How to implement automated SMS messaging and a chatbot with Twilio
  • How to store and fetch a JSON object in Firebase

Further Improvements

  • Expanding the program into a multi-messaging platform, e.g. email
  • A more creative design to make the platform visually more appealing

(MonkeyLearn: username=[email protected], password=!DurHack2022)

sentiment-analyser's People

Contributors

aaroncheung430 avatar rshah033 avatar rafipan avatar sruthi-29 avatar

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