Overview
In the Summer of 2017 I was invited to intern at Lambda Zen, a digital
transformation consultancy. The project that was assigned to me was a sentimental processing app for a digital transformation which got me totally excited!
Our goal was to define the sentimental processing strategy and develop a sentiments processing platform to educate clients about the voice of the customers such as reviews and survey responses, online and social media for applications that range from marketing to customer service by using google natural language API with node.Js technologies.
Details
How would I go about this?
What did I do and for how long
Awesome and satisfying experience for 8 weeks where I worked with an architect to validate the strategy, idea development and design along with a lot of research where I discovered the way to integrate the tool/processes as well as design & deliver the application
Design and Approach
Reach out to the business for the requirements - what next?
Discovery and approach to solve the business problem
Design the best solution
Deliver the solution
Gather the lessons learned to improve things in the future
>> Awesome Product>>
Step By Step
Requirements
Lambda Zen was processing customer sentiment analysis requests as an ad-hoc custom process without automating and building on existing assets.
This project was an effort to build an automated process that would handle broader sentimental request across different industries and different departments to help them focus on marketing and services with just the client’s specification as an input
Immersion and Discovery
> Audit the clients sentiment requests and the processing steps necessary to fulfill the request
>  Use the architect's vision and input and google’s sentiment analysis tool's capability to develop guidelines for the tool/ automation strategy
>  Do further research on google’s sentiment capability based on the requirement and discuss with architect for the tools/automation development guidance
Solution Design
>Â Conduct 1-on-1 meetings with architect to uncover goals, pain points, aspirations, and the needs related to automating sentimental analysis
> Research google sentimental analysis setup, API calls and integration with Node.js to understand the design perspective
> Design the automation and testing strategy by gathering architect feedbackÂ
>Â Leverage teachings from the architecture meeting and use it to refine the automation process
Solution Delivery/ Lessons Learned
> Develop and validate the creation during the test iteration and sprint
> Synthesize and prioritize learnings from iterative testing
> Leverage learnings to create a final, refined version of the app
> Port the solution from my local machine to Lambda Zen’s server
> Document the sentimental analysis scope and details along with the various terminologies like entity, sentiment polarity and magnitude
​