IAT | Devpost

Inspiration

Discover the voice of your customers through the power of information aggregation and data analysis. Uncovering Customer Sentiments provides a deep dive into the emotions behind each comment, giving you the insight you need to improve your online presence and connect with your audience on a deeper level. Also it unleashes the other abilities of an information aggregator like text summarization and text simplification.

What it does

"Uncovering Customer Sentiments: A Comprehensive Analysis of Positive, Neutral, and Negative YouTube Comments" "Summarizes the essence of the vast textual data". "Syntactical simplification of the textual data by preserving the context and the meaning".

How we built it

We built this three tools utilizing python and cutting edge technologies such as state of the art machine learning models

Challenges we ran into

struggles in data collection, finding the appropriate neural model, integration with UI

Accomplishments that we're proud of

We achieved all the tasks in the Information aggregation tasks like data collection, data preprocessing, data modeling , and data visualization. We have a working solution of three major tasks of information aggregation i.e. Sentimental Analysis, Text Summarization and Text Simplification.

What we learned

Various data sources and proper data collection. Techniques of data preprocessing and data cleaning. ML models like RFClassifier, XGBoost, GradientDescent, Adaboost Classifier and etc.. Evaluation metrics for the measuring the performance of the model. HTML ,CSS, Java Script, Python, Flask, UI/UX development. Learnt the usage of WIX for the front end development.

What's next for IAT

Going ahead we can improve the accuracy of the tool by training with some other data sources and making the tool work across different domains. Improving the user experience more and making the tool more user interactive by using advanced front end networks. Adding data storage aspect to the tool so that it can store the information for any company/customer specific data. Deploying it in cloud.

Built With

Submitted to

TAMUhack 2023

Created by

In my role as a backend developer, I made significant contributions to the development of the project by creating Flask APIs and training machine learning models for sentiment analysis and text summarization.

I utilized my expertise in Python programming language to build a robust and scalable backend solution using Flask framework. The APIs I developed enabled seamless integration with the frontend and allowed for easy data exchange between the two.

In addition to this, I applied my knowledge of NLP and machine learning to train and optimize models for sentiment analysis and text summarization. I carefully selected and pre-processed the data to ensure high accuracy and effectiveness of the models. My efforts resulted in models that could accurately predict the sentiment of text inputs and generate concise summaries of long texts.

Praveen Kumar Maddikuntla
akhil chilumuru
Loksubhash Pulivarthi
Druva Kumar Gunda