Amogh Khaparde

deep learning model to predict sentiment of a product using social media posts

Started May 7, 2024

Abstract or project description

The goal of this project is to build a deep learning model to predict sentiment towards products using review comments. The model is trained on the "Brands and product emotions" dataset from data.world which includes reviews from Reddit and other various sources. The dataset itself contains around 8700 reviews about different products such as iPads or iPhones. The model is an encoder-only transformer built from scratch, which represents one of the most state-of-the-art language models aside from BERT, ChatGPT, and other LLMS. This is a supervised classification learning task meant to aid companies' decisions and future product-building. Surrounding this application is a pre-trained model that actively mines reviews from various websites in order to feed the transformer and evaluate public opinion on a certain product.

To what extent can deep neural networks help hearing-impaired individuals communicate better online with hearing individuals by translating fingerspelling into realistic human voice, without a human sign language interpreter?

Started Mar. 28, 2023

Abstract or project description

Nearly 20% of the world’s population is hearing impaired. Many of these people require a sign language interpreter to help them communicate with hearing individuals. However, as AI and deep neural networks become more efficient and powerful, these tools can be used to help hard-of-hearing people communicate. Hiring a human interpreter usually requires prodigious amounts of money over time and may not always be available when needed. However, one can carry a machine learning model in their devices at all times to help them communicate much more easily. To create such a system, I made a large custom image dataset, consisting of approximately 50,000 images of each ASL character, and trained a raw convolutional neural network model which had a 97% accuracy. Later, I also trained a background cropping convolutional neural network model with a different, but equal in size dataset that boasted a 98.5% accuracy. The background cropping did slightly better than the raw CNN model, achieving a 1.5% accuracy increase. To further help hard-of-hearing individuals communicate with hearing individuals, I created an application surrounding this machine learning model. This application consisted of a text-to-speech system using Play.HT API and a ChatGPT API system to correct any misclassifications the model makes and ease communication by letting the user listen rather than read text. The user interface is very intuitive, consisting of the predicted probability, predicted alphabet, user webcam input, and the classified characters in a sentence. Lastly, the application included a manual spacebar and delete key to allow individuals to change any errors they make while signing. In conclusion, I met my goals of creating a high-performance and feasible AI application to help ease communication between hard-of-hearing and hearing individuals.