Exploring User Sentiment on Social Issues via Neural Network Transfer Learning in Digital Communities

Authors

Ananya R. Vangoor, University of Arkansas, FayettevilleFollow

Files

Date of Graduation

5-2025

Description

Understanding public sentiment on social issues is crucial for gauging the stance of the general population. Traditionally, surveys have been a common approach for this. However, to capture more candid opinions, social media provides a rich source of unadulterated opinions. By analyzing social media statements, we can gain insights into the perspectives of specific groups. More specifically, we will investigate the attitudes of the public into the relationship between hard work and success in the workplace.

To begin, I will be training a neural network on X, formerly known as Twitter, tweets to categorize each tweet as either pro-luck or pro-meritocracy. But will this neural network, trained on a particular set of tweets, accurately assess sentiment on a different social media platform? This is where transfer learning becomes valuable. Transfer learning leverages a model trained on one dataset (in this case, tweets) to analyze a similar group of users on another platform. My research focus is on comparing the results of inputting user posts from another social media platform (say Reddit) into the transfer-learned neural network as opposed to feeding those same posts into the original neural network trained on X tweets. By comparing the outputs of each neural network to the actual tweet classifications, we can assess which model is more accurate. The differences in their accuracy scores will help determine which approach performs better.

Transfer learning is a useful technique in neural networks since it eliminates the need to train a neural network from the start, which could be both time consuming and costly. However, it's crucial to evaluate the effectiveness of the transfer learning model to gauge its reliability.

Publication Date

2025

Document Type

Book

Degree Name

Bachelor of Science in Computer Science

Degree Level

Undergraduate

Department

Electrical Engineering and Computer Science

Advisor/Mentor

Gauch, Susan

Disciplines

Engineering

Keywords

Engineering

Citation

Vangoor, A. R. (2025). Exploring User Sentiment on Social Issues via Neural Network Transfer Learning in Digital Communities. 2025 Research Poster Competition. Retrieved from https://scholarworks.uark.edu/hnrcsturpc25/11