Publication Date
Spring 2024
Degree Type
Master's Project
Degree Name
Master of Science in Computer Science (MSCS)
Department
Computer Science
First Advisor
Robert Chun
Second Advisor
Fabio Di Troia
Third Advisor
Lokesh Ponnada
Keywords
Instagram data, engagement class, sentiment analysis, RF, Stacking Classifier, XGB, SMOTE
Abstract
With enormous amount of social media content, we can draw valuable insights. In this paper, we apply different Machine Learning and Deep Learning techniques on Instagram data to determine the techniques that work well to discover the engagement class of a social media post. Out of all the social media platforms, Instagram is growing rapidly not just in the number of users but also in terms of Advertisement and marketing surpassing YouTube’s advertisement revenue. The end goal of this paper is to propose a technique to predict the engagement class. We applied Random Forest (RF), Stacking Classifier, Extreme Gradient Boost (XGB), Voting Classifier, K- Nearest Neighbor (KNN), Neural networks for determining the engagement of a particular post. We check the effect of word embeddings like BERT, Word2Vec in predicting the engagement class. We also check the effect of applying SMOTE on the dataset. Secondly, the paper deals with finding caption sentiments using GPT model and lexicon-based (Vander) sentiment analyzer.
Recommended Citation
Gorrepati, Lakshmi Prasanna, "Instagram Data Analysis Using Machine Learning" (2024). Master's Projects. 1365.
https://scholarworks.sjsu.edu/etd_projects/1365
