Date Approved

4-27-2023

Graduate Degree Type

Project

Degree Name

Applied Computer Science (M.S.)

Degree Program

School of Engineering

First Advisor

Dr. Erik Fredericks

Academic Year

2022/2023

Abstract

This project aims to analyze social media trends using Instagram as the primary source of data extraction and provides insights such as understanding engagement rates, top hashtags, optimal posting times, and post rankings based on engagement rates and sentiment scores thus the users can understand their audience and improve their posting strategies. Firstly, the profile trend chart and area chart based on the average engagement rate is generated to obtain day, week, or month engagement rates. Secondly, text preprocessing is done before generating a word cloud displaying the most frequently occurring words in the captions. The size of each word in the cloud is proportional to its frequency in the captions. By analyzing this word cloud, one can get an overall idea of the themes and topics that the user tends to discuss in their captions. Thirdly, the top hashtags are generated, and fourthly, using engagement rate as a metric the optimal time and day of the week are determined for the posts on a user account. Finally, posts are ranked based on their engagement rates and sentiment scores. By leveraging these analytics, users can better understand the type of content that resonates with their followers, which can inform their posting strategies to increase engagement and grow their following.

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