Sentiment Analysis of Corporate Logo Alterations

Miller, Tyler

Sentiment Analysis of Corporate Logo Alterations

Author(s)

Miller, Tyler

Date Issued

April 29, 2024

Abstract

This study uses artificial data produced by OpenAI’s ChatGPT to demonstrates a Python-based sentiment analysis tool to investigate the effects of business logo modifications. The software analyzes sentiment data from a synthetic dataset, classifying features as positive, negative, or neutral in order to find trends in how the public responds to changes to the logo. Because all data is handled by Python and kept in CSV format, structured query capabilities allow for complex data analysis. Analyzing sentiment is an important part of organizing synthetic data, much like real-world data management procedures. By imposing restrictions such as unique identities and restricted data types, it guarantees accuracy and integrity and offers a strong framework for managing large datasets in a compliant and safe manner. Furthermore, the program supports intricate data analysis jobs, making the extraction process easier and insight validation, as well as playing a crucial role in the development and assessment of machine learning algorithms. This technique is widely used in the corporate world, as businesses frequently invest in rebranding through logo redesigns. The tool assists in determining whether the public views these changes favorably, unfavorably, or neutrally by examining sentiment trends. This analysis is important because negative public opinion might lower profitability and necessitate more expensive changes. Additionally, the sentiment analysis tool uses pie charts to illustrate data and show how much of an impact modifications to a logo have on customer sentiment and brand perception. This analysis can help companies that are changing their logos or their entire identities, including changing their color schemes or mascots. A sentiment shared on social networks provides businesses with information about consumer loyalty and response patterns essential for improving profit margins and strategic planning.

Major

Computer Science

First Reader(s)

Luman, Douglas J.