Research and Methodology


Importance of Research

​Understanding the factors that contribute to a song's popularity is vital for artists, marketers, and record labels. Accurate predictions of song trends can inform strategic decisions in content creation and marketing, leading to more effective audience engagement. Unlike existing applications that predominantly cater to well-established artists, our research focuses on developing a tool specifically designed for content creators and independent musicians. This approach addresses the limitations of native social media analytics, which often provide fragmented and platform-specific data, by offering a comprehensive, research-based solution that integrates insights across multiple platforms. This ensures a more holistic understanding of music trends, free from commercial biases, and aims to empower users with data-driven strategies for content creation and marketing.

Research Methodology Explanation

​In research, the methodology section is crucial as it outlines the processes and techniques used to collect and analyze data, ensuring the study's validity and reliability. In my capstone project, I employed a quantitative approach, utilizing machine learning algorithms to analyze numerical data from Spotify's audio features. This method enabled the identification of patterns and trends in song popularity, providing actionable insights for content creators, small businesses, and marketers. Looking ahead, I plan to integrate qualitative research methods, such as interviews and focus groups, to capture the subjective experiences and preferences of my target audience. This combination of quantitative and qualitative approaches will offer a comprehensive understanding of music trends, enhancing the effectiveness of marketing strategies and content creation.

Initial Capstone Findings

During my capstone project at Full Sail University, we explored the application of machine learning to predict Spotify song popularity trends. By analyzing audio features like tempo, valence, and danceability, we identified that these features strongly correlate with the rise, fall, or stability of a song's popularity. Using a Multi-Layer Perceptron (MLP) neural network, we achieved 97% accuracy, demonstrating that neural networks are essential for capturing the complexity of song trends—especially when dealing with a wide range of interrelated features. To enhance the practical application of the model, I built a Django-based web application to display real-time results and allow users to interact with the model. This application also automates the integration of new data from Spotify, enabling continuous tracking of trends as they evolve. By collecting and processing fresh data regularly, the model can be refined over time, ensuring it stays accurate and responsive to changes in song popularity. This approach not only demonstrates the power of machine learning but also sets the foundation for ongoing improvements and real-time trend predictions.

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Current Research

Building upon the success of my capstone project, my current research is focused on expanding the analysis beyond Spotify and into video content. Since access to the Spotify API has been restricted, I have begun developing my own algorithm for extracting audio features, which will allow me to bypass external data limitations and have more control over the analysis pipeline. Additionally, I am integrating a video analysis algorithm to include emotion detection features, further enhancing the tool's predictive capabilities. This will allow for a more nuanced understanding of content trends by combining both audio and video data, providing richer insights into the factors that drive popularity. By leveraging a neural network model to process these complex, multimodal inputs, I aim to create a more robust and adaptable system for predicting trends across multiple platforms, ensuring that artists, content creators, and marketers can make data-driven decisions to enhance their reach and engagement.

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