Emotion Detection in Student Feedback Using Natural Language Processing

Abstract: The sentiment analysis system presented in this project employs a methodology rooted in manual keyword analysis, capitalizing on the inherent associations between specific words and emotional sentiments. For instance, in movie reviews, positive expressions are characterized by terms like "great" and "love," while negative sentiments are often conveyed through words such as "hate" and "awful." By quantifying the frequency of these selected keywords, comprehensive feature vectors are constructed to capture the nuanced sentiment of input data. These vectors are then utilized in conjunction with a Support Vector Machine (SVM) algorithm for precise sentiment classification. The system showcases commendable accuracy, particularly in contexts where domain-specific sentimentcarrying vocabulary is readily available. However, it is essential to acknowledge the potential limitations stemming from evolving language trends and nuances not captured by the manual keyword analysis.