Voice Based Hybrid Sentiment Analysis on Movie Reviews
Mrs. G. Venkateswari · International Journal for Research in Applied Science and Engineering Technology · 2025
In the digital era, online movie reviews have become a key platform for audiences to share their opinions and sentiments. While many sentiment analysis systems focus exclusively on text-based data, they often miss the subtle emotional signals conveyed through speech. This paper introduces a voice-based hybrid sentiment analysis model that combines both acoustic features and textual content to enhance sentiment classification accuracy. The system integrates machine learning algorithms such as Support Vector Machine (SVM), Naïve Bayes, and Linear Regression to create a robust hybrid model. Acoustic data is analyzed to extract prosodic and spectral features like pitch, energy, and Mel Frequency Cepstral Coefficients (MFCCs), while Natural Language Processing (NLP) techniques are employed to process transcribed text. By merging both audio and text features, the model improves sentiment polarity detection accuracy. Experimental results on publicly available datasets show that this hybrid approach outperforms traditional single-modality methods. This research emphasizes the value of multi-modal sentiment analysis and paves the way for more emotionally intelligent human-computer interactions.