Sentiment analysis by deep learning approaches

We propose a model for carrying out deep learning based multimodal sentiment analysis. The MOUD dataset is taken for experimentation purposes. We developed two parallel text based and audio based models and further, fused these heterogeneous feature maps taken from intermediate layers to complete the architecture. Performance measures – Accuracy, precision, recall and F1-score – are observed to outperform the existing models.