Sentiment-Aware Recommendation System for Healthcare using Social Media
Table 2: Evaluation results of 5-fold cross-validation for sentiment classification.
['[BOLD] Model', '[BOLD] Accuracy', '[BOLD] Cohen-Kappa', '[BOLD] Macro Precision', '[BOLD] Macro Recall', '[BOLD] Macro F1-Score']
We define a baseline model by implementing the CNN based system as proposed in [medicalsentiment2018] under the identical experimental conditions as that of our proposed architecture. We also develop a model based on an LSTM. To see the real impact of the third layer, we also show the performance of a CNN-LSTM based model. Batch size for training was set to 32. Evaluation shows that the proposed model performs better than the baseline system, and efficiently captures medical sentiment from the social media posts. In comparison to the baseline model this is approximately a 9.13% improvement in terms of all the metrics. Posts usually consist of medical events and experiences. Therefore, capturing temporally related spatially close features is required for inferring the overall health status. The proposed CNN-LSTM-CNN network has been shown to be better at this task compared to the other models.