Sentiment-Aware Recommendation System for Healthcare using Social Media
Table 3: Evaluation corresponding to the top-n similar posts retrieval. ’A’ and ’B’ denote the results corresponding to the proposed metric and K-nearest neighbor algorithm using text similarity metric, respectively.
['[BOLD] Sentiment', '[BOLD] Pearson correlation A', '[BOLD] Pearson correlation B', '[BOLD] Precision@5 A', '[BOLD] Precision@5 B', '[BOLD] DCG5 A', '[BOLD] DCG5 B']
We design a baseline model using K-nearest neighbour algorithm that makes use of cosine similarity metric for capturing the textual similarity. From the evaluation results, it is evident that similarity scores assigned by the proposed system are more positively correlated with the human judgments than the baseline. Correlation can be considered statistically significant as the p-values corresponding to all the sentiment classes are less than 0.05. The better Precision@5 metric corresponds that a greater number of relevant posts are retrieved by the proposed approach in comparison to the baseline model.