HAPPINESS/SUFFERING factors recognition based on point-wise mutual information

This work presents a novel approach for happiness improvement based on two psychological factors of redefined individual alphabets on “HAPPINESS” and “SUFFERING” (See Section II-A), of which the factor recognition based on Point-wise Mutual Information (PMI) method is treated as baseline. To further enhance PMI performance, a modified baseline namely Keyword PMI (K-PMI) approach is proposed by using POS tags as features for the keywords selection, and calculates the association between the factor category and the keyword in the training lexicon. To reduce the scale gap of PMI value, normalizing PMI value is essential and adjusted by the significant score of the keyword for the factors category classification. The experimental results have shown that the proposed K-PMI approach can achieve an average recognition accuracy of 96.72% in the inside test. Whereas the outside test, the average precision rate is able to achieve 73.5% accuracy, which is significantly higher than the baseline accuracy of 43.46%, such results further to prove the proposed K-PMI approach outperforms the baseline in the experiment, and demonstrates the efficiency and the feasibility of the proposed approach.