Phonetic-enriched Text Representationfor Chinese Sentiment Analysiswith Reinforcement Learning
TABLE X: Performance comparison between learned and random generated phonetic feature.
['Random phonetic feature (rand)', 'Random phonetic feature (rand)', 'Weibo 53.83', 'It168 56.85', 'Chn2000 55.71', 'Review-4 69.20', 'Review-5 69.77']
Iv-E1 Validating phonetic feature So far, we have examined the effectiveness of our model as a whole by comparing it with different baselines. In this section, we break down the proposed methods into a reinforcement learning framework and a set of features. First of all, we would like to validate if the performance gain mainly results from the reinforcement learning framework. To this end, we replace the phonetic features with random features. In particular, we generate random real-valued vectors as random phonetic feature for each character. Each dimension of the random phonetic feature vector is a float number between -1 to 1 sampled from a Gaussian distribution.