Phonetic-enriched Text Representationfor Chinese Sentiment Analysiswith Reinforcement Learning
TABLE VII: Classification accuracy of unimodality in LSTM.
['GloVe', 'GloVe', 'Weibo  [BOLD] 75.39', 'It168 81.82', 'Chn2000  [BOLD] 84.54', 'Review-4  [BOLD] 87.46', 'Review-5  [BOLD] 86.94']
The training procedure of our DISA network is as follows. Firstly, we skip the policy network and directly train the LSTM critic network with the training objective as Eq. Secondly, we fix the parameters of the LSTM critic network and train the policy network with the training objective as Eq. Lastly, we co-train all the modules together until convergence. For the cases when no phonetic feature/embedding is involved, for example pure textual or visual features, only the LSTM is trained and tested. For textual embeddings, we have compared with state-of-the-art embedding methdos including GloVe, skip-gram, CBOW, charCBOW, charSkipGram and Hsentic. This is due to the fact that they successfully encoded the semantics and dependency between characters. We also find that charCBOW and charSkipGram methods perform quite close to the original CBOW and Skip-gram methods. They perform slightly but not constantly better than their baselines. We conjecture this could be caused by the relatively small size of our training corpus compared to the original Chinese Wikipedia Dump training corpus. With the corpus size increased, all embedding methods are expected to have improved performance. It is without doubts, though, that the corpus we used still presents a fair platform for all methods to compare.