Investigation of Sentiment Controllable Chatbot
TABLE XV: Sentiment classifiers of different NN architectures on PTT dataset.
['Segm. Struct.', 'Word-based CNN', 'Word-based GRU-last', 'Word-based GRU-avg', 'Word-based GRU-last-2', 'Character-based CNN', 'Character-based GRU-last', 'Character-based GRU-avg']
In this section, we switched to the PTT dataset to demonstrate the performance of the above four models. To train a sentiment classifier, we also tried CNN, GRU-last, and GRU-avg on different word segmentation methods at first. However, as the dirty PTT dataset resulted in poor sentiment classifier performance, we improved the sentiment classifier using the following three steps. Second, we picked the more credible data (higher absolute scores) by GRU-last, shrinking the data from 1,402,303 to 1,149,000. Finally, we trained a new sentiment classifier on the remaining data using GRU-last-2. This final sentiment classifier was then used in the four models. GRU-last-2 here indicates the same architecture as GRU-last but not the same model.