A Data-driven Neural Network Architecture for Sentiment Analysis
Table 3: Accuracies of top five network structures
['[BOLD] Network', '[BOLD] Mlpn', '[BOLD] Sent', '[BOLD] Imdb', '[BOLD] Phon', '[BOLD] Yelp']
We performed various experiments using the setup and datasets described above. In general we see that simple network structures of just one or two convolution and max-pooling stacks yield very good results. Highest score achieved on phone reviews seems excellent. The one on song lyrics on the other hand, is not that good. In fact, we did not find any experimental result on these two datasets to compare with. Peak score on sentence polarity dataset is 79.89%. The many authors that have used it, report accuracy scores from 76 to 82%. The record result reported in the literature is actually 83.1 %. It is reported by Zhao et al. Their model uses gating networks to form a hierarchy of representations from words to sentences and finally to entire document. Best score obtained on movie reviews dataset is 90.68%. Top score reported in literature is 92.23 %. For both results, they utilized deep and highly complex neural networks. Even though lower that literature records, our results were reached with very simple and quick models of less than 200,000 parameters. Contrary, the top models we compared with are complex neural networks with millions of trainable parameters. Regarding RQ1, we see that datasets of longer documents ( Yelp, Imdb and Mlpn) perform better on models with longer pool regions. Top scores we got on those datasets were reached with aggregate max-pooling coefficients r=3 x 3 x 3=27, r=5 x 5=25 and r=25 respectively. Contrary, Phon and Sent datasets that contain shorter texts, reach peak scores on models of smaller region. Actually r is the parameter that dictates size of final feature maps (l=ceil(n−k+1r)). According to the results, highest accuracy scores are always reached with l values that are within 6−18.