Image Based Review Text Generation with Emotional Guidance
Table 1: Sample test results of our experiments
['Product image', 'Rating', 'Generated review texts']
It turns out that out model can successfully generate review texts related with correct product information and the emotion of generated texts can change with different review ratings. Our training dataset is relatively small due to time and source limiting, so the result still faces some problems occasionally such as repetition of words, misidentification and generating meaningless texts. But definitely our model will work better with larger datasets or more skillful natural language processing methods. We also did ablation experiments of replacing the gLSTM-based attention by “Deterministic Soft Attention” method in [showattendandtell]. But due to the insufficiency of our dataset, the results did not show too much defects, and the superiority of the final model using gLSTM is not that distinguishable from words. For this reason, we did not display the results of ablation experiments, but only displayed the results of our final models from different products and different ratings.