Image Based Review Text Generation with Emotional Guidance
This addresses a gap in online shopping applications where review generation could aid selection, though it appears incremental.
The paper tackles the problem of generating product review texts from images and ratings, rather than just describing image content. They adapted an existing image-captioning model to incorporate non-image features and obtained effective primary results.
In the current field of computer vision, automatically generating texts from given images has been a fully worked technique. Up till now, most works of this area focus on image content describing, namely image-captioning. However, rare researches focus on generating product review texts, which is ubiquitous in the online shopping malls and is crucial for online shopping selection and evaluation. Different from content describing, review texts include more subjective information of customers, which may bring difference to the results. Therefore, we aimed at a new field concerning generating review text from customers based on images together with the ratings of online shopping products, which appear as non-image attributes. We made several adjustments to the existing image-captioning model to fit our task, in which we should also take non-image features into consideration. We also did experiments based on our model and get effective primary results.