Aspect Sentiment Model for Micro Reviews
TABLE V: Aspect category assignment metrics on Yelp Restaurant dataset
['Metric', 'JST', 'ASUM', 'JAST', 'MicroASM']
Overall, MicroASM significantly outperforms all the other models. The results also show that JAST performs the worst among the models. This is especially very apparent on the agreeability metric, which means that the ϕ distributions extracted by JAST is hard to interpret. One may consider applying larger seed lists, which can be done with insignificant increase in time and space complexity, but with the following complications. First, a good seed list must only have aspect-independent sentiments such as good and bad. When starting with a larger list, it is inevitable that aspect-specific sentiments (e.g. fast is positive in fast performance but negative in fast battery consumption) will be included in the list, with negative effects in the process. Second, a larger seed list may not be available in other low-resource languages such as Korean. Hence in this experiment, a smaller set of sentiment seeds is desirable for aspect sentiment models.