  Aspect-based sentiment analysis (ABSA), exploring sentiment polarity of
aspect-given sentence, is a fine-grained task in the field of nature language
processing. Previously researches typically tend to predict polarity based on
the meaning of aspect and opinions. However, those approaches mainly focus on
considering relations implicitly at the word level, ignore the historical
impact of other positional words when the aspect appears in a certain position.
Therefore, we propose a Position-based Contributive Embeddings (PosCE) to
highlight the historical reference to special position aspect. Contribution of
each positional words to the polarity is similar to the process of fairly
distributing gains to several actors working in coalition (game theory).
Therefore, we quote from the method of Shapley Value and finally gain PosCE to
enhance the aspect-based representation for ABSA task. Furthermore, the PosCE
can also be used for improving performances on multimodal ABSA task. Extensive
experiments on both text and text-audio level using SemEval dataset show that
the mainstream models advance performance in accuracy and F1 (increase 2.82%
and 4.21% on average respectively) by applying our PosCE.
