  Aspect Based Sentiment Analysis (ABSA) is the task of identifying sentiment
polarity of a text given another text segment or aspect. In ABSA, a text can
have multiple sentiments depending upon each aspect. Aspect Term Sentiment
Analysis (ATSA) is a subtask of ABSA, in which aspect terms are contained
within the given sentence. Most of the existing approaches proposed for ATSA,
incorporate aspect information through a different subnetwork thereby
overlooking the advantage of aspect terms' presence within the sentence. In
this paper, we propose a model that leverages the positional information of the
aspect. The proposed model introduces a decay mechanism based on position. This
decay function mandates the contribution of input words for ABSA. The
contribution of a word declines as farther it is positioned from the aspect
terms in the sentence. The performance is measured on two standard datasets
from SemEval 2014 Task 4. In comparison with recent architectures, the
effectiveness of the proposed model is demonstrated.
