Abstract:
Sentiment analysis plays an important role in e-commerce, as it allows the industries
to better understand the customer experience and its brand value. Aspect Based
Sentiment Analysis (ABSA) is a ne-grained version of sentiment analysis. ABSA
not only focuses on analysing opinions in a given review but also looks into the
several aspects and their sentiments thus giving a much clearer understanding. Aspect
extraction is a crucial part of this ABSA task on which much attention has not been
paid until recent years. Limited number of training data has made the task further
challenging. This project addresses the problem of extraction of aspects from review
comments and thereby attempts to improve the state of the art results in ABSA.
For language modeling, BERT is used and it's netuned on a novel Neurosyntactic
model architecture. POS and dependency tags are used along review comments for
extraction of aspect terms. Experiments conducted on SemEval dataset show that
the proposed architecture achieves the state of the art results on the dataset.
Description:
Dissertation under the supervision Utpal Garain,Professor & Debapriyo Majumdar, Assistant Professor,
CVPR
