  Aspect based Sentiment Analysis is a major subarea of sentiment analysis.
Many supervised and unsupervised approaches have been proposed in the past for
detecting and analyzing the sentiment of aspect terms. In this paper, a
graph-based semi-supervised learning approach for aspect term extraction is
proposed. In this approach, every identified token in the review document is
classified as aspect or non-aspect term from a small set of labeled tokens
using label spreading algorithm. The k-Nearest Neighbor (kNN) for graph
sparsification is employed in the proposed approach to make it more time and
memory efficient. The proposed work is further extended to determine the
polarity of the opinion words associated with the identified aspect terms in
review sentence to generate visual aspect-based summary of review documents.
The experimental study is conducted on benchmark and crawled datasets of
restaurant and laptop domains with varying value of labeled instances. The
results depict that the proposed approach could achieve good result in terms of
Precision, Recall and Accuracy with limited availability of labeled data.
