Aspect Term Extraction using Graph-based Semi-Supervised Learning
This work addresses aspect-based sentiment analysis for domains like restaurants and laptops, but it is incremental as it builds on existing graph-based methods with efficiency improvements.
The paper tackles aspect term extraction by proposing a graph-based semi-supervised learning approach that uses label spreading and kNN for efficiency, achieving good precision, recall, and accuracy with limited labeled data on restaurant and laptop datasets.
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.