Using Informative Score for Instance Selection Strategy in Semi-Supervised Sentiment Classification

Sentiment classification is a useful tool to classify reviews about sentiments and attitudes towards a product or service. Existing studies heav-ily rely on sentiment classification methods that require fully annotated inputs. However, there is limited labelled text available, making the acquire-ment process of the fully annotated input costly and labour-intensive. Lately, semi-supervised methods emerge as they require only partially labelled input but perform comparably to supervised methods. Nevertheless, some works reported that the performance of the semi-supervised model degraded after adding unlabelled instances into training. Literature also shows that not all unlabelled instances are equally useful; thus identifying the informative unlabelled instances is beneficial in training a semi-supervised model. To achieve this, an informative score is proposed and incorporated into semi -supervised sentiment classification. The evaluation is performed on a semi -supervised method without an informative score and with an informative score. By using the informative score in the instance selection strategy to iden-tify informative unlabelled instances, semi-supervised models perform better compared to models that do not incorporate informative scores into their training. Although the performance of semi-supervised models incorporated with an informative score is not able to surpass the supervised models, the results are still found promising as the differences in performance are subtle with a small difference of 2% to 5%, but the number of labelled instances used is greatly reduced from 100% to 40%. The best finding of the proposed instance selection strategy is achieved when incorporating an informative score with a baseline confidence score at a 0.5:0.5 ratio using only 40% labelled data.