Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In the literature, various approaches have been proposed to address the domain adaptation problem in sentiment classification (also called cross-domain sentiment classification). Hover, the adaptation performance normally much suffers when the data distributions in the source and target domains differ significantly. ,  suggest to perform active learning for cross-domain sentiment classification by actively selecting a small amount of labeled data in the target domain. Accordingly,  propose an novel active learning approach for cross-domain sentiment classification. First,  train two individual classifiers, i.e., the source and target classifiers with the labeled data from the source and target respectively. Then, the two classifiers are employed to select informative samples with the selection strategy of Query By Committee (QBC). Third, the two classifier is combined to make the classification decision. Importantly, the two classifiers are trained by fully exploiting the unlabeled data in the target domain with the label propagation (LP) algorithm. Empirical studies demonstrate the effectiveness of our active learning approach for cross-domain sentiment classification over some strong baselines.
['Shoushan Li', 'Yunxia Xue', 'Zhongqing Wang', 'Guodong Zhou']
I am writing a research paper on a idea Active learning for cross-domain sentiment classification
[{"from": "human", "value": "I am writing a research paper on a idea Active learning for cross-domain sentiment classification"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In the literature, various approaches have been proposed to address the domain adaptation problem in sentiment classification (also called cross-domain sentiment classification). Hover, the adaptation performance normally much suffers when the data distributions in the source and target domains differ significantly. ,  suggest to perform active learning for cross-domain sentiment classification by actively selecting a small amount of labeled data in the target domain. Accordingly,  propose an novel active learning approach for cross-domain sentiment classification. First,  train two individual classifiers, i.e., the source and target classifiers with the labeled data from the source and target respectively. Then, the two classifiers are employed to select informative samples with the selection strategy of Query By Committee (QBC). Third, the two classifier is combined to make the classification decision. Importantly, the two classifiers are trained by fully exploiting the unlabeled data in the target domain with the label propagation (LP) algorithm. Empirical studies demonstrate the effectiveness of our active learning approach for cross-domain sentiment classification over some strong baselines."}]