-
Adaptive Semi-supervised Learning for Cross-domain Sentiment Classification
We consider the cross-domain sentiment classification problem, where a s...
read it
-
Weakly-Supervised Deep Learning for Domain Invariant Sentiment Classification
The task of learning a sentiment classification model that adapts well t...
read it
-
Recommendation Chart of Domains for Cross-Domain Sentiment Analysis:Findings of A 20 Domain Study
Cross-domain sentiment analysis (CDSA) helps to address the problem of d...
read it
-
Curriculum CycleGAN for Textual Sentiment Domain Adaptation with Multiple Sources
Sentiment analysis of user-generated reviews or comments on products and...
read it
-
Missing-Class-Robust Domain Adaptation by Unilateral Alignment for Fault Diagnosis
Domain adaptation aims at improving model performance by leveraging the ...
read it
-
Multi-Source Domain Adaptation for Text Classification via DistanceNet-Bandits
Domain adaptation performance of a learning algorithm on a target domain...
read it
-
How to Pick the Best Source Data? Measuring Transferability for Heterogeneous Domains
Given a set of source data with pre-trained classification models, how c...
read it
Distance Based Source Domain Selection for Sentiment Classification
Automated sentiment classification (SC) on short text fragments has received increasing attention in recent years. Performing SC on unseen domains with few or no labeled samples can significantly affect the classification performance due to different expression of sentiment in source and target domain. In this study, we aim to mitigate this undesired impact by proposing a methodology based on a predictive measure, which allows us to select an optimal source domain from a set of candidates. The proposed measure is a linear combination of well-known distance functions between probability distributions supported on the source and target domains (e.g. Earth Mover's distance and Kullback-Leibler divergence). The performance of the proposed methodology is validated through an SC case study in which our numerical experiments suggest a significant improvement in the cross domain classification error in comparison with a random selected source domain for both a naive and adaptive learning setting. In the case of more heterogeneous datasets, the predictability feature of the proposed model can be utilized to further select a subset of candidate domains, where the corresponding classifier outperforms the one trained on all available source domains. This observation reinforces a hypothesis that our proposed model may also be deployed as a means to filter out redundant information during a training phase of SC.
READ FULL TEXT
Comments
There are no comments yet.