Sentiment Domain Adaptation with Multiple Sources

Abstract

Domain adaptation is an important research topic in sentiment analysis area. Existing domain adaptation methods usually transfer sentiment knowledge from only one source domain to target domain. In this paper, we propose a new domain adaptation approach which can exploit sentiment knowledge from multiple source domains. We first extract both global and domain-specific sentiment knowledge from the data of multiple source domains using multi-task learning. Then we transfer them to target domain with the help of words' sentiment polarity relations extracted from the unlabeled target domain data. The similarities between target domain and different source domains are also incorporated into the adaptation process. Experimental results on benchmark dataset show the effectiveness of our approach in improving cross-domain sentiment classification performance.

Learning to select data for transfer learning with Bayesian Optimization

“…However, adaptation between two dissimilar domains is often undesirable, as it may lead to negative transfer (Rosenstein et al, 2005). Only recently, many-to-one adaptation (Mansour, 2009;Wu and Huang, 2016) has received some attention, as it replicates the realistic scenario of multiple source domains where performance on the target domain is the foremost objective.…”

“…As a reference, we also list the performance of the state-of-the-art multi-domain adaptation approach (Wu and Huang, 2016), which shows that task-independent data selection is in fact competitive with a task-specific, heuristic state-of-the-art domain adaptation approach. In fact our transferred similarity+diversity feature (E->D) outperforms the state-of-the-art (Wu and Huang, 2016) on DVD. This is encouraging as previous work (Remus, 2012) has shown that data selection and domain adaptation can be complementary.…”

“…Remus (2012) apply Jensen-Shannon divergence to select training examples for sentiment analysis. Finally, Wu and Huang (2016) propose a similarity metric based on a sentiment graph. We test previously explored similarity metrics and complement them with diversity.…”

“…Only recently research considered using multiple sources. Such studies are rare and typically rely on specific model transfer approaches (Mansour, 2009;Wu and Huang, 2016).…”

Sentiment Reviews Classification using Hybrid Feature Selection

“…Wu and Huang proposed new domain adaptation approach which maps sentiments from multiple source domains to target domain using sentiment graphs [21]. Sanju and Mirnalinee extracted unigram and bigram features from the reviews and selected best opinion features by computing relevance score and obtained 78% and 78.85% of accuracy using DVD and Books reviews respectively [22].…”