JEAM: A Novel Model for Cross-Domain Sentiment Classification Based on Emotion Analysis

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 2503–2508, Lisbon, Portugal, 17-21 September 2015. c⃝2015 Association for Computational Linguistics. JEAM: A Novel Model for Cross-Domain Sentiment Classification Based on Emotion Analysis Kun-Hu Luo, Zhi-Hong Deng, Liang-Chen Wei, Hongliang Yu School of Electronic Engineering and Computer Science Peking University, Beijing, China {dr.tiger126@gmail.com, zhdeng@cis.pku.edu.cn, pkuhaywire@gmail.com, yuhongliang324@gmail.com} Abstract Cross-domain sentiment classification (CSC) aims at learning a sentiment classifier for unlabeled data in the target domain based on the labeled data from a different source domain. Due to the differences of data distribution of two domains in terms of the raw features, the CSC problem is difficult and challenging. Previous researches mainly focused on concepts mining by clustering words across data domains, which ignored the importance of authors’ emotion contained in data, or the different representations of the emotion between domains. In this paper, we propose a novel framework to solve the CSC problem, by modelling the emotion across domains. We first develop a probabilistic model named JEAM to model author’s emotion state when writing. Then, an EM algorithm is introduced to solve the likelihood maximum problem and to obtain the latent emotion distribution of the author. Finally, a supervised learning method is utilized to assign the sentiment polarity to a given online review. Experiments show that our approach is effective and outperforms state-of-the-art approaches. 1 Introduction Cross-domain sentiment classification (CSC) is the task that learns a sentiment classifier for unlabeled data in the target domain based on the labeled data from the source domain. With the increasing amount of opinion information  Corresponding author available on the Internet, CSC has become a hot spot in recent years. Traditional machine learning algorithms often train a classifier utilizing the labeled data for CSC. However, in some practical cases, we may have many labeled data for some domains (source domains) but very few or no labeled data for other domains (target domains). Due to the differences of the distribution of two domains in terms of raw features, e.g. raw term frequency, the classifier trained from the source domain often performs badly on the target domain. To overcome this issue, several feature-based studies have been proposed to improve the sentiment classification domain adaptation [Zhuang et al., 2013; He et al., 2011; Gao and Li, 2011; Li et al., 2012; Dai et al., 2007; Zhuang et al., 2010; Pan et al., 2010; Wang et al., 2011; Long et al., 2012; Lin and He, 2009]. Existing studies build various generative models to solve the domain adaptation problems for CSC. In most cases, the models are trained by using the whole corpora without specifying on the sentiment of the texts. For example, [Zhuang et al., 2013] propose a general framework HIDC to mine high-level concepts (e.g. word clusters) across various domains. However, their learned concepts contain many topics not restricted to the sentiment. On the other hand, some researchers focus on the usage of the sentiment in CSC study [Mitra et al., 2013a; Mitra et …

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