Joint Modeling of Opinion Expression Extraction and Attribute Classification

Joint Modeling of Opinion Expression Extraction and Attribute Classification Bishan Yang Department of Computer Science Cornell University bishan@cs.cornell.edu Claire Cardie Department of Computer Science Cornell University cardie@cs.cornell.edu Abstract In this paper, we study the problems of opin- ion expression extraction and expression-level polarity and intensity classification. Tradi- tional fine-grained opinion analysis systems address these problems in isolation and thus cannot capture interactions among the tex- tual spans of opinion expressions and their opinion-related properties. We present two types of joint approaches that can account for such interactions during 1) both learning and inference or 2) only during inference. Exten- sive experiments on a standard dataset demon- strate that our approaches provide substantial improvements over previously published re- sults. By analyzing the results, we gain some insight into the advantages of different joint models. 1 Introduction Automatic extraction of opinions from text has at- tracted considerable attention in recent years. In particular, significant research has focused on ex- tracting detailed information for opinions at the fine- grained level, e.g. identifying opinion expressions within a sentence and predicting phrase-level po- larity and intensity. The ability to extract fine- grained opinion information is crucial in supporting many opinion-mining applications such as opinion summarization, opinion-oriented question answer- ing and opinion retrieval. In this paper, we focus on the problem of identi- fying opinion expressions and classifying their at- tributes. We consider as an opinion expression any subjective expression that explicitly or implic- itly conveys emotions, sentiment, beliefs, opinions (i.e. private states) (Wiebe et al., 2005), and con- sider two key attributes — polarity and intensity — for characterizing the opinions. Consider the sen- tence in Figure 1, for example. The phrases “a bias in favor of” and “being severely criticized” are opin- ion expressions containing positive sentiment with medium intensity and negative sentiment with high intensity, respectively. Most existing approaches tackle the tasks of opin- ion expression extraction and attribute classification in isolation. The first task is typically formulated as a sequence labeling problem, where the goal is to la- bel the boundaries of text spans that correspond to opinion expressions (Breck et al., 2007; Yang and Cardie, 2012). The second task is usually treated as a binary or multi-class classification problem (Wil- son et al., 2005; Choi and Cardie, 2008; Yessenalina and Cardie, 2011), where the goal is to assign a class label to a text fragment (e.g. a phrase or a sen- tence). Solutions to the two tasks can be applied in a pipeline architecture to extract opinion expressions and their attributes. However, pipeline systems suf- fer from error propagation: opinion expression er- rors propagate and lead to unrecoverable errors in attribute classification. Limited work has been done on the joint modeling of opinion expression extraction and attribute clas- sification. Choi and Cardie (2010) first proposed a joint sequence labeling approach to extract opin- ion expressions and label them with polarity and in- tensity. Their approach treats both expression ex- traction and attribute classification as token-level se- 505 Transactions of the Association …

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