In this paper, we develop multilingual supervised latent Dirichlet allocation (MLSLDA), a probabilistic generative model that allows insights gleaned from one language's data to inform how the model captures properties of other languages. MLSLDA accomplishes this by jointly modeling two aspects of text: how multilingual concepts are clustered into thematically coherent topics and how topics associated with text connect to an observed regression variable (such as ratings on a sentiment scale). Concepts are represented in a general hierarchical framework that is flexible enough to express semantic ontologies, dictionaries, clustering constraints, and, as a special, degenerate case, conventional topic models. Both the topics and the regression are discovered via posterior inference from corpora. We show MLSLDA can build topics that are consistent across languages, discover sensible bilingual lexical correspondences, and leverage multilingual corpora to better predict sentiment. Sentiment analysis (Pang and Lee, 2008) offers the promise of automatically discerning how people feel about a product, person, organization, or issue based on what they write online, which is potentially of great value to businesses and other organizations. However, the vast majority of sentiment resources and algorithms are limited to a single language, usually English (Wilson, 2008; Baccianella and Sebastiani, 2010) . Since no single language captures a majority of the content online, adopting such a limited approach in an increasingly global community risks missing important details and trends that might only be available when text in multiple languages is taken into account. Up to this point, multiple languages have been addressed in sentiment analysis primarily by transferring knowledge from a resource-rich language to a less rich language (Banea et al., 2008) , or by ignoring differences in languages via translation into English (Denecke, 2008) . These approaches are limited to a view of sentiment that takes place through an English-centric lens, and they ignore the potential to share information between languages. Ideally, learning sentiment cues holistically, across languages, would result in a richer and more globally consistent picture. In this paper, we introduce Multilingual Supervised Latent Dirichlet Allocation (MLSLDA), a model for sentiment analysis on a multilingual corpus. MLSLDA discovers a consistent, unified picture of sentiment across multiple languages by learning "topics," probabilistic partitions of the vocabulary that are consistent in terms of both meaning and relevance to observed sentiment. Our approach makes few assumptions about available resources, requiring neither parallel corpora nor machine translation. The rest of the paper proceeds as follows. In Section 1, we describe the probabilistic tools that we use to create consistent topics bridging across languages and the MLSLDA model. In Section 2, we present the inference process. We discuss our set of semantic bridges between languages in Section 3, and our experiments in Section 4 demonstrate that this approach functions as an effective multilingual topic model, discovers sentiment-biased topics, and uses multilingual corpora to make better sentiment predictions across languages. Sections 5 and 6 discuss related research and discusses future work, respectively. 45
Boyd-Graber and Resnik REF built Latent Dirichlet Allocation models to investigate how multilingual concepts are clustered into topics.
Holistic Sentiment Analysis Across Languages: Multilingual Supervised Latent Dirichlet Allocation