text: Every culture and language is unique. Our work expressly focuses on the uniqueness of culture and language in relation to human affect, specifically sentiment and emotion semantics, and how they manifest in social multimedia. We develop sets of sentiment- and emotion-polarized visual concepts by adapting semantic structures called adjective-noun pairs, originally introduced by (2013), but in a multilingual context. We propose a new language-dependent method for automatic discovery of these adjective-noun constructs. We show how this pipeline can be applied on a social multimedia platform for the creation of a large-scale multilingual visual sentiment concept ontology (MVSO). Unlike the flat structure in (2013), our unified ontology is organized hierarchically by multilingual clusters of visually detectable nouns and subclusters of emotionally biased versions of these nouns. In addition, we present an image-based prediction task to show how generalizable language-specific models are in a multilingual context. A new, publicly available dataset of >15.6K sentiment-biased visual concepts across 12 languages with language-specific detector banks, >7.36M images and their metadata is also released.
  @cite_13: Mood is a popular access point in music digital libraries and online music repositories, and is often represented as numerical values in a small number of emotion-related dimensions (e.g., valence and arousal). As music mood is recognized as culturally dependent, this study investigates whether regression models built with music data in one culture can be applied to music in another culture. Results indicate that cross-cultural predictions of both valence and arousal values are feasible.
  @cite_29: Understanding and meeting the needs of a broad range of music users across different cultures and languages are central in designing a global music digital library. This exploratory study examines cross-cultural multilingual music information seeking behaviors and reveals some important characteristics of these behaviors by analyzing 107 authentic music information queries from a Korean knowledge search portal Naver 지식 (knowledge) iN and 150 queries from Google Answers website. We conclude that new sets of access points must be developed to accommodate music queries that cross cultural or language boundaries.
  @cite_10: There is a growing interest in mining opinions using sentiment analysis methods from sources such as news, blogs and product reviews. Most of these methods have been developed for English and are difficult to generalize to other languages. We explore an approach utilizing state-of-the-art machine translation technology and perform sentiment analysis on the English translation of a foreign language text. Our experiments indicate that (a) entity sentiment scores obtained by our method are statistically significantly correlated across nine languages of news sources and five languages of a parallel corpus; (b) the quality of our sentiment analysis method is largely translator independent; (c) after applying certain normalization techniques, our entity sentiment scores can be used to perform meaningful cross-cultural comparisons.
  @cite_25: This paper discusses learning multilingual subjective language via cross-lingual projections.
summary: Other work in cross-lingual research comes from text sentiment analysis and music information retrieval. In @cite_10 and @cite_25 , they developed multilingual methods for international text sentiment analysis in online blogs and news articles, respectively. In @cite_29 and @cite_13 , they presented approaches to indexing digital music libraries with music from multiple languages. Specific to emotion, @cite_13 tried to highlight differences between languages by building models for predicting the musical mood and then cross-predicting in other languages. Unlike these works, we propose a multimedia-driven approach for cross-cultural visual sentiment analysis in the context of online image collections.