Unsupervised graph-based pattern extraction for multilingual emotion classification

TERMS: textual emotion recognition in multidimensional space

Microblogs generate a vast amount of data in which users express their emotions regarding almost all aspects of everyday life. Capturing affective content from these context-dependent and subjective texts is a challenging task. We propose an intelligent probabilistic model for textual emotion recognition in multidimensional space (TERMS) that captures the subjective emotional boundaries and contextual information embedded in a text for robust emotion recognition. It is implausible with discrete label assignment;therefore, the model employs a soft assignment by mapping varying emotional perceptions in a multidimensional space and generates them as distributions via the Gaussian mixture model (GMM). To strengthen emotion distributions, TERMS integrates a probabilistic emotion classifier that captures the contextual and linguistic information from texts. The integration of these aspects, the context-aware emotion classifier and the learned GMM parameters provide a complete coverage for accurate emotion recognition. The large-scale experimentation shows that compared to baseline and state-of-the-art models, TERMS achieved better performance in terms of distinguishability, prediction, and classification performance. In addition, TERMS provide insights on emotion classes, the annotation patterns, and the models application in different scenarios.

Multilingual Sentiment Analysis: A Systematic Literature Review

With the explosive growth of social media, the online community can freely express their opinions without disclosing their identities. People with hidden agendas can easily post fake opinions to discredit target products, services, politicians, or organizations. With these big data, monitoring opinions and distilling their sentiments remain a formidable task because of the proliferation of diverse sites with a large volume of opinions that are portrayed in multilingual. Therefore, this paper aims to provide a systematic literature review on multilingual sentiment analysis, which summarises the common languages supported in multilingual sentiment analysis, pre-processing techniques, existing sentiment analysis approaches, and evaluation models that have been used for multilingual sentiment analysis. By following the systematic literature review, the findings revealed, most of the models supported two languages, and English is seen as the most used language in sentiment analysis studies. None of the reviewed literature has catered the combination of languages for English, Chinese, Malay, and Hindi language on multilingual sentiment analysis. The common pre-processing techniques for the multilingual domain are tokenization, normalization, capitalization, N-gram, and machine translation. Meanwhile, the sentiment analysis classification techniques for multilingual sentiment are hybrid sentiment analysis, which includes localized language analysis, unsupervised topic clustering, and then followed by multilingual sentiment analysis. In terms of evaluation, most of the studies used precision, recall, and accuracy as the benchmark for the results.