Cross-lingual sentiment classification using semi-supervised learning

Cross-lingual sentiment classification aims to utilize annotated sentiment resources in one language for text sentiment classification in another language. Automatic machine translation services are the most commonly used tools to directly project information from one language into another. However, different term distribution between translated and original documents, translation errors and different intrinsic structure of documents in various languages are the problems that lead to low performance in sentiment classification. Furthermore, due to the existence of different linguistic terms in different languages, translated documents cannot cover all vocabularies which exist in the original documents. The aim of this thesis is to propose an enhanced framework for cross-lingual sentiment classification to overcome all the aforementioned problems in order to improve the classification performance. Combination of active learning and semi-supervised learning in both single view and bi-view frameworks is proposed to incorporate unlabelled data from the target language in order to reduce term distribution divergence. Using bi-view documents can partially alleviate the negative effects of translation errors. Multi-view semisupervised learning is also used to overcome the problem of low term-coverage through employing multiple source languages. Features that are extracted from multiple source languages can cover more vocabularies from test data and consequently, more sentimental terms can be used in the classification process. Content similarities of labelled and unlabelled documents are used through graphbased semi-supervised learning approach to incorporate the structure of documents in the target language into the learning process. Performance evaluation performed on sentiment data sets in four different languages certifies the effectiveness of the proposed approaches in comparison to the well-known baseline classification methods. The experiments show that incorporation of unlabelled data from the target language can effectively improve the classification performance. Experimental results also show that using multiple source languages in the multi-view learning model outperforms other methods. The proposed framework is flexible enough to be applied on any new language, and therefore, it can be used to develop multilingual sentiment analysis systems

A comparative study of Bayesian models for unsupervised sentiment detection

This paper presents a comparative study of three closely related Bayesian models for unsupervised document level sentiment classification, namely, the latent sentiment model (LSM), the joint sentimenttopic (JST) model, and the Reverse-JST model. Extensive experiments have been conducted on two corpora, the movie review dataset and the multi-domain sentiment dataset. It has been found that while all the three models achieve either better or comparable performance on these two corpora when compared to the existing unsupervised sentiment classification approaches, both JST and Reverse-JST are able to extract sentiment-oriented topics. In addition, Reverse-JST always performs worse than JST suggesting that the JST model is more appropriate for joint sentiment topic detection

Adversarial Training in Affective Computing and Sentiment Analysis: Recent Advances and Perspectives

Over the past few years, adversarial training has become an extremely active research topic and has been successfully applied to various Artificial Intelligence (AI) domains. As a potentially crucial technique for the development of the next generation of emotional AI systems, we herein provide a comprehensive overview of the application of adversarial training to affective computing and sentiment analysis. Various representative adversarial training algorithms are explained and discussed accordingly, aimed at tackling diverse challenges associated with emotional AI systems. Further, we highlight a range of potential future research directions. We expect that this overview will help facilitate the development of adversarial training for affective computing and sentiment analysis in both the academic and industrial communities

Fidelity-Weighted Learning

Training deep neural networks requires many training samples, but in practice training labels are expensive to obtain and may be of varying quality, as some may be from trusted expert labelers while others might be from heuristics or other sources of weak supervision such as crowd-sourcing. This creates a fundamental quality versus-quantity trade-off in the learning process. Do we learn from the small amount of high-quality data or the potentially large amount of weakly-labeled data? We argue that if the learner could somehow know and take the label-quality into account when learning the data representation, we could get the best of both worlds. To this end, we propose "fidelity-weighted learning" (FWL), a semi-supervised student-teacher approach for training deep neural networks using weakly-labeled data. FWL modulates the parameter updates to a student network (trained on the task we care about) on a per-sample basis according to the posterior confidence of its label-quality estimated by a teacher (who has access to the high-quality labels). Both student and teacher are learned from the data. We evaluate FWL on two tasks in information retrieval and natural language processing where we outperform state-of-the-art alternative semi-supervised methods, indicating that our approach makes better use of strong and weak labels, and leads to better task-dependent data representations.Comment: Published as a conference paper at ICLR 201