The sentiment classification performance relies on high-quality sentiment resources. However, these resources are imbalanced in different languages. Cross-language sentiment classification (CLSC) can leverage the rich resources in one language (source language) for sentiment classification in a resource-scarce language (target language). Bilingual embeddings could eliminate the semantic gap between two languages for CLSC, but ignore the sentiment information of text. This paper proposes an approach to learning bilingual sentiment word embeddings (BSWE) for English-Chinese CLSC. The proposed BSWE incorporate sentiment information of text into bilingual embeddings. Furthermore, we can learn high-quality BSWE by simply employing labeled corpora and their translations, without relying on largescale parallel corpora. Experiments on NLP&CC 2013 CLSC dataset show that our approach outperforms the state-of-theart systems.

/pdf/learning-bilingual-sentiment-word-embeddings-for-cross-1w3ediwjm8.pdf

Learning Bilingual Sentiment Word Embeddings for Cross-language Sentiment Classification

Machine learning and deep learning for sentiment analysis across languages: A survey

NOTE FROM ACM: It has been determined that this article plagiarized the contents of a previously published paper. Therefore ACM has shut off access to this paper. This article has been removed from the ACM Digital Library because it was found to plagiarize an earlier work written by Xiangbo Shu, Guo-Jin Qi, Jinhui Tang, and Jingdong Wang published by ACM and entitled DOI:http://doi.acm.org/10.1145/2733373.2806216 Weakly-Shared Deep Transder Networks for Heterogeneous-Domain Knowledge Propagation. In Proceedings of the 23rd ACM International Conference on Multimedia (MM '15). ACM, New York, NY, USA, 35-44. For further information, contact the ACM Director of Publications.

Transfer Learning for Cross-Lingual Sentiment Classification with Weakly Shared Deep Neural Networks

Sentiment classification aims to determine the sentiment polarity expressed in a text. In online customer reviews, the sentiment polarities of words are usually dependent on the corresponding aspects. For instance, in mobile phone reviews, we may expect the long battery time but not enjoy the long response time of the operating system. Therefore, it is necessary and appealing to consider aspects when conducting sentiment classification. Probabilistic topic models that jointly detect aspects and sentiments have gained much success recently. However, most of the existing models are designed to work well in a language with rich resources. Directly applying those models on poor-quality corpora often leads to poor results. Consequently, a potential solution is to use the cross-lingual topic model to improve the sentiment classification for a target language by leveraging data and knowledge from a source language. However, the existing cross-lingual topic models are not suitable for sentiment classification because sentiment factors are not considered therein. To solve these problems, we propose for the first time a novel cross-lingual topic model framework which can be easily combined with the state-of-the-art aspect/sentiment models. Extensive experiments in different domains and multiple languages demonstrate that our model can significantly improve the accuracy of sentiment classification in the target language.

An unsupervised cross-lingual topic model framework for sentiment classification

Sentiment classification system relies on high-quality emotional resources. However, these resources are imbalanced in different languages. The way of how to leverage rich labeled data of one language (source language) for the sentiment classification of resource-poor language (target language), namely cross-lingual sentiment classification (CLSC), becomes a focus topic. This paper utilizes rich English resources for Chinese sentiment classification. To eliminate the language gap between English and Chinese, this paper proposes a combination CLSC approach based on denoising autoencoder. First, two classifiers based on denoising autoencoder are learned respectively in English and Chinese views by using English corpus and English-to-Chinese corpus. Second, we classify Chinese test data and Chinese-to-English test data with the two classifiers trained in the two views. Last, the final sentiment classification results are obtained by the combination of the two results in two views. Experiments are carried out on NLP&CC 2013 CLSC dataset including book, DVD and music categories. The results show that our approach achieves the accuracy of 80.02%, which outperforms the current state-of-the-art systems.

Cross-Lingual Sentiment Classification Based on Denoising Autoencoder

Cross-lingual sentiment classification aims to predict the sentiment orientation of a text in a language (named as the target language) with the help of the resources from another language (named as the source language). However, current cross-lingual performance is normally far away from satisfaction due to the huge difference in linguistic expression and social culture. In this paper, we suggest to perform active learning for cross-lingual sentiment classification, where only a small scale of samples are actively selected and manually annotated to achieve reasonable performance in a short time for the target language. The challenge therein is that there are normally much more labeled samples in the source language than those in the target language. This makes the small amount of labeled samples from the target language flooded in the aboundance of labeled samples from the source language, which largely reduces their impact on cross-lingual sentiment classification. To address this issue, we propose a data quality controlling approach in the source language to select high-quality samples from the source language. Specifically, we propose two kinds of data quality measurements, intra- and extra-quality measurements, from the certainty and similarity perspectives. Empirical studies verify the appropriateness of our active learning approach to cross-lingual sentiment classification.

/pdf/active-learning-for-cross-lingual-sentiment-classification-1ycxjvxbis.pdf

Active Learning for Cross-Lingual Sentiment Classification

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Active Learning for Cross-Lingual Sentiment Classification