Latent sentiment model for weakly-supervised cross-lingual sentiment classification

In this paper, we present a novel weakly-supervised method for crosslingual sentiment analysis. In specific, we propose a latent sentiment model (LSM) based on latent Dirichlet allocation where sentiment labels are considered as topics. Prior information extracted from English sentiment lexicons through machine translation are incorporated into LSM model learning, where preferences on expectations of sentiment labels of those lexicon words are expressed using generalized expectation criteria. An efficient parameter estimation procedure using variational Bayes is presented. Experimental results on the Chinese product reviews show that the weakly-supervised LSM model performs comparably to supervised classifiers such as Support vector Machines with an average of 81% accuracy achieved over a total of 5484 review documents. Moreover, starting with a generic sentiment lexicon, the LSM model is able to extract highly domainspecific polarity words from text

Cross-language Text Classification with Convolutional Neural Networks From Scratch

Cross language classification is an important task in multilingual learning, where documents in different languages often share the same set of categories. The main goal is to reduce the labeling cost of training classification model for each individual language. The novel approach by using Convolutional Neural Networks for multilingual language classification is proposed in this article. It learns representation of knowledge gained from languages. Moreover, current method works for new individual language, which was not used in training. The results of empirical study on large dataset of 21 languages demonstrate robustness and competitiveness of the presented approach

Cross-Lingual Adaptation using Structural Correspondence Learning

Cross-lingual adaptation, a special case of domain adaptation, refers to the transfer of classification knowledge between two languages. In this article we describe an extension of Structural Correspondence Learning (SCL), a recently proposed algorithm for domain adaptation, for cross-lingual adaptation. The proposed method uses unlabeled documents from both languages, along with a word translation oracle, to induce cross-lingual feature correspondences. From these correspondences a cross-lingual representation is created that enables the transfer of classification knowledge from the source to the target language. The main advantages of this approach over other approaches are its resource efficiency and task specificity. We conduct experiments in the area of cross-language topic and sentiment classification involving English as source language and German, French, and Japanese as target languages. The results show a significant improvement of the proposed method over a machine translation baseline, reducing the relative error due to cross-lingual adaptation by an average of 30% (topic classification) and 59% (sentiment classification). We further report on empirical analyses that reveal insights into the use of unlabeled data, the sensitivity with respect to important hyperparameters, and the nature of the induced cross-lingual correspondences

Sentiment Analysis: Comparative Analysis of Multilingual Sentiment and Opinion Classification Techniques

Sentiment analysis and opinion mining have become emerging topics of research in recent years but most of the work is focused on data in the English language. A comprehensive research and analysis are essential which considers multiple languages, machine translation techniques, and different classifiers. This paper presents, a comparative analysis of different approaches for multilingual sentiment analysis. These approaches are divided into two parts: one using classification of text without language translation and second using the translation of testing data to a target language, such as English, before classification. The presented research and results are useful for understanding whether machine translation should be used for multilingual sentiment analysis or building language specific sentiment classification systems is a better approach. The effects of language translation techniques, features, and accuracy of various classifiers for multilingual sentiment analysis is also discussed in this study