Cross-lingual sentiment classification in low-resource B engali language (2020)

Abstract: Sentiment analysis research in low-resource languages such as Bengali is still unexplored due to the scarcity of annotated data and the lack of text processing tools. Therefore, in this work, we focus on generating resources and showing the applicability of the cross-lingual sentiment analysis approach in Bengali. For benchmarking, we created and annotated a comprehensive corpus of around 12000 Bengali reviews. To address the lack of standard text-processing tools in Bengali, we leverage resources from English utilizing machine translation. We determine the performance of supervised machine learning (ML) classifiers in machine-translated English corpus and compare it with the original Bengali corpus. Besides, we examine sentiment preservation in the machine-translated corpus utilizing Cohen’s Kappa and Gwet’s AC1. To circumvent the laborious data labeling process, we explore lexicon-based methods and study the applicability of utilizing cross-domain labeled data from the resource-rich language. We find that supervised ML classifiers show comparable performances in Bengali and machine-translated English corpus. By utilizing labeled data, they achieve 15%-20% higher F1 scores compared to both lexicon-based and transfer learning-based methods. Besides, we observe that machine translation does not alter the sentiment polarity of the review for most of the cases. Our experimental results demonstrate that the machine translation based cross-lingual approach can be an effective way for sentiment classification in Bengali.

## introduction sentiment analysis classifies the semantic orientation of a text. with the rapid growth of usergenerated content, nowadays, it is essential to determine user opinions, attitudes, and feelings from the textual data. in literature, researchers identified sentiment orientations of the text in various levels, such as document, sentence, or aspect. researchers employed both the machine learning-based and lexicon-based approaches for sentiment analysis. utilizing labeled data, supervised ml classifiers such as naive bayes (nb), maximum entropy (me), support vector machines (svm), etc. @xcite and deep learning-based classifiers @xcite have been employed by the researchers for sentiment classification. though the lexicon-based methods @xcite do not require labeled data, they suffer from the lexicon coverage problem and are not robust to deal with the ambiguity and linguistic variations of natural languages. though english and few other languages enjoy ample resources for sentiment analysis, such resources are not available in many other languages. cross-lingual sentiment classification aims to leverage resources like labeled data, polarity lexicons, contextual valence shifters, modifiers, etc. from resource-rich languages (typically english) to classify the sentiment polarity of the text written in a low-resource language (such as bengali). for language mapping, several approaches such as machine translation @xcite @xcite @xcite , cross-lingual word embedding @xcite @xcite , etc. have been used by the researchers. ## dataset one of the barriers of sentiment analysis research in bengali is the lack of publicly available review datasets. in literature, researchers reported results using their curated datasets that are not publicly available. the few publicly available datasets are either small in size or not well-annotated. therefore, here, we have prepared a well-annotated bengali review dataset that we made publicly available. foot_1 ## cross-lingual sentiment analysis in bengali as bengali is a resource-poor language, we leverage sentiment lexicon and labeled data from english for sentiment analysis in bengali. we investigate the performances of various approaches (i.e., supervised, unsupervised, and transfer-learning based approaches) of sentiment analysis utilizing resources from english. figure 2 shows the overview of various approaches we studied. ## conclusion to facilitate sentiment analysis research in bengali, in this work, we introduce a benchmark dataset and explore the adaptation of resources and tools from english. we notice that due to misspellings, usage of regional varieties of bengali, and advancement of the machine translation system, supervised ml algorithms perform comparably in the bengali and machine-translated corpus. the agreements of the predictions suggest that bengali-english machine translation can preserve the sentiment information. the mediocre performances of the lexicon-based methods infer that annotated data are essential to achieve better classification accuracy. we present the performance of simple transfer learning utilizing cross-domain data. we note that with enough cross-domain training data, supervised ml classifiers provide a comparable performance of the lexicon-based methods, though lag behind the performance achieved through in-domain data. we report our findings regarding cross-lingual sentiment classification approaches in bengali, which provide directions for future research.