Bilingual Sentiment Consistency for Statistical Machine Translation

Chen, Boxing; Zhu, Xiaodan

doi:10.3115/v1/e14-1064

Cited by 14 publications

(11 citation statements)

References 20 publications

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“…Additionally, we deal with noisy social media texts as opposed to more polished news media texts. There exists research on using sentiment analysis to improve machine translation (Chen and Zhu, 2014), but that is beyond the scope of this paper.…”

Section: Multilingual Sentiment Analysismentioning

confidence: 99%

Sentiment after Translation: A Case-Study on Arabic Social Media Posts

Salameh¹,

Mohammad²,

Kiritchenko³

2015

Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

112

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When text is translated from one language into another, sentiment is preserved to varying degrees. In this paper, we use Arabic social media posts as stand-in for source language text, and determine loss in sentiment predictability when they are translated into English, manually and automatically. As benchmarks, we use manually and automatically determined sentiment labels of the Arabic texts. We show that sentiment analysis of English translations of Arabic texts produces competitive results, w.r.t. Arabic sentiment analysis. We discover that even though translation significantly reduces the human ability to recover sentiment, automatic sentiment systems are still able to capture sentiment information from the translations.

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Section: Multilingual Sentiment Analysismentioning

confidence: 99%

Sentiment after Translation: A Case-Study on Arabic Social Media Posts

Salameh¹,

Mohammad²,

Kiritchenko³

2015

Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

112

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show abstract

“…The authors observed that these German, Spanish and French sentiment analysis systems performed similar to the initial English sentiment classifier. There also exists research on using sentiment analysis to improve machine translation, such as the work by Chen and Zhu [45], but that is beyond the scope of the proposed work.…”

Section: Multilingual Sentiment Analysismentioning

confidence: 99%

A Novel, Gradient Boosting Framework for Sentiment Analysis in Languages where NLP Resources Are Not Plentiful: A Case Study for Modern Greek

Athanasiou

Μaragoudakis

2017

Algorithms

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Sentiment analysis has played a primary role in text classification. It is an undoubted fact that some years ago, textual information was spreading in manageable rates; however, nowadays, such information has overcome even the most ambiguous expectations and constantly grows within seconds. It is therefore quite complex to cope with the vast amount of textual data particularly if we also take the incremental production speed into account. Social media, e-commerce, news articles, comments and opinions are broadcasted on a daily basis. A rational solution, in order to handle the abundance of data, would be to build automated information processing systems, for analyzing and extracting meaningful patterns from text. The present paper focuses on sentiment analysis applied in Greek texts. Thus far, there is no wide availability of natural language processing tools for Modern Greek. Hence, a thorough analysis of Greek, from the lexical to the syntactical level, is difficult to perform. This paper attempts a different approach, based on the proven capabilities of gradient boosting, a well-known technique for dealing with high-dimensional data. The main rationale is that since English has dominated the area of preprocessing tools and there are also quite reliable translation services, we could exploit them to transform Greek tokens into English, thus assuring the precision of the translation, since the translation of large texts is not always reliable and meaningful. The new feature set of English tokens is augmented with the original set of Greek, consequently producing a high dimensional dataset that poses certain difficulties for any traditional classifier. Accordingly, we apply gradient boosting machines, an ensemble algorithm that can learn with different loss functions providing the ability to work efficiently with high dimensional data. Moreover, for the task at hand, we deal with a class imbalance issues since the distribution of sentiments in real-world applications often displays issues of inequality. For example, in political forums or electronic discussions about immigration or religion, negative comments overwhelm the positive ones. The class imbalance problem was confronted using a hybrid technique that performs a variation of under-sampling the majority class and over-sampling the minority class, respectively. Experimental results, considering different settings, such as translation of tokens against translation of sentences, consideration of limited Greek text preprocessing and omission of the translation phase, demonstrated that the proposed gradient boosting framework can effectively cope with both high-dimensional and imbalanced datasets and performs significantly better than a plethora of traditional machine learning classification approaches in terms of precision and recall measures.

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“…Our approach does not break phrases down to words, but learns phrase embeddings directly. Chen et al (2010) represent a rule in the hierarchical phrase table using a bag-of-words approach. Instead, we learn phrase vectors directly without resorting to their constituent words.…”

Section: Related Workmentioning

confidence: 99%

Vector Space Models for Phrase-based Machine Translation

Alkhouli¹,

Guta²,

Ney³

2014

Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation

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This paper investigates the application of vector space models (VSMs) to the standard phrase-based machine translation pipeline. VSMs are models based on continuous word representations embedded in a vector space. We exploit word vectors to augment the phrase table with new inferred phrase pairs. This helps reduce out-of-vocabulary (OOV) words. In addition, we present a simple way to learn bilingually-constrained phrase vectors. The phrase vectors are then used to provide additional scoring of phrase pairs, which fits into the standard log-linear framework of phrase-based statistical machine translation. Both methods result in significant improvements over a competitive in-domain baseline applied to the Arabic-to-English task of IWSLT 2013.

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Bilingual Sentiment Consistency for Statistical Machine Translation

Cited by 14 publications

References 20 publications

Sentiment after Translation: A Case-Study on Arabic Social Media Posts

Sentiment after Translation: A Case-Study on Arabic Social Media Posts

A Novel, Gradient Boosting Framework for Sentiment Analysis in Languages where NLP Resources Are Not Plentiful: A Case Study for Modern Greek

Vector Space Models for Phrase-based Machine Translation

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