How Translation Alters Sentiment

Mohammad, Saif M.; Salameh, Mohammad; Kiritchenko, Svetlana

doi:10.1613/jair.4787

Cited by 127 publications

(49 citation statements)

References 35 publications

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“…Mihalcea et al [42] used English resources to automatically generate a Romanian subjectivity lexicon using an English-Romanian dictionary. The work of [43] investigated both (a) and (b) for Arabic. More specifically, they conducted several experiments by either using translated English polarity lexica into Arabic texts or freely-available machine translation engines and manual translations for converting Arabic to English in order to subsequently apply sentiment analysis.…”

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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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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“…Moreover, it is widely believed that aspects of sentiment may be lost in translation, especially in automatic translation [3]. The extent of this loss, in terms of drop in accuracy of automatic sentiment analysis remains undetermined [3]. In this work, one of the main objectives is to determine extent of this drop.…”

Section: Introductionmentioning

confidence: 98%

“…Typically, CLSC refers to the task of predicting the polarity of the opinion expressed in a text in a label-scarce target language using a classifier trained on the corpus from a label-rich source language. CLSC is popularly studied to reduce the expense of manual annotation efforts required in the target language domain [1]- [3]. To date, a variety of lexicon-based and corpus-based methods have been developed for sentiment classification.…”

Section: Introductionmentioning

confidence: 99%

“…Because most previous work focuses on English sentiment classification, many annotated sentiment lexica and corpora for English sentiment classification in various domains are freely available on the Web. The annotated resources for sentiment classification in many other languages are not abundant and it is time-consuming to manually label a rich and reliable sentiment lexicon or corpus in those languages [3]- [5]. In general, efforts towards building sentiment analysis methods for other languages have been hampered by the high cost involved in creating corpora and lexical resources for a new language.…”

Section: Introductionmentioning

confidence: 99%

“…However, adapting machine translated English resources to entirely new languages usually produces various challenges, as each language may be significantly different in terms of the characteristics and translation quality differs from language pair to language pair. Moreover, it is widely believed that aspects of sentiment may be lost in translation, especially in automatic translation [3]. The extent of this loss, in terms of drop in accuracy of automatic sentiment analysis remains undetermined [3].…”

Section: Introductionmentioning

confidence: 99%

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Cross-Lingual Sentiment Classification from English to Arabic using Machine Translation

Al-Shabi¹,

Adel²,

Omar³

et al. 2017

ijacsa

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Abstract-Cross-lingual sentiment learning is becoming increasingly important due to the multilingual nature of usergenerated content on social media and the scarce resources for languages other than English. However, cross-lingual sentiment learning is a challenging task due to the different distribution between translated data and original data and due to the language gap, i.e. each language has its own ways to express sentiments. This work explores the adaptation of English resources for sentiment analysis to a new language, Arabic. The aim is to design a light model for cross-lingual sentiment classification from English to Arabic, without any manual annotation effort which, at the same time, is easy to build and does not require deep linguistic analysis. The ultimate goal is to find an optimal baseline model and to determine the relation between the noise in the translated data and the accuracy of sentiment classification. Different configurations of several factors are investigated including feature representation, feature reduction methods, and the learning algorithms to find the optimal baseline model. Experiments show that a good classification model can be obtained from translated data regardless of the artificial noise added by machine translation. The results also show a significant cost to automation, and thus the best path to future enhancement is through the inclusion of language-specific knowledge and resources.

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