How Translation Alters Sentiment

By Saif M. Mohammad, Mohammad Salameh, Svetlana Kiritchenko. 2016

211 citations recorded by the source corpus.

Sentiment analysis research has predominantly been on English texts. Thus there exist many sentiment resources for English, but less so for other languages. Approaches to improve sentiment analysis in a resource-poor focus language include: (a) translate the focus language text into a resource-rich language such as English, and apply a powerful English sentiment analysis system on the text, and (b) translate resources such as sentiment labeled corpora and sentiment lexicons from English into the focus language, and use them as additional resources in the focus-language sentiment analysis system. In this paper we systematically examine both options. We use Arabic social media posts as stand-in for the focus language text. We show that sentiment analysis of English translations of Arabic texts produces competitive results, w.r.t. Arabic sentiment analysis. We show that Arabic sentiment analysis systems benefit from the use of automatically translated English sentiment lexicons. We also conduct manual annotation studies to examine why the sentiment of a translation is different from the sentiment of the source word or text. This is especially relevant for building better automatic translation systems. In the process, we create a state-of-the-art Arabic sentiment analysis system, a new dialectal Arabic sentiment lexicon, and the first Arabic-English parallel corpus that is independently annotated for sentiment by Arabic and English speakers.

Subfields: Artificial Intelligence, Social Psychology, Natural Language Processing

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Citations in the corpus

XNLI: Evaluating Cross-lingual Sentence Representations (2018, background): “For instance, Mohammad et al. (2016) analyze the differences in human sentiment annotations of Arabic sentences and their English translations, and conclude that most of them come from cultural differences.”
XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization (2020, background): “For instance, it does not capture differences in classification performance that are due to cultural differences (Mohammad et al., 2016; Smith et al., 2016).”
Sentiment Analysis: Detecting Valence, Emotions, and Other Affectual States from Text (2016, background): “Salameh et al. (2015) and Mohammad et al. (2015) conducted experiments to determine loss in sentiment predictability when they translate Arabic social media posts into English, manually and automatically.”
SemEval-2017 Task 4: Sentiment Analysis in Twitter (2017, background): “…sentiment analysis of figurative language on Twitter (Ghosh et al., 2015), implicit event polarity (Russo et al., 2015), detecting stance in tweets (Mohammad et al., 2016a), out-ofcontext sentiment intensity of words and phrases (Kiritchenko et al., 2016), and emotion detection (Strapparava and…”
Cross-Lingual Transfer Learning for Question Answering (2019, background): “Using MT to achieve knowledge transfer between different languages has been studied on sentiment analysis (Mohammad et al., 2016), spoken language understanding (Stepanov et al.”
Ranking Transfer Languages with Pragmatically-Motivated Features for Multilingual Sentiment Analysis (2020, background): “Mohammad et al. (2016) have shown that, even with sound machine translation systems, achieving cross-lingual transfer by translating low-resource languages to high-resource languages and applying models trained on the highresource languages is impeded by culture-specific 1In linguistics, pragmatics…”
compare-mt: A Tool for Holistic Comparison of Language Generation Systems (2019, methodology): “For example, one may perform analysis to ﬁnd accuracy of generation of words with particular morphological tags (Popovi´c et al., 2006), or words that appear in a sentiment lexicon (Mohammad et al., 2016).”