Emoji-Powered Representation Learning for Cross-Lingual Sentiment Classification

Chen, Zhenpeng; Shen, Sheng; Hu, Ziniu; Lü, Xuan; Mei, Qiaozhu; Liu, Xuanzhe

doi:10.1145/3308558.3313600

Cited by 59 publications

(31 citation statements)

References 39 publications

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“…Ref. [24] proposed a representation learning method that utilizes emojis as an instrument to learn language-independent sentiment-aware text representations. The approach is however limited to text types where emojis regularly appear.…”

Section: Related Workmentioning

confidence: 99%

Zero-Shot Learning for Cross-Lingual News Sentiment Classification

et al. 2020

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In this paper, we address the task of zero-shot cross-lingual news sentiment classification. Given the annotated dataset of positive, neutral, and negative news in Slovene, the aim is to develop a news classification system that assigns the sentiment category not only to Slovene news, but to news in another language without any training data required. Our system is based on the multilingual BERTmodel, while we test different approaches for handling long documents and propose a novel technique for sentiment enrichment of the BERT model as an intermediate training step. With the proposed approach, we achieve state-of-the-art performance on the sentiment analysis task on Slovenian news. We evaluate the zero-shot cross-lingual capabilities of our system on a novel news sentiment test set in Croatian. The results show that the cross-lingual approach also largely outperforms the majority classifier, as well as all settings without sentiment enrichment in pre-training.

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Section: Related Workmentioning

confidence: 99%

Zero-Shot Learning for Cross-Lingual News Sentiment Classification

et al. 2020

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“…Emoji can be interpreted differently according to the platform, which might influence communication [28]. Besides, some researchers have investigated the power of Emoji in the cross-lingual sentiment classification task [11] and have performed large scale empirical study on how developers used Emoji on GitHub [25].…”

Section: Emoji Usage Analysismentioning

confidence: 99%

Inferring attributes with picture metadata embeddings

Pijani

Imine

Rusinowitch

2020

SIGAPP Appl. Comput. Rev.

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Users in online social networks are vulnerable to attribute inference attacks due to some published data. Thus, the picture owner's gender has a strong influence on individuals' emotional reactions to the photo. In this work, we present a graph-embedding approach for gender inference attacks based on pictures meta-data such as (i) alt-texts generated by Facebook to describe the content of images, and (ii) Emojis/Emoticons posted by friends, friends of friends or regular users as a reaction to the picture. Specifically, we apply a semi-supervised technique, node2vec, for learning a mapping of pictures meta-data to a low-dimensional vector space. Next, we study in this vector space the gender closeness of users who published similar photos and/or received similar reactions. We leverage this image sharing and reaction mode of Facebook users to derive an efficient and accurate technique for user gender inference. Experimental results show that privacy attack often succeeds even when other information than pictures published by their owners is either hidden or unavailable.

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“…Recent studies extended the distant supervison to emojis, a more diverse set of noisy labels [17,23]. As emojis are becoming increasingly popular [9,16,43] and have the ability to express emotions [33], they are considered benign noisy labels of sentiments in current sentiment analysis [17,23]. The sentiment information contained in the emoji usage data can supplement the limited manually labeled data.…”

Section: Emojis In Sentiment Analysismentioning

confidence: 99%

SEntiMoji: an emoji-powered learning approach for sentiment analysis in software engineering

Chen

Cao

Lü

et al. 2019

Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of

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Sentiment analysis has various application scenarios in software engineering (SE), such as detecting developers' emotions in commit messages and identifying their opinions on Q&A forums. However, commonly used out-of-the-box sentiment analysis tools cannot obtain reliable results on SE tasks and the misunderstanding of technical jargon is demonstrated to be the main reason. Then, researchers have to utilize labeled SE-related texts to customize sentiment analysis for SE tasks via a variety of algorithms. However, the scarce labeled data can cover only very limited expressions and thus cannot guarantee the analysis quality. To address such a problem, we turn to the easily available emoji usage data for help. More specifically, we employ emotional emojis as noisy labels of sentiments and propose a representation learning approach that uses both Tweets and GitHub posts containing emojis to learn sentiment-aware representations for SE-related texts. These emoji-labeled posts can not only supply the technical jargon, but also incorporate more general sentiment patterns shared across domains. They as well as labeled data are used to learn the final sentiment classifier. Compared to the existing sentiment analysis methods used in SE, the proposed approach can achieve significant improvement on representative benchmark datasets. By further contrast experiments, we find that the Tweets make a key contribution to the power of our approach. This finding informs future research not to unilaterally pursue the domain-specific resource, but try to transform knowledge from the open domain through ubiquitous signals such as emojis.

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Emoji-Powered Representation Learning for Cross-Lingual Sentiment Classification

Cited by 59 publications

References 39 publications

Zero-Shot Learning for Cross-Lingual News Sentiment Classification

Zero-Shot Learning for Cross-Lingual News Sentiment Classification

Inferring attributes with picture metadata embeddings

SEntiMoji: an emoji-powered learning approach for sentiment analysis in software engineering

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