Automatic Construction of an Emoji Sentiment Lexicon

Kimura, Mayu; Katsurai, Marie

doi:10.1145/3110025.3110139

Cited by 32 publications

(17 citation statements)

References 6 publications

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“…Jiang et al (2015) proposed an emoticon space model to automatically match emotional tags for emoji. Kimura and Katsurai (2017) assigned multi-dimensional emotional vectors to emoji by calculating the co-occurrence frequency of emoji and emotional words in WordNet-Affect. Aoki and Uchida (2011) have also automatically generated emoji vectors based on the relationship between emotional words and emoji.…”

Section: Research Fields Regarding Emojimentioning

confidence: 99%

A Systematic Review of Emoji: Current Research and Future Perspectives

et al. 2019

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A growing body of research explores emoji, which are visual symbols in computer mediated communication (CMC). In the 20 years since the first set of emoji was released, research on it has been on the increase, albeit in a variety of directions. We reviewed the extant body of research on emoji and noted the development, usage, function, and application of emoji. In this review article, we provide a systematic review of the extant body of work on emoji, reviewing how they have developed, how they are used differently, what functions they have and what research has been conducted on them in different domains. Furthermore, we summarize directions for future research on this topic.

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Section: Research Fields Regarding Emojimentioning

confidence: 99%

A Systematic Review of Emoji: Current Research and Future Perspectives

et al. 2019

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“…In 2016, Liu et al [31] integrated HowNet and NTUSD (released by University of Taiwan) to construct sentiment lexicon based on microblog. About one year later, Kimura and Katsurai [32] introduced emoji into the construction of sentiment lexicon and assigned a vector representation to each emoji by calculating the co-occurrence between an emoji and each sentimental word. The limitation of above methods is that they cannot judge the sentiment of words that do not present in sentiment lexicons.…”

Section: B Construction Approaches Of Sentiment Lexiconsmentioning

confidence: 99%

The Construction of Sentiment Lexicon Based on Context-Dependent Part-of-Speech Chunks for Semantic Disambiguation

2020

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Sentiment lexicon, which provides sentiment information for words, plays an important role in sentiment analysis task. Currently, most of sentiment lexicons have only one sentiment polarity for each word and ignore sentimental ambiguity. In this paper, we propose to construct the sentiment lexicon based on context-dependent part-of-speech (POS) chunks, namely CP-chunks, which aims at solving the ambiguity of lexical sentiments. Given that the POS of context has impact on the word polarity and intensity, we take CP-chunks as an unit to do sentiment calculation. Our method is evaluated through the classification task of text sentiment. The experiment results indicate that, in comparison with the existing methods, the applicability of our method is more stable and balanced for both the positive and negative polarities corpora, and the accuracy of our method reaches 82% for the sentiment classification of a domain-specific corpus.

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“…Emoji play an important role in the emotional content of a message. Several sentiment lexicons for emojis have been proposed (Novak et al, 2015;Kimura and Katsurai, 2017;Rodrigues et al, 2018) and also studies in the context of emotion and emojis have been published recently (Wood and Ruder, 2016;Hu et al, 2017).…”

Section: Related Workmentioning

confidence: 99%

Multimodal Emoji Prediction

Barbieri¹,

Ballesteros²,

Ronzano³

et al. 2018

Proceedings of the 2018 Conference of the North American Chapter Of the Association for Computational Linguistics: Hu

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Emojis are small images that are commonly included in social media text messages. The combination of visual and textual content in the same message builds up a modern way of communication, that automatic systems are not used to deal with. In this paper we extend recent advances in emoji prediction by putting forward a multimodal approach that is able to predict emojis in Instagram posts. Instagram posts are composed of pictures together with texts which sometimes include emojis. We show that these emojis can be predicted by using the text, but also using the picture. Our main finding is that incorporating the two synergistic modalities, in a combined model, improves accuracy in an emoji prediction task. This result demonstrates that these two modalities (text and images) encode different information on the use of emojis and therefore can complement each other.

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Automatic Construction of an Emoji Sentiment Lexicon

Cited by 32 publications

References 6 publications

A Systematic Review of Emoji: Current Research and Future Perspectives

A Systematic Review of Emoji: Current Research and Future Perspectives

The Construction of Sentiment Lexicon Based on Context-Dependent Part-of-Speech Chunks for Semantic Disambiguation

Multimodal Emoji Prediction

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