ML-Ask: Open Source Affect Analysis Software for Textual Input in
                        Japanese

Ptaszyński, Michał; Dybała, Paweł; Rzepka, Rafał; Araki, Koji; Masui, Fumito

doi:10.5334/jors.149

Cited by 29 publications

(15 citation statements)

References 21 publications

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“…Secondly, we discarded any smileys and emoticons, since they rarely occurred. However, we recognise that, in other contexts and other datasets, the presence of such emoticons, such as frown, or angry faces, may be indicative of cyberbullying, as shown by Ptaszynski et al (2010) and Ptaszynski et al (2016). In addition, although not encountered in the present datasets, Unicode (2017) allows not only emoticons to be inserted in text, but also other symbols for gestures or animals that may constitute cyberbullying, such as fist-making or monkey face.…”

Section: Discussionmentioning

confidence: 73%

“…From this perspective, the task of cyberbullying detection was previously approached as a classification task (Yin et al 2009) that involves data acquisition and pre-processing, feature extraction, and classification. These techniques were used mostly in targeting explicit textual cyberbullying language and rely on detecting features such as profanities (Yin et al 2009;Dinakar et al 2012;Dadvar et al 2013;Al-garadi et al 2016), bad words (Reynolds et al 2011;Huang et al 2014), foul terms (Nahar et al 2013), bullying terms (Kontostathis et al 2013;Nandhini and Sheeba 2015), pejoratives and obscenities (Chen et al 2012), emotemes and vulgarities (Ptaszynski et al 2010;Ptaszynski et al 2016), curses (Chatzakou et al 2017) or negative words (Van Hee et al 2015).…”

Section: Related Workmentioning

confidence: 99%

“…Several other studies have also targeted the detection of implicit forms of cyberbullying (Chen et al 2012;Dinakar et al 2012;Nitta et al 2013;Ptaszynski et al 2010;Ptaszynski et al 2016). However, they do not clearly define the forms that represent implicit cyberbullying.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Detecting Discourse-Independent Negated Forms of Public Textual Cyberbullying

Power¹,

Keane²,

Nolan³

et al. 2018

J. Comp. Assist. Linguist. Res.

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Cyberbullying is a risk associated with the online safety of young people and, in this paper, we address one of its most common implicit forms -negation-based forms. We first describe the role of negation in public textual cyberbullying interaction and identify the cyberbullying constructions that characterise these forms. We then formulate the overall detection mechanism which captures the three necessary and sufficient elements of public textual cyberbullying -the personal marker, the dysphemistic element, and the link between them. Finally, we design rules to detect both overt and covert negation-based forms, and measure their effectiveness using a development dataset, as well as a novel test dataset, across several metrics: accuracy, precision, recall, and the F1-measure. The results indicate that the rules we designed closely resemble the performance of human annotators across all measures.

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Section: Discussionmentioning

confidence: 73%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Detecting Discourse-Independent Negated Forms of Public Textual Cyberbullying

Power¹,

Keane²,

Nolan³

et al. 2018

J. Comp. Assist. Linguist. Res.

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“…Sentiment Analysis using ML-Ask is a Sentiment Analysis method that focuses on a wider range of emotions. Emotions are classified into 10 categories using the Emotional Expression Dictionary: "sadness", "shame", "anger", "dislike", "fear", "surprise", "like", "excitement", "peace", and "joy" [4].…”

Section: Sentiment Analysismentioning

confidence: 99%

A Study on the Impact of "Flaming" on Content from "A Crocodile Who Will Die in 100 Days"

Sugimoto

Nakamura

2023

Proceedings of International Conference on Artificial Life and Robotics

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“…For this reason, along with the spread of social media, research on the automatic extraction, collection, and analysis of emotional information from text data contained in these media has gained in popularity [1][2][3][4][5]. An example is ML-Ask (https://github.com/ ptaszynski/mlask (accessed on 19 May 2024)) [6], an affect analysis system for textual input in Japanese developed by Ptaszynski et al The original version of ML-Ask was developed a few years ago, and its source code was all uploaded to GitHub since it was the first open-sourced affect system for Japanese [6]. The fact is that the affect lexicon contained in the database used in ML-Ask was constructed based on the Dictionary of Emotive Expressions [7], which was created in the 1990s, and thus many contemporary emotive expressions have not yet been included.…”

Section: Introductionmentioning

confidence: 99%

The Limits of Words: Expanding a Word-Based Emotion Analysis System with Multiple Emotion Dictionaries and the Automatic Extraction of Emotive Expressions

Wang,

Isomura,

Ptaszynski

et al. 2024

Applied Sciences

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Wide adoption of social media has caused an explosion of information stored online, with the majority of that information containing subjective, opinionated, and emotional content produced daily by users. The field of emotion analysis has helped effectively process such human emotional expressions expressed in daily social media posts. Unfortunately, one of the greatest limitations of popular word-based emotion analysis systems has been the limited emotion vocabulary. This paper presents an attempt to extensively expand one such word-based emotion analysis system by integrating multiple emotion dictionaries and implementing an automatic extraction mechanism for emotive expressions. We first leverage diverse emotive expression dictionaries to expand the emotion lexicon of the system. To do that, we solve numerous problems with the integration of various dictionaries collected using different standards. We demonstrate the performance improvement of the system with improved accuracy and granularity of emotion classification. Furthermore, our automatic extraction mechanism facilitates the identification of novel emotive expressions in an emotion dataset, thereby enriching the depth and breadth of emotion analysis capabilities. In particular, the automatic extraction method shows promising results for applicability in further expansion of the dictionary base in the future, thus advancing the field of emotion analysis and offering new avenues for research in sentiment analysis, affective computing, and human–computer interaction.

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ML-Ask: Open Source Affect Analysis Software for Textual Input in Japanese

Cited by 29 publications

References 21 publications

Detecting Discourse-Independent Negated Forms of Public Textual Cyberbullying

Detecting Discourse-Independent Negated Forms of Public Textual Cyberbullying

A Study on the Impact of "Flaming" on Content from "A Crocodile Who Will Die in 100 Days"

The Limits of Words: Expanding a Word-Based Emotion Analysis System with Multiple Emotion Dictionaries and the Automatic Extraction of Emotive Expressions

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