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Sari, Yuita Arum; Ratnasari, Evy Kamilah; Mutrofin, Siti; Arifin, Agus Zainal

doi:10.21609/jiki.v7i1.252

Cited by 6 publications

(2 citation statements)

References 7 publications

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“…As emoji are widely used in expressing emotions, they have become an effective means of sentiment analysis (Hogenboom et al, 2013; Cappallo et al, 2015). A number of studies have confirmed the effective performance of emoji in sentiment analysis (Sari et al, 2014; Cahyaningtyas et al, 2017; Felbo et al, 2017; LeCompte and Chen, 2017). Besides, emoji-based sentiment analysis is language-independent and exhibits cross-language validity (Guthier et al, 2017), for example, Al-Azani et al (2018) found that emoji can also be used in analyzing the sentiment of Arabic tweets.…”

Section: Research Fields Regarding Emojimentioning

confidence: 94%

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: 94%

A Systematic Review of Emoji: Current Research and Future Perspectives

et al. 2019

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“…Accordingly, they are utilized for research in various fields. Due to their function as emotional signals [6,7] adding a sentimental information to a written text, sentiment analysis or emotion identification is one application field within computer science [7,9,[12][13][14][15][16][17], which, among other things, led to the result that the utilization of emojis helps to improve sentiment scores [18,19]. Other research questions related to emojis are of semantic origin and explore their meaning and use [14,[20][21][22].…”

Section: State Of the Artmentioning

confidence: 99%

Emojis as Contextual Indicants in Location-Based Social Media Posts

Hauthal

Dunkel

Burghardt

2021

IJGI

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The presented study aims to investigate the relationship between the use of emojis in location-based social media and the location of the corresponding post in terms of perceived objects and conducted activities connected to this place. The basis for this is not a purely frequency-based assessment, but a specifically introduced measure called typicality. To evaluate the typicality measure and examine the assumption that emojis are contextual indicants, a dataset of worldwide geotagged posts from Instagram relating to sunset and sunrise events is used, converted to a privacy-aware version based on a Hyperloglog approach. Results suggest that emojis can often provide more nuanced information about user activities and the surrounding environment than is possible with hashtags. Thus, emojis may be suitable for identifying less obvious characteristics and the sense of a place. Emojis are already explored in research, but mainly for sentiment analysis, for semantic studies or as part of emoji prediction. In contrast, this work provides novel insights into the user’s spatial or activity context by applying the typicality measure and therefore considers emojis contextual indicants.

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Natural Language Processing–Based Virtual Cofacilitator for Online Cancer Support Groups: Protocol for an Algorithm Development and Validation Study

Leung¹,

Wouterloot²,

Adikari³

et al. 2021

JMIR Res Protoc

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Background Cancer and its treatment can significantly impact the short- and long-term psychological well-being of patients and families. Emotional distress and depressive symptomatology are often associated with poor treatment adherence, reduced quality of life, and higher mortality. Cancer support groups, especially those led by health care professionals, provide a safe place for participants to discuss fear, normalize stress reactions, share solidarity, and learn about effective strategies to build resilience and enhance coping. However, in-person support groups may not always be accessible to individuals; geographic distance is one of the barriers for access, and compromised physical condition (eg, fatigue, pain) is another. Emerging evidence supports the effectiveness of online support groups in reducing access barriers. Text-based and professional-led online support groups have been offered by Cancer Chat Canada. Participants join the group discussion using text in real time. However, therapist leaders report some challenges leading text-based online support groups in the absence of visual cues, particularly in tracking participant distress. With multiple participants typing at the same time, the nuances of the text messages or red flags for distress can sometimes be missed. Recent advances in artificial intelligence such as deep learning–based natural language processing offer potential solutions. This technology can be used to analyze online support group text data to track participants’ expressed emotional distress, including fear, sadness, and hopelessness. Artificial intelligence allows session activities to be monitored in real time and alerts the therapist to participant disengagement. Objective We aim to develop and evaluate an artificial intelligence–based cofacilitator prototype to track and monitor online support group participants’ distress through real-time analysis of text-based messages posted during synchronous sessions. Methods An artificial intelligence–based cofacilitator will be developed to identify participants who are at-risk for increased emotional distress and track participant engagement and in-session group cohesion levels, providing real-time alerts for therapist to follow-up; generate postsession participant profiles that contain discussion content keywords and emotion profiles for each session; and automatically suggest tailored resources to participants according to their needs. The study is designed to be conducted in 4 phases consisting of (1) development based on a subset of data and an existing natural language processing framework, (2) performance evaluation using human scoring, (3) beta testing, and (4) user experience evaluation. Results This study received ethics approval in August 2019. Phase 1, development of an artificial intelligence–based cofacilitator, was completed in January 2020. As of December 2020, phase 2 is underway. The study is expected to be completed by September 2021. Conclusions An artificial intelligence–based cofacilitator offers a promising new mode of delivery of person-centered online support groups tailored to individual needs. International Registered Report Identifier (IRRID) DERR1-10.2196/21453

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Untitled

Cited by 6 publications

References 7 publications

A Systematic Review of Emoji: Current Research and Future Perspectives

A Systematic Review of Emoji: Current Research and Future Perspectives

Emojis as Contextual Indicants in Location-Based Social Media Posts

Natural Language Processing–Based Virtual Cofacilitator for Online Cancer Support Groups: Protocol for an Algorithm Development and Validation Study

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