Journal articles: 'Emoticon'

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Merlina, Tina, Lia Maulia, and Rosaria Mita Amalia. "Verbal and Visual Expression of Emotions on Kaskus: a Semiotic Study." MIMBAR, Jurnal Sosial dan Pembangunan 29, no. 1 (June 20, 2013): 85. http://dx.doi.org/10.29313/mimbar.v29i1.373.

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This research investigates the types of emoticons which occured on Kaskus. This is a qualitative research. The writer take the data from Kaskus, therefore it is a forum in the internet which has grown to be one of the most popular websites in Indonesia. To identify the types of Emotions on Kaskus, the writer analyzed the data using Ekman (2003) . From the discussions, there are verbal and non verbal sign in the emoticons that appear on Kaskus. The meaning of verbal sign and nonverbal sign in emoticon “marah” represents anger emotion. Emoticon “Ngakak” and “thumbup” represent enjoyable emotion. Emoticon “Sorry” and “Cool” represent sadness emotion. For future studies need to be conducted with an increased sample by using another media such as Whatsapp, YM, etc.

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Liao, Songyang, Katsuaki Sakata, and Galina V. Paramei. "Color Affects Recognition of Emoticon Expressions." i-Perception 13, no. 1 (January 2022): 204166952210807. http://dx.doi.org/10.1177/20416695221080778.

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In computer-mediated communication, emoticons are conventionally rendered in yellow. Previous studies demonstrated that colors evoke certain affective meanings, and face color modulates perceived emotion. We investigated whether color variation affects the recognition of emoticon expressions. Japanese participants were presented with emoticons depicting four basic emotions (Happy, Sad, Angry, Surprised) and a Neutral expression, each rendered in eight colors. Four conditions (E1–E4) were employed in the lab-based experiment; E5, with an additional participant sample, was an online replication of the critical E4. In E1, colored emoticons were categorized in a 5AFC task. In E2–E5, stimulus affective meaning was assessed using visual scales with anchors corresponding to each emotion. The conditions varied in stimulus arrays: E2: light gray emoticons; E3: colored circles; E4 and E5: colored emoticons. The affective meaning of Angry and Sad emoticons was found to be stronger when conferred in warm and cool colors, respectively, the pattern highly consistent between E4 and E5. The affective meaning of colored emoticons is regressed to that of achromatic expression counterparts and decontextualized color. The findings provide evidence that affective congruency of the emoticon expression and the color it is rendered in facilitates recognition of the depicted emotion, augmenting the conveyed emotional message.

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Fujisawa, Akira, Kazuyuki Matsumoto, Minoru Yoshida, and Kenji Kita. "Emotion Estimation Method Based on Emoticon Image Features and Distributed Representations of Sentences." Applied Sciences 12, no. 3 (January 25, 2022): 1256. http://dx.doi.org/10.3390/app12031256.

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This paper proposes an emotion recognition method for tweets containing emoticons using their emoticon image and language features. Some of the existing methods register emoticons and their facial expression categories in a dictionary and use them, while other methods recognize emoticon facial expressions based on the various elements of the emoticons. However, highly accurate emotion recognition cannot be performed unless the recognition is based on a combination of the features of sentences and emoticons. Therefore, we propose a model that recognizes emotions by extracting the shape features of emoticons from their image data and applying the feature vector input that combines the image features with features extracted from the text of the tweets. Based on evaluation experiments, the proposed method is confirmed to achieve high accuracy and shown to be more effective than methods that use text features only.

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Huang, Robin, Na Liu, Mary Ann Nicdao, Mary Mikaheal, Tanya Baldacchino, Annabelle Albeos, Kathy Petoumenos, Kamal Sud, and Jinman Kim. "Emotion sharing in remote patient monitoring of patients with chronic kidney disease." Journal of the American Medical Informatics Association 27, no. 2 (October 21, 2019): 185–93. http://dx.doi.org/10.1093/jamia/ocz183.

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Abstract Objective To investigate the relationship between emotion sharing and technically troubled dialysis (TTD) in a remote patient monitoring (RPM) setting. Materials and Methods A custom software system was developed for home hemodialysis patients to use in an RPM setting, with focus on emoticon sharing and sentiment analysis of patients’ text data. We analyzed the outcome of emoticon and sentiment against TTD. Logistic regression was used to assess the relationship between patients’ emotions (emoticon and sentiment) and TTD. Results Usage data were collected from January 1, 2015 to June 1, 2018 from 156 patients that actively used the app system, with a total of 31 159 dialysis sessions recorded. Overall, 122 patients (78%) made use of the emoticon feature while 146 patients (94%) wrote at least 1 or more session notes for sentiment analysis. In total, 4087 (13%) sessions were classified as TTD. In the multivariate model, when compared to sessions with self-reported very happy emoticons, those with sad emoticons showed significantly higher associations to TTD (aOR 4.97; 95% CI 4.13–5.99; P = < .001). Similarly, negative sentiments also revealed significant associations to TTD (aOR 1.56; 95% CI 1.22–2; P = .003) when compared to positive sentiments. Discussion The distribution of emoticons varied greatly when compared to sentiment analysis outcomes due to the differences in the design features. The emoticon feature was generally easier to understand and quicker to input while the sentiment analysis required patients to manually input their personal thoughts. Conclusion Patients on home hemodialysis actively expressed their emotions during RPM. Negative emotions were found to have significant associations with TTD. The use of emoticons and sentimental analysis may be used as a predictive indicator for prolonged TTD.

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Brito, Pedro Quelhas, Sandra Torres, and Jéssica Fernandes. "What kind of emotions do emoticons communicate?" Asia Pacific Journal of Marketing and Logistics 32, no. 7 (December 10, 2019): 1495–517. http://dx.doi.org/10.1108/apjml-03-2019-0136.

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Purpose The purpose of this paper is to study the nature and concept of emoticons/emojis. Instead of taking for granted that these user-generated formats are necessarily emotional, we empirically assessed in what extent are they and the specificity of each one. Drawing on congruent mood state, valence core and emotion appraisal theories we expected a compatible statistical association between positive/negative/neutral emotional valence expressions and emoticons of similar valence. The positive emoticons were consistently associated with positive valence posts. Added to that analysis, 21 emotional categories were identified in posts and correlated with eight emoticons. Design/methodology/approach Two studies were used to address this question. The first study defined emoticon concept and interpreted their meaning highlighting their communication goals and anticipated effects. The link between emojis and emoticons was also obtained. Some emoticons types present more ambiguity than others. In the second study, three years of real and private (Facebook) posts from 82 adolescents were content analyzed and coded. Findings Only the neutral emoticons always matched neutral emotional categories found in the written interaction. Although the emoticon valence and emotional category congruence pattern was the rule, we also detected a combination of different valence emoticons types and emotion categories valence expressions. Apparently the connection between emoticon and emotion are not so obviously straightforward as the literature used to assume. The created objects designed to communicate emotions (emoticons) have their specific corresponding logic with the emotional tone of the message. Originality/value Theoretically, we discussed the emotional content of emoticons/emojis. Although this king of signals have an Asian origin and later borrowed from the western countries, their ambiguity and differing specificity have never been analyzed.

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Urabe, Yuki, Rafal Rzepka, and Kenji Araki. "Emoticon Recommendation System to Richen Your Online Communication." International Journal of Multimedia Data Engineering and Management 5, no. 1 (January 2014): 14–33. http://dx.doi.org/10.4018/ijmdem.2014010102.

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Japanese emoticons are widely used to express users' feelings and intentions in social media, blogs and instant messages. Japanese smartphone keypads have a feature that shows a list of emoticons, enabling users to insert emoticons simply by touching them. However, this list of emoticons contains more than 200, which is difficult to choose from, so a method to reorder the list and recommend appropriate emoticons to users is necessary. This paper proposes an emoticon recommendation method based on the emotive statements of users and their past selections of emoticons. The system is comprised of an affect analysis system and an original emoticon database: a table of 59 emoticons numerically categorized by 10 emotion types. The authors' experiments showed that 73.0% of chosen emoticons were among the top five recommended by the system, which is an improvement of 43.5% over the method used in current smartphones, which is based only on users' past emoticon selections.

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Tinambunan, Gusmariani, and Suharjito. "SENTIMENT ANALYSIS OF INDONESIA’S DIGITAL WALLET USING COMBINATION MACHINE LEARNING AND EMOTICON WEIGHT." Jurnal Ilmiah Teknologi dan Rekayasa 26, no. 3 (2021): 170–82. http://dx.doi.org/10.35760/tr.2021.v26i3.5266.

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Opinions on social media can be used to determine user sentiment by using sentiment analysis concept. Sentiment analysis requires several important stages, namely, preprocessing, feature extraction and classification method stages. The preprocessing stage was carried out to eliminate inconsistent data. In previous research, punctuation marks removal was applied at the preprocessing stage which can eliminate the emoticon position. Emoticons are a combination of punctuation marks. According to previous research, the emoticon feature has no contribution in sentiment analysis. There is another suggestion to maintain an emoticon position like converting an emoticon into a more relevant word such as :) into a “smile”. However, the feature of emoticon weights has not been considered in the sentiment analysis process. In order to consider the role of emoticons and to improve sentiment analysis performance, we propose using a combination of machine learning and emoticon weights. We perform emoticon weight based on probability and sentiment score. Each probability value and sentiment score of the emoticon will be normalized using the z-score method. There are several machine learning methods that have the best classification success rates, namely, Naïve Bayes and SVM. Based on the evaluation results of the proposed model, the best accuracy is 87% - 89% when using the combination of machine learning and emoticon sentiment score. Based on the results also show that the emoticon sentiment score has a significant effect on the accuracy of sentiment analysis.

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Yamamoto, Hisako, Misako Kawahara, Mariska Kret, and Akihiro Tanaka. "Cultural Differences in Emoticon Perception: Japanese See the Eyes and Dutch the Mouth of Emoticons." Letters on Evolutionary Behavioral Science 11, no. 2 (December 15, 2020): 40–45. http://dx.doi.org/10.5178/lebs.2020.80.

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This study investigated cultural differences in the perception of emoticons between Japanese and Dutch participants. We manipulated the eyes and mouth of emoticons independently and asked participants to evaluate the emotion of each emoticon. The results show that Japanese participants tended to focus on the emotion expressed with the eyes while Dutch participants put weight on the shape of the mouth when evaluating emoticons. This tendency is consistent with a previous cross-cultural study comparing people from Japan and the United States (Yuki et al., 2007).

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FELDMAN, LAURIE BETH, CECILIA R. ARAGON, NAN-CHEN CHEN, and JUDITH F. KROLL. "Emoticons in informal text communication: a new window on bilingual alignment." Bilingualism: Language and Cognition 21, no. 1 (July 19, 2017): 209–18. http://dx.doi.org/10.1017/s1366728917000359.

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The study of emoticon use in text communication is in its early stages (Aragon, Feldman, Chen & Kroll, 2014), with even less known about how emoticons function in multilingual environments. We describe a preliminary longitudinal analysis of text communication in an online bilingual scientific work environment and demonstrate how patterns of emoticon use constitute a novel yet systematic nonverbal aspect of communication. Specifically, coordination over bilingual speakers entails reductions in emoticon diversity over time that are greater for those who communicate in their L2 than in their L1. An analogous but weaker pattern is evident for lexical diversity in L2 but not L1. We hypothesize that reductions in emoticon diversity in the L2 are likely to reflect social contributions to alignment rather than purely proficiency.

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Yus, Francisco. "NOT ALL EMOTICONS ARE CREATED EQUAL." Linguagem em (Dis)curso 14, no. 3 (December 2014): 511–29. http://dx.doi.org/10.1590/1982-4017-140304-0414.

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Text deformation and emoticon use have become pervasive in today's computer-mediated communication. In this article, emoticons are analysed from a pragmatic, relevance-theoretic perspective, which entails determining the extent to which emoticons contribute to the eventual relevance of the information communicated by the text typed on the keyboard. Eight pragmatic functions are proposed, which correspond to the different ways in which emoticons satisfy the user's search for relevance. The analysis will also address how emoticons contribute to a more fine-grained identification of the user's attitudes, feelings and emotions, which are often difficult to pin down in text-based communication.

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Suwarti, Tarcisia Sri, Siti Lestari, and Muhammad Wahyu Widiyanto. "Pembelajaran Literasi Digital PAUD melalui Pelatihan Tutor Paud di Pos PAUD Dahlia Kelurahan Palebon Kecamatan Pedurungan." Indonesian Journal of Community Services 2, no. 2 (December 26, 2020): 118. http://dx.doi.org/10.30659/ijocs.2.2.118-125.

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Perkembangan teknologi mendukung kemajuan pendidikan di Indonesia secara signifikan. Bentuk paling sederhana dari teknologi yang sering digunakan secara digital adalah emoticon. Anak-anak usia dini pada era sekarang telah mengenal teknologi modern yang mendukung mereka untuk mengenal dunia sekitar. Sebagai contoh adalah handphone yang memiliki fitur emoticon sebagai sarana komunikasi. Oleh karena itu, para tutor Pos PAUD Dahlia Semarang perlu dibekali pembelajaran literasi digital PAUD dengan media Box Bas yang mengandung unsur emoticon untuk mengimbangi pengetahuan siswanya, sehingga meningkatkan pengetahuan pemanfaatan literasi digital bagi pembelajaran PAUD.The development of technology supports education in Indonesia significantly. The simplest form of technologies which is often used digitally is emoticon. Early young learners are recently very familiar with technology in order to know the world surround. For example, handphone which has emoticon features to show expressions. Therefore, tutors of Pos PAUD Dahlia Semarang need to be equipped with digital literacy learning through Box Bas media that contains emoticons to correspond with their students knowledge. This activity can upgrade the tutors� knowledge in teaching early young learner students by using digital literacy learning.

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Alita, Debby, Sigit Priyanta, and Nur Rokhman. "Analysis of Emoticon and Sarcasm Effect on Sentiment Analysis of Indonesian Language on Twitter." Journal of Information Systems Engineering and Business Intelligence 5, no. 2 (October 24, 2019): 100. http://dx.doi.org/10.20473/jisebi.5.2.100-109.

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Background: Indonesia is an active Twitter user that is the largest ranked in the world. Tweets written by Twitter users vary, from tweets containing positive to negative responses. This agreement will be utilized by the parties concerned for evaluation.Objective: On public comments there are emoticons and sarcasm which have an influence on the process of sentiment analysis. Emoticons are considered to make it easier for someone to express their feelings but not a few are also other opinion researchers, namely by ignoring emoticons, the reason being that it can interfere with the sentiment analysis process, while sarcasm is considered to be produced from the results of the sarcasm sentiment analysis in it.Methods: The emoticon and no emoticon categories will be tested with the same testing data using classification method are Naïve Bayes Classifier and Support Vector Machine. Sarcasm data will be proposed using the Random Forest Classifier, Naïve Bayes Classifier and Support Vector Machine method.Results: The use of emoticon with sarcasm detection can increase the accuracy value in the sentiment analysis process using Naïve Bayes Classifier method.Conclusion: Based on the results, the amount of data greatly affects the value of accuracy. The use of emoticons is excellent in the sentiment analysis process. The detection of superior sarcasm only by using the Naïve Bayes Classifier method due to differences in the amount of sarcasm data and not sarcasm in the research process.Keywords: Emoticon, Naïve Bayes Classifier, Random Forest Classifier, Sarcasm, Support Vector Machine

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Howman, Hannah Elizabeth, and Ruth Filik. "The role of emoticons in sarcasm comprehension in younger and older adults: Evidence from an eye-tracking experiment." Quarterly Journal of Experimental Psychology 73, no. 11 (June 2, 2020): 1729–44. http://dx.doi.org/10.1177/1747021820922804.

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We present an eye-tracking experiment examining moment-to-moment processes underlying the comprehension of emoticons. Younger (18–30) and older (65+) participants had their eye movements recorded while reading scenarios containing comments that were ambiguous between literal or sarcastic interpretations (e.g., But you’re so quick though). Comments were accompanied by wink emoticons or full stops. Results showed that participants read earlier parts of the wink scenarios faster than those with full stops, but then spent more time reading the text surrounding the emoticon. Thus, readers moved more quickly to the end of the text when there was a device that may aid interpretation but then spent more time processing the conflict between the superficially positive nature of the comment and the tone implied by the emoticon. Interestingly, the wink increased the likelihood of a sarcastic interpretation in younger adults only, suggesting that perceiver-related factors play an important role in emoticon interpretation.

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Lee, Crystal T., and Sara H. Hsieh. "Engaging consumers in mobile instant messaging: the role of cute branded emoticons." Journal of Product & Brand Management 28, no. 7 (November 18, 2019): 849–63. http://dx.doi.org/10.1108/jpbm-08-2018-1997.

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Purpose With the rapid surge of mobile marketing, an increasing number of brands have launched branded emoticons in an attempt to build brand relationships with consumers. Despite the apparent promise of branded emoticon usage, there is only limited academic research on branded emoticons. This paper aims to build on impression management theory and the conceptualization of cuteness to investigate how the effect of cuteness in branded emoticon design influences perceived playfulness in mobile instant messaging (MIM) interaction and the creation of brand engagement in self-concept. Design/methodology/approach Consumers with usage experience of branded emoticons in MIM apps were recruited to complete an online survey. Partial least squares structural equation modelling was used to analyze the data. Findings Two facets of cuteness – kindchenschema cuteness and whimsical cuteness – can project a favourable social image to consumers that facilitates playfulness in social interaction and enhances brand engagement in self-concept, which leads to their willingness to purchase the brand and stickiness to the MIM apps. Originality/value The popularity of branded emoticons represents a new form of social interaction and an innovative way to build brand relationships. The present study is the first to examine the design aspects of branded emoticons and highlights that the cuteness of a branded emoticon may be a crucial factor in engaging consumers in MIM.

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Chairunnisa, Sabrina, and Benedictus A.S. "Analysis of Emoji and Emoticon Usage in Interpersonal Communication of Blackberry Messenger and WhatsApp Application User." International Journal of Social Sciences and Management 4, no. 2 (April 24, 2017): 120–26. http://dx.doi.org/10.3126/ijssm.v4i2.17173.

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The aims of this research are: 1) to know the display of emoji and emoticon in Blackberry Messenger and WhatsApp application, 2) to know the usage of emoji and emoticon in Blackberry Messenger and WhatsApp application on interpersonal communication, and 3) to know the role of emoji and emoticon usage on interpersonal communication. In this research, the method used was qualitative research method. This research was based on media richness theory. Through the communication channel, the message will have high noise level so that it could be conveyed perfectly. Research results showed that emoji and emoticon have very important role in interpersonal communication. In media richness theory, Blackberry Messenger and WhatsApp media had fulfilled the criteria. 1) Ability of communication channel in conveying message signals such as facial expression, body movement, and vocal inflection; 2) Feedbacks are directly given by the message receiver as response to every chats, 3) Variety of language such as the presence of symbols and foreign language, and 4) Ability of communication channel in conveying personality such as showing personal emotion. This research concluded that emoji and emoticon have important role in interpersonal communication to enhance the text message’s meaning.Int. J. Soc. Sc. Manage. Vol. 4, Issue-2: 120-126

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Mohd Zukhi, Mohd Zhafri, Azham Hussain, and Husniza Husni. "Culturicon Design Model for Social Mobile Application." International Journal of Interactive Mobile Technologies (iJIM) 14, no. 05 (April 7, 2020): 16. http://dx.doi.org/10.3991/ijim.v14i05.13313.

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The usage of emoticon in computer-mediated communication has been growing rapidly among users, especially in social media. Emoticon has been used to express feelings, emotions, gestures, actions and places. Despite the growing number of emoticon users around the world, study on the cultural elements of the emoticon is still lacking. This research aims to propose a model for the development of Culturicon, which is Cultural-Based Emoticon. In doing so, a verification process must be done to the proposed model to ensure that the model is well verified. Expert review method was used for the verification method. Experts from the field of Human-Computer Interaction, User Experience and cultural study especially the academicians were chosen. In addition, application developer and graphic designer also were chosen as expert from the industry. The experts were approached by email and performed the verification by answering online questionnaire provided. The result obtained from these experts were analyzed and amendment were made based from the comments and suggestions. Results showed that 91% experts agreed the connections and flows of all components in the proposed model are logical and readable. Expert verification is important to ensure that the model is being develop correctly. By having this model, it can aid designer and developer in designing meaningful and effective Culturicon.

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Xiang, Wei, Shi Chen, Lingyun Sun, Shiwei Cheng, and V. Michael Bove. "Odor emoticon: An olfactory application that conveys emotions." International Journal of Human-Computer Studies 91 (July 2016): 52–61. http://dx.doi.org/10.1016/j.ijhcs.2016.04.001.

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Kusumasari, Lita, Kusumasari Wahyu Anggoro, and Miswanto . "The Effectiveness between Emoticons and Traditional Figures on Presenting Accounting Information." International Journal of Engineering & Technology 7, no. 3.30 (August 24, 2018): 348. http://dx.doi.org/10.14419/ijet.v7i3.30.18331.

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Accounting is business language. A language has two components that are symbol and grammatical rule. Accounting information usually presented as the set of procedures for creating financial reports. Based on framework for the preparation and presentation of financial statements, information provided in financial statements are useful to users if it has qualitative characteristics. One of the characteristics is understandability. For this purpose, users are assumed to have a reasonable knowledge of business and economic activities and accounting and a willingness to study the information with reasonable diligence. Accounting information usually presents as numbers and conventional graph. Previous studies suggest that schematic faces is used as alternative communication for accounting information. Existing application of schematic faces is found by Chernoff. In the modern age, people is get used to know emoticon as symbol for communicate easily, so this paper employs emoticon not the Chernoff faces. This research is aimed to know the impacts on the effectiveness between emoticon and traditional figure on presenting accounting information. The analysis of company net income, current ratio, liquidity ratio, return on asset and earning per share are presented in traditional figures and emoticons. Respondent has to find out which company is better performance compare to the other. Method of analysis are descriptive statistic and differential t-test. Respondents are accounting and management students which are taken by purposive random sampling. The result show that respondents have better average score on choosing the better company using emoticon (88.18) compares to traditional figure (82.72). The effectiveness of financial information is also significantly evidenced in emoticons compare to traditional figure.

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Hanafi, Yoandini, Muhammad Basri D, and Hadijah Hadijah. "A Semiotic Analysis Of Emoticon Symbols on Whatsapp Messenger In Students’ Chat Activies." Tamaddun 20, no. 1 (August 31, 2021): 92–105. http://dx.doi.org/10.33096/tamaddun.v20i1.92.

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The purpose of this study was to find out more about the meaning of semiotic and the use of emoticon symbols and how students view the use of emoticon symbols on WhatsApp messenger. This study uses a qualitative method. To collect data and determine research subjects, the authors used a questionnaire and observation as research instruments. The results of this study prove that most students mean the meaning of each emoticon symbol on WhatsApp and often use emoticon symbols in chat activities on WhatsApp messenger. They often express feelings and expressions through the symbol emoticon. Two main points are discussed in this thesis. First, the semiotic meaning and use of emoticon symbols, and the second, students' views on the use of emoticon symbols on WhatsApp messenger.

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Manganari, Emmanouela E., Evangelos Mourelatos, and Efthalia Dimara. "Beyond the Lexical Sense of Online Reviews: The Role of Emoticons and Consumer Experience." Interacting with Computers 32, no. 5-6 (April 29, 2020): 475–89. http://dx.doi.org/10.1093/iwc/iwab004.

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Abstract The present study focuses on the effect of emoticon use in online consumer reviews (OCRs) on consumers’ booking intention and the moderating effect of consumer personal characteristics. Consumers’ prior experience and their reliance on OCRs are embedded in the research model. A 2 × 2 (review valence * emoticon use) experimental study is designed, and an econometric model is used. Results show that the interaction between review valence and emoticons affect booking intention. Consumers with no prior experience are mainly affected by the cognitive aspects of their experience (i.e. review credibility and attitude toward the review) while experienced consumers are affected by the experiential aspects of booking process (i.e. entertainment, satisfaction and social influence). Consumers that rely on OCRs are affected by emoticons while consumers without review reliance are affected by emoticons only in the case of positive reviews. The personalization of websites and the provision of a focused list of emoticons can be adopted by managers to enhance OCRs effectiveness and the online shopping experience as a whole.

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Mehta, Ansh. "Emotion Detection using Social Media Data." International Journal for Research in Applied Science and Engineering Technology 9, no. 11 (November 30, 2021): 1456–59. http://dx.doi.org/10.22214/ijraset.2021.39027.

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Abstract: Previous research on emotion recognition of Twitter users centered on the use of lexicons and basic classifiers on pack of words models, despite the recent accomplishments of deep learning in many disciplines of natural language processing. The study's main question is if deep learning can help them improve their performance. Because of the scant contextual information that most posts offer, emotion analysis is still difficult. The suggested method can capture more emotion sematic than existing models by projecting emoticons and words into emoticon space, which improves the performance of emotion analysis. In a microblog setting, this aids in the detection of subjectivity, polarity, and emotion. It accomplishes this by utilizing hash tags to create three large emotion-labeled data sets that can be compared to various emotional orders. Then compare the results of a few words and character-based repetitive and convolutional neural networks to the results of a pack of words and latent semantic indexing models. Furthermore, the specifics examine the transferability of the most recent hidden state representations across distinct emotional classes and whether it is possible to construct a unified model for predicting each of them using a common representation. It's been shown that repetitive neural systems, especially character-based ones, outperform pack-of-words and latent semantic indexing models. The semantics of the token must be considered while classifying the tweet emotion. The semantics of the tokens recorded in the hash map may be simply searched. Despite these models' low exchange capacities, the recently presented training heuristic produces a unity model with execution comparable to the three solo models. Keywords: Hashtags, Sentiment Analysis, Facial Recognition, Emotions.

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Tan, Laurence, Hui Jin Toh, Lai Kiow Sim, and James Alvin Low. "Sensitivity and Specificity Analysis: Use of Emoticon for Screening of Depression in Elderly in Singapore." Journal of the American Psychiatric Nurses Association 24, no. 5 (March 30, 2018): 452–56. http://dx.doi.org/10.1177/1078390318766665.

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BACKGROUND: The current screening tools for depression can be tedious to administer, especially in the elderly population with hearing impairment and/or limited proficiency in English language. OBJECTIVES: To look at the feasibility of using emoticon as a screening and assessment tool for depression in the elderly. DESIGN: Cross-sectional study. METHOD: A total of 77 elderly patients completed the study from June 2014 to August 2015 in a general geriatric outpatient clinic of an acute care hospital in Singapore. Patients rated their mood using an emoticon scale, which ranges from 1 ( most happy face) to 7 ( most sad face). Depression was assessed using the Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM-IV) criteria as the gold standard. Sensitivity and specificity for depression were calculated for the cutoff scores from 1 to 7 on the emoticon scale. RESULTS: The sensitivity percentages were low across all cutoff scores. The specificity was more than 90% for the cutoff score of 5 and above on the emoticon scale. However, all the patients who had depression diagnosed using the DSM-IV criteria did not have emoticon scores of 5 and above. CONCLUSIONS: The emoticon scale was easy to use, but its effectiveness in the screening of depression in the elderly needs to be explored further. The inability to use the emoticon scale as a tool may be the lack of measurements in the other domains of the DSM-IV criteria (sleep, energy, appetite, etc.), rather than failure of the emoticon scale to assess mood.

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Berlianty, Santy. "Makna Simbol EmoticonWhatsapp Dalam Komunikasi Kelompok Mahasiswa Stisipol Pahlawan 12." Studia Komunika: Jurnal Ilmu Komunikasi 3, no. 1 (July 10, 2020): 19–42. http://dx.doi.org/10.47995/jik.v3i1.38.

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Penelitian ini membahas tentang makna simbol emoticon WhatsApp dalam komunikasi kelompok mahasiswa STISIPOL Pahlawan 12. Penelitian ini bertujuan untuk mengetahui makna yang terkandung dalam emoticon WhatsApp yang digunakan oleh mahasiswa STISIPOL Pahlawan 12. Teori yang digunakan adalah teori segitiga makna Charles Sanders Pierce yang terdiri dari tanda, objek, dan makna. Jenis penelitian yang digunakan adalah penelitian kualitatif dengan pendekatan deskriptif kualitatif. Sumber data meliputi data primer dan data sekunder, sedangkan teknik pengumpulan data menggunakan observasi, wawancara, dan dokumentasi. Hasil dari penelitian adalah mahasiswa STISIPOL Pahlawan 12 memahami makna yang terkandung dalam setiap emoticon yang mereka gunakan. Emoticon yang digunakan meliputi emoticon dari kategori smiley dan orang yang meliputi ekspresi wajah seperti halnya tertawa, tersenyum, terkejut maupun menangis, yang diinterpretasikan sebagai ungkapan untuk memberi semangat, dukungan, mengekspresikan kebahagiaan, kesedihan, mengungkapkan perasaan malu-malu atau menyindir seseorang, mengungkapkan rasa canggung, permohonan maaf, rasa syukur, penuh cinta, kecewa, kesombongan, dan kekesalan yang dirasakan.

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Lee, Hyun-Ki, and Janghoon Yang. "Design of Character Emoticon based on Sex for Efficient Emotion Communication." Journal of Digital Contents Society 19, no. 9 (September 30, 2018): 1641–52. http://dx.doi.org/10.9728/dcs.2018.19.9.1641.

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Islam, Juyana, M. A. H. Akhand, Md Ahsan Habib, Md Abdus Samad Kamal, and Nazmul Siddique. "Recognition of Emotion from Emoticon with Text in Microblog Using LSTM." Advances in Science, Technology and Engineering Systems Journal 6, no. 3 (June 2021): 347–54. http://dx.doi.org/10.25046/aj060340.

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Lee, Seung Young. "A Study on the Identification Theory in Mobile Messenger Emoticon and Brand Emoticon." JOURNAL OF THE KOREAN SOCIETY DESIGN CULTURE 24, no. 1 (March 31, 2018): 481–93. http://dx.doi.org/10.18208/ksdc.2018.24.1.481.

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Suwarti, Tarcisia Sri, Akmal Akmal, and Jafar Sodiq. "IBM BAGI TUTOR PAUD ANAK BANGSA VI KELURAHAN TANDANG SEMARANG." E-DIMAS 7, no. 2 (October 28, 2016): 98. http://dx.doi.org/10.26877/e-dimas.v7i2.1138.

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Pendidikan anak usia dini (PAUD) adalah jenjang pendidikan sebelum jenjang pendidikan dasar yang merupakan suatu upaya pembinaan yang ditujukan bagi anak sejak lahir sampai dengan usia enam tahun agar anak memiliki kesiapan dalam memasuki pendidikan lebih lanjut. Kelurahan Tandang, Semarang memiliki 14 kelompok PAUD yang masing-masing memiliki mayoritas tutor ibu rumah tangga yang kurang memiliki bekal pengetahuan dalam pengelolaan PAUD. Dilihat dari sisi lain, anak-anak usia dini pada era sekarang sedikit banyak telah mengenal berbagai teknologi modern yang mendukung mereka untuk mengenal dunia di sekitarnya termasuk penggunaan fitur emoticon yang terdapat dalam teknologi modern seperti handphone. Oleh karena itu, untuk mengimbangi perkembangan pengetahuan siswanya, para tutor PAUD Anak Bangsa VI di Kelurahan Tandang, Semarang perlu dibekali teknik-teknik pengajaran PAUD secara khusus dalam hal teknik pengajaran kosakata bahasa kepada siswa melalui media emoticon sehingga bisa menarik minat siswa untuk memperluas kosakata bahasa mereka. Berkenaan dengan hal ini, tim pelaksana memberikan pelatihan berupa pembekalan yang berisi tentang teori dan teknik pengajaran kosakata bahasa dengan menggunakan media emoticon. Dalam pembekalan tersebut, tim juga menyediakan fasilitas mengajar berupa media gambar emoticon dengan tampilan yang menarik. Setelah dilakukan pelatihan tersebut, selanjutnya peserta mulai diajak untuk mempraktekan teknik mengajar tersebut kepada siswa PAUD yang dibimbingnya. Luaran yang telah dicapai dalam program pengabdian kepada masyarakat ini adalah produk pengabdian berupa emoticon card, para tutor PAUD dapat memahami teori mengenai teknik pengajaran kosakata bahasa kepada siswa PAUD, para tutor PAUD dapat langsung menerapkan teknik pengajaran kosakata bahasa dengan menggunakan media emoticon kepada siswa didiknya, sehingga para siswa PAUD memiliki semangat untuk mengikuti kegiatan dalam PAUD karena penggunaan media yang menarik tersebut. Selain itu, dengan penggunaan media tersebut dapat mempermudah anak untuk menangkap informasi dan memperluas pengetahuan mengenai kosakata bahasa. Kata kunci: emoticon, paud, kosakata, bahasa

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Shin, Jeoung Eun, and Juhyun Eune. "A Study on Emoticon Design for Active Seniors - focused on KakaoTalk Emoticons -." Journal of Basic Design & Art 20, no. 2 (April 30, 2019): 209–20. http://dx.doi.org/10.47294/ksbda.20.2.16.

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Budiša, Dajana, Ajla Halilović, Ljiljana Jovanović, Nedeljka Prole, and Svetlana Borojević. "DEPRESIVNOSTI VIZUELNO PAMĆENJE SPECIFIČNIH STIMULUSA (EMOTIKONA)." ГОДИШЊАК ЗА ПСИХОЛОГИЈУ 18, no. 1 (December 13, 2021): 89–105. http://dx.doi.org/10.46630/gpsi.18.2021.06.

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Depression is a state of reduced psychophysical activity that is accompanied by various changes in cognitive, emotional and social functioning. Previous studies have found that depression leads to changes in the recognition of the emotions of others, makes it difficult to direct attention and significantly impairs visual memory. The main goal of this research is to examine the relations between depressive symptoms and visual memory of specific stimuli that show emotions. We also want to examine whether the intensity of depressive symptoms is related to longer reaction time in the experimental task, as well as whether the accuracy of the emoticon’s visual memory depends on the set size. The research was conducted on a sample of 84 participants, students of Faculty of Philosophy in Banja Luka (90% female). The PHQ-9 questionnaire was used to assess depressive symptoms. Visual memory task was created in SuperLab 4.1. for Windows.The results show that there is a partial contribution of moderate depression to the accuracy of emoticon memory with sadness expression. No partial contribution of any category of depression to the memory accuracy of emoticons with the expression of happiness has been determined. A statistically significant negative correlation for the category of “sad” stimuli was obtained between the expression of depressive symptoms and the response time in the experimental task, while no statistically significant correlation was obtained for the second category of stimuli. It was also found that the number of errors increases with the set size. These results can be explained by negative bias and cognitive load in information processing. Key words: visual memory, depression, emoticons, expression of happiness, expression of sadness

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Liu, Chang, Taiao Liu, Shuojue Yang, and Yajun Du. "Individual Emotion Recognition Approach Combined Gated Recurrent Unit With Emoticon Distribution Model." IEEE Access 9 (2021): 163542–53. http://dx.doi.org/10.1109/access.2021.3124585.

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Liu, Zheng. "The Design of Emoticon-Generating Device Based on Artificial Neural Network." Wireless Communications and Mobile Computing 2022 (February 17, 2022): 1–11. http://dx.doi.org/10.1155/2022/3518757.

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Along with the rapid development of the new internet media, people’s lives are becoming increasingly digital, information is diversified, and communication methods have undergone significant changes. “Emoticon pack,” as a kind of visual symbol that was generated and developed in the context of the internet, can use nonverbal symbols such as words, images, symbols, and other nonverbal symbols to simulate expressions, posture, and movements and a new expression and cultural phenomenon. In this study, the principle of neural network optimization is analyzed by applying the particle swarm algorithm, incorporating the harmonic search algorithm to neural network optimization, and the principle of neural network optimization is analyzed. This study is aimed at college students, investigating the consumption status of emoticon packs and the consumption amount of emoticon packs for men and women; among them, people who only use free emoji packs and those who only use emoji packs under 10 yuan account for the highest proportion, accounting for 73.07% and 22.41% of the surveyed people. Among the people who buy emoticons, boys are more likely to have large consumption behaviors than girls. In the consumption segment below ten yuan and above 150 yuan, the ratio of men to women is the same; in the consumption range of 11–50 yuan, the number of girls exceeds than that of boys, accounting for 70% of the number; however, in the consumption range of 51 yuan to 150 yuan, the proportion of boys surpassed than that of girls and the number of consumers accounted for more than 60%.

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Fullwood, Chris, Lisa J. Orchard, and Sarah A. Floyd. "Emoticon convergence in Internet chat rooms." Social Semiotics 23, no. 5 (November 2013): 648–62. http://dx.doi.org/10.1080/10350330.2012.739000.

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BANG, Gyo Young. "Cultural Anthropology on Emoticon : Russian Case." Journal of The Korean Society of Illustration Research 55 (June 30, 2018): 7–16. http://dx.doi.org/10.37379/jksir.2018.55.1.

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Lawrence, Louise, and Deborah Abdel Nabi. "The Compilation and Validation of a Collection of Emotional Expression Images Communicated by Synthetic and Human Faces." International Journal of Synthetic Emotions 4, no. 2 (July 2013): 34–62. http://dx.doi.org/10.4018/ijse.2013070104.

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The BARTA (Bolton Affect Recognition Tri-Stimulus Approach) is a unique database comprising over 400 colour images of the universally recognised basic emotional expressions and is the first compilation to include three different classes of validated face stimuli; emoticon, computer-generated cartoon and photographs of human faces. The validated tri-stimulus collection (all images received =70% inter-rater (child and adult) consensus) has been developed to promote pioneering research into the differential effects of synthetic emotion representation on atypical emotion perception, processing and recognition in autism spectrum disorders (ASD) and, given the recent evidence for an ASD synthetic-face processing advantage (Rosset et al., 2008), provides a means of investigating the benefits associated with the recruitment of synthetic face images in ASD emotion recognition training contexts.

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Hasan, Farida, and Maemuna Muhayyang. "The Linguistic Features Uniqueness of the Students’ Written Discourse in Online Learning." ELT Worldwide: Journal of English Language Teaching 5, no. 1 (May 7, 2018): 73. http://dx.doi.org/10.26858/eltww.v5i1.5777.

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This research aimed at finding and describing the types and the uniqueness of linguistic features used by the students in their written discourse in BritishEnglishClass.com. The researcher applied a descriptive qualitative method. The data collection was done using document analysis in form of chat history in Whatsapp and taking ten students as the participants of this study. The result of the analysis showed that the students used six types of linguistic features namely (1) Lexical features in terms of the use of interjection, abbreviation, word letter replacement, word combination, code switching, code mixing and diction, (2) Orthographic features in terms of word spelling and capitalization, (3) Grammatical features in terms of ellipsis, passive voice, verb use, and personal pronoun, (4) Discourse features in terms of the use of interactional features and the stream of consciousness, (5) Paralinguistic and Graphic in terms of emoticon usage and excessive punctuation and, (6) other features in terms of written out laughter . These features are unique because they are different from the standard form of the language. It can be seen also from the use of abbreviation and ellipsis that shorten the students’ sentence, the use of emoticon representing the emotions and psychical activity, the use of lower and upper case to represent the sound and the intonation of the student’ sentence, the abandonment of convention of capitalization of proper nouns and the first words of sentences, and the creative orthography of some words. The linguistic features used by the students were different from the standard form and their function that allowed the student to express and emphasize their intention, meaning and emotion in the chat room make the students’ written discourse in online learning is uniqueKeywords: uniqueness, linguistic features

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Choi, Bo Ah. "Preference Survey following the Emoticon Character Design Expression Type - Focusing on the emoticon of KakaoTalk and Line -." Journal of Cultural Product and Design 52 (March 31, 2018): 135–44. http://dx.doi.org/10.18555/kicpd.2018.52.13.

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Asma Sulaiman, Nor Anis, and Leelavathi Rajamanickam. "The State Art of Text Sentiment from Opinions to Emotion Mining." Journal of Engineering & Technological Advances 5, no. 2 (2020): 43–52. http://dx.doi.org/10.35934/segi.v5i2.43.

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This study is aiming to analyse the feelings expressed by the users in a text on a comment posted on social media. Text Mining and Emotion Mining can be analysed by using both technique of Natural Processing Language (NLP). Mostly on the previous study of text mining is using unsupervised technique and referring to Ekman’s Emotion Model (EEM) but it has restrained coverage of polarity shifters, negations and lack emoticon. In this study have proposed a Naïve Bayes algorithm as a tool to produce users’ emotion pattern. The most important contribution of this study is to visualize the emotion’s theory with the text sentiment based on the computational methods for classifying users’ feelings from natural language text. Then, the general system framework of extracting opinions to emotion mining has produced and capable use in any domains.

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Kim, Seung Min. "A Study on Emoticon Design in Publications." Korean Publishing Science Society 83 (September 30, 2018): 77–99. http://dx.doi.org/10.21732/skps.2018.83.77.

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HIDAKA, Motonobu, Tetsuko KASAI, and Harumitsu MUROHASHI. "Is the N170 influenced by inverted emoticon?" Proceedings of the Annual Convention of the Japanese Psychological Association 75 (September 15, 2011): 3AM060. http://dx.doi.org/10.4992/pacjpa.75.0_3am060.

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Jiang, Fei, Yi-Qun Liu, Huan-Bo Luan, Jia-Shen Sun, Xuan Zhu, Min Zhang, and Shao-Ping Ma. "Microblog Sentiment Analysis with Emoticon Space Model." Journal of Computer Science and Technology 30, no. 5 (September 2015): 1120–29. http://dx.doi.org/10.1007/s11390-015-1587-1.

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Jagadishwari, V., A. Indulekha, Kiran Raghu, and P. Harshini. "Sentiment analysis of Social Media Text-Emoticon Post with Machine learning Models Contribution Title." Journal of Physics: Conference Series 2070, no. 1 (November 1, 2021): 012079. http://dx.doi.org/10.1088/1742-6596/2070/1/012079.

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Abstract Social Media is an arena in recent times for people to share their perspectives on a variety of topics. Most of the social interactions are through the Social Media. Though all the Online Social Networks allow users to express their views and opinions in many forms like audio, video, text etc, the most popular form of expression is text, Emoticons and Emojis. The work presented in this paper aims at detecting the sentiments expressed in the Social Media posts. The Machine Learning Models namely Bernoulli Bayes, Multinomial Bayes, Regression and SVM were implemented. All these models were trained and tested with Twitter Data sets. Users on Twitter express their opinions in the form of tweets with limited characters. Tweets also contain Emoticons and Emojis therefore Twitter data sets are best suited for the sentiment analysis. The effect of emoticons present in the tweet is also analyzed. The models are first trained only with the text and then they are trained with text and emoticon in the tweet. The performance of all the four models in both cases are tested and the results are presented in the paper.

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Rodrigues, David, Marília Prada, Rui Gaspar, Margarida V. Garrido, and Diniz Lopes. "Lisbon Emoji and Emoticon Database (LEED): Norms for emoji and emoticons in seven evaluative dimensions." Behavior Research Methods 50, no. 1 (March 31, 2017): 392–405. http://dx.doi.org/10.3758/s13428-017-0878-6.

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Saeed, Sehrish, and Dr Muhammad Arfan Lodhi. "A Study of Semiotic and Linguistic Gap Between the Meaning and the Usage of Emoticons in Pakistani Society." English Literature and Language Review, no. 64 (April 5, 2020): 37–47. http://dx.doi.org/10.32861/ellr.64.37.47.

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Current research deals with the semiotic and linguistics gap between the actual meaning and the usage of Emoticons in Pakistani society. This study aimed to find out the gap between the meaning and the use of Emoticon and to examine the use and place of Emoticons as a form of language. This research also aimed to identify whether men or women tend to use Emoticons more frequently. To analyze the gap, a survey questionnaire was used having 45 questions as tool for collecting data from mobile phone users and 05 conversations have been analyzed using content analysis technique. According to the results, mobile phone users use Emoticons in their conversations without knowing what they mean, whether they have any referential or literal meaning or which conceptual categories they denote, they do not know the answers to all these questions. This research tried to give valuable suggestions in order to solve the problems of using Emoticons without proper understandings. On the basis of findings, it is suggested that Emoticons must not give false and fake expressions rather these Emoticons should prevent misapprehension and confusion and should be aligned with the receiver.

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Park, Jung-Hoon, and Seung-in Kim. "Usefulness of Six emoticon newly adapted to facebook." Journal of Digital Convergence 14, no. 9 (September 28, 2016): 417–22. http://dx.doi.org/10.14400/jdc.2016.14.9.417.

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Dubrofsky, Rachel E. "Frayed Edges: Selfies, Auschwitz, and a Blushing Emoticon." a/b: Auto/Biography Studies 33, no. 3 (September 2, 2018): 597–605. http://dx.doi.org/10.1080/08989575.2018.1499491.

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Kadge, Sanam, Saba Panchbhai, Sakina Padghawala, and Kausar Chhipa. "Emoticon Analysis with Dynamic Text based Opinion Mining." International Journal of Computer Applications 136, no. 9 (February 17, 2016): 29–36. http://dx.doi.org/10.5120/ijca2016908573.

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Wolf, Alecia. "Emotional Expression Online: Gender Differences in Emoticon Use." CyberPsychology & Behavior 3, no. 5 (October 2000): 827–33. http://dx.doi.org/10.1089/10949310050191809.

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Kumari, S. Surya, and G. Anjan Babu. "Sentiment classification using unlabelled data with emoticon classification." International Journal of Knowledge Engineering and Soft Data Paradigms 7, no. 1 (2020): 1. http://dx.doi.org/10.1504/ijkesdp.2020.10034771.

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Kumari, S. Surya, and G. Anjan Babu. "Sentiment classification using unlabelled data with emoticon classification." International Journal of Knowledge Engineering and Soft Data Paradigms 7, no. 1 (2020): 1. http://dx.doi.org/10.1504/ijkesdp.2020.112616.

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Zou, Haochen, and Kun Xiang. "Sentiment Classification Method Based on Blending of Emoticons and Short Texts." Entropy 24, no. 3 (March 12, 2022): 398. http://dx.doi.org/10.3390/e24030398.

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With the development of Internet technology, short texts have gradually become the main medium for people to obtain information and communicate. Short text reduces the threshold of information production and reading by virtue of its short length, which is in line with the trend of fragmented reading in the context of the current fast-paced life. In addition, short texts contain emojis to make the communication immersive. However, short-text content means it contains relatively little information, which is not conducive to the analysis of sentiment characteristics. Therefore, this paper proposes a sentiment classification method based on the blending of emoticons and short-text content. Emoticons and short-text content are transformed into vectors, and the corresponding word vector and emoticon vector are connected into a sentencing matrix in turn. The sentence matrix is input into a convolution neural network classification model for classification. The results indicate that, compared with existing methods, the proposed method improves the accuracy of analysis.

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Journal articles on the topic 'Emoticon'

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