Recognition of Affect Conveyed by Text Messaging in Online Communication

This chapter addresses the tasks of recognition, interpretation and visualization of affect communicated through text messaging in virtual communication environments. In order to facilitate sensitive and expressive communication in such environments, we introduced a novel syntactic rule-based approach to affect recognition from text. Our Affect Analysis Model follows the compositionality principle, according to which emotional meaning of a sentence is determined by composing parts that correspond to lexical units or other linguistic constituent types governed by the rules of aggregation, propagation, domination, neutralization, and intensification, at various grammatical levels. The proposed rule-based approach processes each sentence in sequential stages, including symbolic cue processing, detection and transformation of abbreviations, sentence parsing, and word/phrase/sentence-level analyses. Our method is capable of processing sentences of different complexity, including simple, compound, complex (with complement and relative clauses), and complex-compound sentences. Affect in text is classified into nine emotion categories (or neutral), and, additionally, information that indicates social communicative behaviour is identified. The evaluation of the Affect Analysis Model algorithm showed promising results regarding its capability to accurately recognize affective information in text from an existing corpus of informal online conversations. The applications of the developed Affect Analysis Model in Instant Messaging system (AffectIM) and in Second Life (EmoHeart, iFeel_IM!) are described in the chapter.

In this paper, we address the tasks of recognition and interpretation of affect communicated through text messaging. The evolving nature of language in online conversations is a main issue in affect sensing from this media type, since sentence parsing might fail while syntactical structure analysis. The developed Affect Analysis Model was designed to handle not only correctly written text, but also informal messages written in abbreviated or expressive manner. The proposed rule-based approach processes each sentence in sequential stages, including symbolic cue processing, detection and transformation of abbreviations, sentence parsing, and word/phrase/sentence-level analyses. In a study based on 160 sentences, the system result agrees with at least two out of three human annotators in 70% of the cases. In order to reflect the detected affective information and social behaviour, an avatar was created.KeywordsAffective sensing from textaffective user interfaceavataremotionsonline communicationlanguage parsing and understandingtext analysis

In this paper, we address the tasks of recognition and interpretation of affect communicated through text messaging in online communication environments. Specifically, we focus on Instant Messaging (IM) or blogs, where people use an informal or garbled style of writing. We introduced a novel rule-based linguistic approach for affect recognition from text. Our Affect Analysis Model (AAM) was designed to deal with not only grammatically and syntactically correct textual input, but also informal messages written in an abbreviated or expressive manner. The proposed rule-based approach processes each sentence in stages, including symbolic cue processing, detection and transformation of abbreviations, sentence parsing and word/phrase/sentence-level analyses. Our method is capable of processing sentences of different complexity, including simple, compound, complex (with complement and relative clauses) and complex–compound sentences. Affect in text is classified into nine emotion categories (or neutral). The strength of the resulting emotional state depends on vectors of emotional words, relations among them, tense of the analysed sentence and availability of first person pronouns. The evaluation of the Affect Analysis Model algorithm showed promising results regarding its capability to accurately recognize fine-grained emotions reflected in sentences from diary-like blog posts (averaged accuracy is up to 77 per cent), fairy tales (averaged accuracy is up to 70.2 per cent) and news headlines (our algorithm outperformed eight other systems on several measures).

User study on AffectIM, an avatar-based Instant Messaging system employing rule-based affect sensing from text

Our research addresses the tasks of recognition, interpretation and visualization of affect communicated through text messaging. In order to facilitate sensitive and expressive interaction in computer-mediated communication, we previously introduced a novel syntactical rule-based approach to affect recognition from text. The evaluation of the developed Affect Analysis Model showed promising results regarding its capability to accurately recognize affective information in text from an existing corpus of informal online conversations. To enrich the user's experience in online communication, make it enjoyable, exciting and fun, we implemented a web-based IM application, AffectIM, and endowed it with emotional intelligence by integrating the developed Affect Analysis Model. This paper describes the findings of a twenty-person study conducted with our AffectIM system. The results of the study indicated that automatic emotion recognition function can bring a high level of affective intelligence to the IM application.

Unlike sentiment analysis which detects positive, negative, or neutral sentences, textual affect sensing tries to detect more detailed affective or emotional states appearing in text, such as joy, sadness, anger, fear, disgust, surprise and much more. The authors describe here their following two approaches for textual affect sensing: The first one detects nine emotions using a set of rules implemented on the basis of a linguistic compositionality principle for textual affect interpretation. This process includes symbolic cue processing, detection and transformation of abbreviations, sentence parsing, and word/phrase/sentence-level analyses. The second one challenged to recognize 22 emotion types defined in the OCC (Ortony, Clore & Collins) emotion model, which is the most comprehensive emotion model and employs several cognitive variables. In this research, we have shown how these cognitive variables of the emotion model can be computed from linguistic components in text. These two approaches have exploited detailed level analyses of text in two different ways more than ever towards textual affect sensing. Applications towards affective communication are also outlined, including affective instant messaging, affective chat in 3D virtual world, affective haptic interaction, and online news classification relying on affect.

Reflective learning is a teaching method where active student learning is a core component of instructional design. In mathematical instructions, reflection and knowledge-construction take place in the language of mathematics. The development of mathematics module in the current study employs three computer-mediated communication (CMC) tools: Google Classroom, discussion forum and text messaging. This study seeks to determine the communication and learning forms which support reflective learning in the usage of CMC tools within the mathematics module. 30 Form 2 students with different mathematical abilities were selected as participants. Data collection include students’ online communication on Google Classroom (GC), forum discussion, and text messaging. A survey of students’ perception of communication using CMC tools and learning were also distributed. Google Classroom and text messaging were reported to have the highest frequency of communication among learners. The combined usage of the three CMC tools was found to be effective as it caters to learners’ preferred learning styles, encouraging collaboration and experiential learning. This study offers insights into group interaction in a reflective learning environment and its influence on the scaffolding of mathematics knowledge.

In the quest to discover the next high-technology solution to solve many health problems, proven established technologies are often overlooked in favor of more “technologically advanced” systems that have not been fully explored for their applicability to support behavior change theory, or used by consumers. Text messages or SMS is one example of an established technology still used by consumers, but often overlooked as part of the mobile health (mHealth) toolbox. The purpose of this paper is to describe the benefits of text messages as a health promotion modality and to advocate for broader scale implementation of efficacious text message programs.Text messaging reaches consumers in a ubiquitous real-time exchange, contrasting the multistep active engagement required for apps and wearables. It continues to be the most widely adopted and least expensive mobile phone function. As an intervention modality, text messaging has taught researchers substantial lessons about tailored interactive health communication; reach and engagement, particularly in low-resource settings; and embedding of behavior change models into digital health. It supports behavior change techniques such as reinforcement, prompts and cues, goal setting, feedback on performance, support, and progress review. Consumers have provided feedback to indicate that text messages can provide them with useful information, increase perceived support, enhance motivation for healthy behavior change, and provide prompts to engage in health behaviors. Significant evidence supports the effectiveness of text messages alone as part of an mHealth toolbox or in combination with health services, to support healthy behavior change. Systematic reviews have consistently reported positive effects of text message interventions for health behavior change and disease management including smoking cessation, medication adherence, and self-management of long-term conditions and health, including diabetes and weight loss. However, few text message interventions are implemented on a large scale. There is still much to be learned from investing in text messaging delivered research. When a modality is known to be effective, we should be learning from large-scale implementation. Many other technologies currently suffer from poor long-term engagement, the digital divide within society, and low health and technology literacy of users. Investing in and incorporating the learnings and lessons from large-scale text message interventions will strengthen our way forward in the quest for the ultimate digitally delivered behavior change model.

Does online communication mitigate the association between a decrease in face-to-face communication and laughter during the COVID-19 pandemic? A cross-sectional study from JACSIS study

Emojis or emoticons are commonly used to convey emotional status to others in text‐based, online communication. While several studies have investigated the influence of emojis on emotional processing, the influence of emojis on the recognition of messages is less understood. In the present study, we investigated the effects of emojis accompanying a short text message on the emotional impressions and memory of the messages. The results suggested that emojis modulated the emotional processing of the messages; the emotional arousal of the messages increased by adding emojis, and the emotional valence of messages was biased towards the valence of emojis. Furthermore, we found that the memory of the text messages was modulated by emojis; the recognition performance of the positive text messages was improved when they appeared with negative emojis. These results implied that emojis would have an impact on cognitive processing, as well as the emotional processing of text messages.

In recent years, information hiding technology has developed from the focus of verifying the authenticity into Internet communication as an effective means of enhancing its safety secretly. High transmission efficiency, low resource occupancy and intelligible meaning make text message as the most commonly used type of media in our daily communication. But, text message is difficult to hide secret information effectively and reliably because of its restriction of redundant information as well as the alterability in manual operation. This paper introduces the text steganography, an information hiding technology based on text message, and explores the application of Markov state transferring probability among the sequence of nature languages to achieve the purpose of text steganography in online communication. The above approach can realize information hiding in text message with the capability of immunity from regular operations, such as formatting, compressing and sometimes manual altering operation on its text attributes.

In addition to semantic content, affect conveyed by text plays an important role for rich and friendly communication. This is particularly true in human communication. In recent days, the percentage of human-computer and computer-mediated communications is increasing in our life. In this situation, a computer is expected to understand the affects or emotions included in text. We have been working on this problem, i.e., textual affect sensing, for some years. As a related topic, textual sentiment analysis has been studied, where positive and negative sentences are typically extracted for Web opinion mining with respect to a specific issue or product. While the distinction between affect sensing and sentiment analysis is not necessarily clear in this field, I call here sentiment analysis when a sentence is classified into positive, negative or neutral one. Unlike this sort of sentiment analysis, our textual affect sensing detects more detailed affective or emotional states appearing in text, such as happy, sad, anger, fear, disgust, surprise and much more. We basically have developed the following two such models or systems so far: (A) The first one detects nine emotions using a set of rules implemented on the basis of a compositionality principle proposed for textual affect interpretation. This process includes symbolic cue processing, detection and transformation of abbreviations, sentence parsing, and word/phrase/sentence-level analyses. (B) The second one challenged to recognize 22 emotion types defined in the OCC (Ortony, Clore & Collins) emotion model, which is the most comprehensive emotion model and employs several cognitive variables including one relating to valenced reactions of events or agents. In this research, we have shown how these cognitive variables of the emotion model can be computed from linguistic components in text.

The objective of this study was to evaluate the feasibility of using text messages to enhance mental health screening and education of women in the immediate postpartum period. A total of 937 postpartum women were recruited from an obstetrics and gynecology clinic of a large urban hospital. Participants received a text message containing a two-question screen for postpartum depression every two weeks and three text messages per week about postpartum mental health for the first 12 weeks postpartum. Those who screened positive were administered the Edinburgh Postnatal Depression Scale. They were matched with a subset of women who were also assessed with the Edinburgh Postnatal Depression Scale after screening negative for depression with the text messaging screen. At 12 to 13 weeks postpartum, all participants received an online survey assessing satisfaction with the text messages. Of 937 participants, 126 (13%) screened positive. Agreement between the texted screen and the Edinburgh Postnatal Depression Scale was moderate (κ=0.45), with good sensitivity (0.90, 95% confidence interval [95% CI]=0.81-0.96) and specificity (0.82, 95% CI=0.79-0.85). Nine hundred thirty (99%) participants responded to at least one of the six texted screens, whereas 632 (67%) responded to all six. Of the 589 (63%) who responded to the satisfaction survey, 459 (78%) recommended that all women be screened for postpartum depression via text messaging and that all women in the postpartum period be sent information texts about postpartum depression (N=504, 91%). Using text messaging technology to screen women for postpartum depression and provide information on postpartum mental health appears to be sensitive, feasible, and well accepted.

The purpose of this study is to determine which of the following factors influence children's online communication: parent–child communication (PCC), social self-efficacy (SSE), and unwillingness to communicate (UTC). To examine children's online communication, the researchers obtained survey data from 425 elementary school students in South Korea and tested a hypothesized structural model using EQS/Windows. The findings suggest that open communication between parents and children is associated with higher levels of SSE and lower levels of UTC among children. According to the two variables, open PCC has an indirect influence on interactive communication in online communities. Overall, this study offers meaningful results indicating that children's interactive online communication is influenced by their characteristics of interpersonal communication resulting from open PCC.

Sociability as a disposition describes a tendency to affiliate with others (vs. be alone). Yet, we know relatively little about how much social behavior people engage in during a typical day. One challenge to documenting social behavior tendencies is the broad number of channels over which socializing can occur, both in-person and through digital media. To examine individual differences in everyday social behavior patterns, here we used smartphone-based mobile sensing methods (MSMs) in four studies (total N = 926) to collect real-world data about young adults' social behaviors across four communication channels: conversations, phone calls, text messages, and use of messaging and social media applications. To examine individual differences, we first focused on establishing between-person variability in daily social behavior, examining stability of and relationships among daily sensed social behavior tendencies. To explore factors that may explain the observed individual differences in sensed social behavior, we then expanded our focus to include other time estimates (e.g., times of the day, days of the week) and personality traits. In doing so, we present the first large-scale descriptive portrait of behavioral sociability patterns, characterizing the degree to which young adults engaged in social behaviors and mapping these behaviors onto self-reported personality dispositions. Our discussion focuses on how the observed sociability patterns compare to previous research on young adults' social behavior. We conclude by pointing to areas for future research aimed at understanding sociability using mobile sensing and other naturalistic observation methods for the assessment of social behavior. (PsycInfo Database Record (c) 2020 APA, all rights reserved).