Text Conversion Research Articles

Natural language processing to evaluate texting conversations between patients and healthcare providers during COVID-19 Home-Based Care in Rwanda at scale.

Community isolation of patients with communicable infectious diseases limits spread of pathogens but our understanding of isolated patients' needs and challenges is incomplete. Rwanda deployed a digital health service nationally to assist public health clinicians to remotely monitor and support SARS-CoV-2 cases via their mobile phones using daily interactive short message service (SMS) check-ins. We aimed to assess the texting patterns and communicated topics to better understand patient experiences. We extracted data on all COVID-19 cases and exposed contacts who were enrolled in the WelTel text messaging program between March 18, 2020, and March 31, 2022, and linked demographic and clinical data from the national COVID-19 registry. A sample of the text conversation corpus was English-translated and labeled with topics of interest defined by medical experts. Multiple natural language processing (NLP) topic classification models were trained and compared using F1 scores. Best performing models were applied to classify unlabeled conversations. Total 33,081 isolated patients (mean age 33·9, range 0-100), 44% female, including 30,398 cases and 2,683 contacts) were registered in WelTel. Registered patients generated 12,119 interactive text conversations in Kinyarwanda (n = 8,183, 67%), English (n = 3,069, 25%) and other languages. Sufficiently trained large language models (LLMs) were unavailable for Kinyarwanda. Traditional machine learning (ML) models outperformed fine-tuned transformer architecture language models on the native untranslated language corpus, however, the reverse was observed of models trained on English-only data. The most frequently identified topics discussed included symptoms (69%), diagnostics (38%), social issues (19%), prevention (18%), healthcare logistics (16%), and treatment (8·5%). Education, advice, and triage on these topics were provided to patients. Interactive text messaging can be used to remotely support isolated patients in pandemics at scale. NLP can help evaluate the medical and social factors that affect isolated patients which could ultimately inform precision public health responses to future pandemics.

Media Conversion and Recontextualization of Biblical Text: Focusing on the Parable of the Prodigal Son

Media Conversion and Recontextualization of Biblical Text: Focusing on the Parable of the Prodigal Son

Stylistic Affiliation of Set Expressions with the Component MAYOR, MENOR in the Spanish Language

The article is devoted to the study of set expressions in the Spanish language, one of the components of which is the lexical unit mayor or menor, from the point of view of identifying their linguistic features when used in various functional styles of speech. The lexical units mayor and menor, which have the dictionary meaning of “greater” or “smaller”, acquire additional connotations in the composition of various phrases, on which their translation into Russian largely depends. The work considers the most commonly used set expressions fixed in the phraseological dictionary. The source of the study were texts of various genres characteristic of each style of speech: the Constitutions of Spain and Latin American countries, codes, laws, decrees, regulations, orders, contracts, agreements, business letters, works of Spanish-language fiction, texts of periodicals, articles, essays, reviews, conversations, interviews, blogs and comments on social networks. When analyzing stable expressions that actively function in the Spanish language, their semantic meaning is indicated, and their stylistic affiliation is determined.The study presents a classification of stable phrases with the components mayor / menor based on grammatical features, describes their lexical component, determines the component composition, and analyzes the evaluative connotation, functional and stylistic potential.The article studies language means in the field of Spanish stylistics and phraseology, which are of scientific interest to Hispanists in matters of identifying the national and cultural specificity of the Spanish language. The work also provides a brief overview of the history of linguistic research on stylistics in Spain and Russia, notes the importance of the functional direction of stylistic research, presents functional styles of speech and speech genres characteristic of them.All the studied set expressions are used in texts of various genres inherent in the functional styles of speech in the Spanish language, while each of them, due to its semantic and stylistic features, is used mainly in a certain style.The study of stylistic possibilities of set expressions with the components mayor / menor helps to identify genre features of texts characteristic of different functional styles of speech, and to determine the features of the formation of the linguistic picture of the world of Spanish-speaking peoples.The article may be useful for philologists, specialists in the field of Spanish studies, teachers of Spanish, students studying Spanish.

Introducing ReChat: A Lab-in-the-Cloud for Text Discussions

Abstract Text is a major medium of contemporary interpersonal communication but is difficult for social scientists to study unless they have significant resources or the skills to build their own research platform. In this paper, we introduce a cloud-based software solution to this problem: ReChat, an online research platform for conducting experimental and observational studies of live text conversations. We demonstrate ReChat by applying it to a specific phenomenon of interest to political scientists: conversations among co-partisans. We present results from two studies, focusing on (1) self-selection factors that make chat participants systematically unrepresentative and (2) a pre-registered analysis of loquaciousness that finds a significant association between speakers’ ideological extremity and the amount they write in the chat. We conclude by discussing practical implications and advice for future practitioners of chat studies.

AUDIO TO SIGN LANGUAGE CONVERSION

Due to high demand for inclusive technologies and the rapid electrons communication age, there is growing interest in automatic audio to sign language conversion systems. This article has given the relevant way to solve this problem by converting spoken language into sign languages in real time using machine translation that can shape communication path for deaf and dumb community. In our method, we use Natural Language Processing (NLP) to transcribe audio into text and then deep learning for sign language generation. The system recognizes keywords and important phrases in the transcribed text, associates them with corresponding sign language gestures, and presents such signs animations using an avatar-based visualization. The recent accomplishment is different from previous systems that work directly and only translate a sentence to its corresponding sign, rather focusing on the contextual analysis for better conveying of meaning. Key Words: Audio to Sign Language Translation, Speech- to- Text Conversion, Natural Language Processing (NLP), Machine Translation, Deep Learning, Gesture Recognition, Avatar-based Sign Language Visualization, Accessibility, Multilingual Sign Language

Phatic emoji of Javanese netizens in social media: a cyberpragmatics perspective on a creative communicative strategy

The massive shift of communication culture from direct to virtual channels due to the COVID-19 pandemic affected virtual politeness performance. The limitation of physical expression in digital platforms encourages emojis to replace gestures, mimics, and other expressions. This study aims to describe the types of speech acts and the function of the emojis along with the performance of politeness strategies in the WhatsApp group of Javanese netizens. Understanding politeness performance through the use of speech acts and emojis in virtual communication provides insights into how universal emojis are specifically used in a particular culture and shows the creativity of emoji use in phatic communication. The virtual textual data were taken from five WhatsApp group’s with 174 Javanese members by observation method. The emoji that occurred in the conversational texts were classified based on the types of speech acts and their functions. The analysis was conducted within the cyberpragmatics framework. The findings showed that emojis were used in assertive speech acts, directive speech acts, expressive speech acts, and phatic speech acts. The functions were to emphasize meaning, clarify meaning, direct certain actions, express feelings, and establish social rapport. The research contributes to the development of cyberpragmatics as the current approach in pragmatics study. Moreover, the findings depicted the current phenomenon of social media communication on a cultural basis.

Exploring the Role of AI in UX Research

Presently, artificial intelligence (AI) is primarily utilized in user experience (UX) research for handling routine tasks like text conversion (e.g., transcription, survey preparation) to manage large volumes of data efficiently. However, its potential as a collaborative partner in generating ideas and insights remains largely untapped, possibly due to the limited AI education among UX researchers. There is a recognized need to enhance AI literacy among Human-Computer Interaction (HCI) students to effectively utilize AI tools in UX research for an AI-driven future. Despite its growing importance, the integration of AI literacy into HCI curricula remains insufficient. This study aims to explore AI’s potential in addressing current challenges in UX research and assess UX researchers’ readiness to adopt AI tools. Through in-depth interviews with five early-career UX researchers, it examined their experiences, challenges, and perspectives on integrating AI. Thematic analysis of the interview data uncovers insights, revealing that AI has significant potential to overcome challenges in collecting, interpreting, and synthesizing qualitative data, as well as in translating user needs into design concepts. The findings are intended to inform strategies for incorporating AI literacy into HCI education, aligning with industry demands for AI-enabled expertise in UX design.

Deep emotion recognition in textual conversations: a survey

Emotion Recognition in Conversations (ERC) is a key step towards successful human–machine interaction. While the field has seen tremendous advancement in the last few years, new applications and implementation scenarios present novel challenges and opportunities. These range from leveraging the conversational context, speaker, and emotion dynamics modelling, to interpreting common sense expressions, informal language, and sarcasm, addressing challenges of real-time ERC, recognizing emotion causes, different taxonomies across datasets, multilingual ERC, and interpretability. This survey starts by introducing ERC, elaborating on the challenges and opportunities of this task. It proceeds with a description of the emotion taxonomies and a variety of ERC benchmark datasets employing such taxonomies. This is followed by descriptions comparing the most prominent works in ERC with explanations of the neural architectures employed. Then, it provides advisable ERC practices towards better frameworks, elaborating on methods to deal with subjectivity in annotations and modelling and methods to deal with the typically unbalanced ERC datasets. Finally, it presents systematic review tables comparing several works regarding the methods used and their performance. Benchmarking these works highlights resorting to pre-trained Transformer Language Models to extract utterance representations, using Gated and Graph Neural Networks to model the interactions between these utterances, and leveraging Generative Large Language Models to tackle ERC within a generative framework. This survey emphasizes the advantage of leveraging techniques to address unbalanced data, the exploration of mixed emotions, and the benefits of incorporating annotation subjectivity in the learning phase.

Meshwork Ecology: Climate and Colonialism in Diene's Hell Freezes Over and Ruga's Public Service Announcement

ABSTRACT: Within global academia, activism, and cultural production there has been a turn towards what I conceptualize as meshwork ecology . Here, the relationality and interdependency of all earthbound things are emphasized, as is the multiplicity of human and nonhuman ontologies and epistemologies existing on the planet. This paper demonstrates how reading for meshwork ecological elements can bring to light new features of ecocentric African speculative fiction. After unpacking my understanding of meshwork ecology, I read for certain meshwork ecological elements the novella "Hell Freezes Over" by Mame Bougouma Diene and the short film Public Service Announcement by Athipatra Ruga. I show how reading for meshwork ecological elements illuminates four central aspects of these texts: 1) the interrogation of colonial ways of inhabiting the earth, 2) the centering of interconnectedness and relationality with the nonhuman, 3) thinking with multiple temporalities, and 4) exploring assemblages that involve technology, humanity, and the nonhuman. I expound on how I understand these texts in conversation with meshwork ecological scholarship as examples of planetary resonances, in this case a shared engagement with the looming reality of climate change and the pressing need to rethink how humanity relates to the nonhuman world.

Capturing multiple emotions from conversational data using fine tuned transformers

Emotion detection is one of the crucial topics of Natural Language Processing (NLP) in recent years, and now one of the biggest motivating factors in correct identification and interpretation of a wide range of emotional expressions in textual data. This research examines the use of Bidirectional Encoder Representations from Transformers (BERT), a trained transformer model for emotion detection in textual data. This analysis evaluates how good BERT is at identifying emotions such as surprise, anger, fear, happiness and sadness compared with ordinary machine learning as well as deep learning techniques. The addition of weighted emotions approach enhances the model performance and gives a deeper emotional context awareness making it more effective to deal with complicated emotional utterances. The purpose of the research is to develop a fine-tuned BERT model with a weighted emotion framework to enhance the accuracy of emotion classification in conversational text. The context is improving emotion recognition in scenarios where multiple emotions co-exist, addressing limitations in traditional models by capturing subtle and overlapping emotional expressions. In terms of training the methods multiple datasets are considered and also the research examines various models performance. The article further discusses possible application areas of BERT modified in light of NLP.

Age Group Differences in Social Media Language Use: Identity Representation via Emojis and Abbreviations

This paper aims to analyse how age groups impact the use of social media language (SL) in online textual conversations, focusing on the type, polarity, and subjectivity of SL to explore identity representation across different age groups. During data collection in 2023, there were 4.76 billion social media users worldwide; hence, understanding the use of SL is essential for analysing how individuals express their emotions and identities online. SL consists of emoticons/emojis, abbreviations, and mixed language in textual conversations. For this study, a qualitative approach was used, with 46 participants from three age groups: 25-34, 35-44, and 45-54 engaging in mock group conversations for one hour using WhatsApp. The participants were separated into six group conversations. The findings show that the older age groups (35-44 and 45-54) prefer to discuss technical or professional topics, whereas the younger age groups (25-34) are more interested in games, food, entertainment, and fun. Furthermore, for the age group 25-34 there is a high usage of emoticons/emojis, whereas the age group 45-54 uses them the least. These findings indicate that age groups have a significant influence on the use of SL and topic preferences in textual conversations, providing insights that will be used to develop a Natural Language Processing (NLP) tool for online identity classification. This study enhances human-computer interaction by investigating how different age groups use SL in digital environments.

An Emotional Dialogue System Using Conditional Generative Adversarial Networks with a Sequence-to-Sequence Transformer Encoder

Understanding the expression of emotion and generating appropriate responses are key steps toward constructing emotional, conversational agents. In this paper, we propose a framework for single-turn emotional conversation generation, and there are three main components in our model, namely, a sequence-to-sequence model with stacked encoders, a conditional variational autoencoder, and conditional generative adversarial networks. For the sequence-to-sequence model with stacked encoders, we designed a two-layer encoder by combining Transformer with gated recurrent units-based neural networks. Because of the flexibility of the sequence-to-sequence model, we adopted a conditional variational autoencoder in our framework, which uses latent variables to learn a distribution over potential responses and generates diverse responses. Furthermore, we regard a conditional variational autoencoder-based, sequence-to-sequence model as the generative model, and the training of the generative model is assisted by both a content discriminator and an emotion classifier, which assists our model in promoting content information and emotion expression. We use automated evaluation and human evaluation to evaluate our model and baselines on the NTCIR short text conversation task (STC-3) Chinese emotional conversation generation (CECG) Subtask dataset [44], and the experimental results demonstrate that our proposed framework can generate semantically reasonable and emotionally appropriate responses.

Community-Driven Research with People Who Use Drugs: A Virtual Project During Multiple Epidemics

Sociological approaches to digital and community-engaged research experienced significant innovation in recent years. This article examines developing and implementing a primarily virtual community-driven research (CDR) project with the National Survivors Union, the American national drug-users union, during the COVID-19 pandemic. Relationships between researchers and directly impacted people, such as people who use drugs, face many barriers. These issues were exacerbated during COVID-19 when in-person research decreased while drug-related harms increased. In response, this project modified the CDR model for drug-use research. The CDR model is particularly beneficial for studies with marginalized populations who may mistrust researchers. In CDR, impacted community members are fundamental project drivers. This project’s data are based on 29 months of weekly group meetings in National Survivors Union online spaces, group and individual text conversations, phone calls, and shared-document group work. The project co-developed methods for CDR with directly impacted people, including community-initiated research questions, low-threshold methods, collaborative writing strategies, coauthorship practices foregrounding directly impacted perspectives, and multiple dissemination forms. Modified CDR expands sociological methods for digital research, citizen science, and community-engaged research with vulnerable, criminalized groups. This approach may aid inclusive, innovative sociological scholarship and effective public health policy for reducing morbidity and mortality during multiple crises.

KEMAMPUAN LITERASI BAHASA INDONESIA PELAKU UMKM PRODUK LOKAL BADUI PADA TOKO ONLINE DAN E-COMMERCE TOKOPEDIA

The indigenous Badui community is known for its strict customs; among other things, prohibitions on schools and the use of technology in daily life so it is not surprising that the majority of Badui people cannot write, especially the Badui Dalam indigenous community and the older generation of the Badui Luar community. As time goes by, the Badui Luar indigenous people are starting to openup using technology in the form of cell phones and the internet. The use of cell phones and the internet among the Badui Luar indigenous community is not only for communication, but also for trading. Initially, the use of social media by the Badui indigenous community was Facebook and Instagram for trading. In the last year, some of them have started using e-commerce. The use of social media indirectly becomes a learning medium for the indigenous Badui Luar community to write because like it or not they have to communicate in writing with consumers in the form of chat texts and create product descriptions in the online shops they own. However, due to their limited ability to write and speak Indonesian well, they often experience communication barriers with consumers which ultimately impacts the success of buying and selling transactions. The objectives to be achieved in this research include: (1) knowing the ability of small business owner local Badui to write sentences, (2) knowing the ability of small business owner in understanding sentences or short conversation texts and responding to chats in writing. This research is a development of the research results of Saleh Abas (2006) which states that a person's ability to write is determined by accuracy in using languageelements, organizing discourse in the form of an essay, accuracy in using language, and choosing the words used to write. The method used in this research is descriptive qualitative. The research subjects were local Badui product who already had online shops on Tokopedia e-commerce. The sample obtained by purposive sampling consisted of 10 small business owner who already had an online shop on Tokopedia e-commerce with the status still active. Research data was obtained based on 26 texts describing local Badui products; including woven cloth, koja bags, palm sugar and tutu coffee. This data is to determine sentence structure, diction, punctuation, and creativity in creating sentences. The data analysis technique uses descriptive analysis based on product description text. The results of the research show that 6 out of 10 small business owner who are local Badui products have quite good writing skills, but are not good in terms of grammar, diction, punctuation and creativity in creating sentences. The conclusions from this research show that the Indonesian language literacy skills of small business owner local Badui product are quite good even though they have not received formal education.

Implementation of Natural Language Processing for Bullying Complaints with Voice to Text Conversion

Bullying in high school is frequent and negatively affects students' psychological well-being. The lack of effective reporting mechanisms makes students hesitant to report bullying cases for fear of their identity being exposed, which can lead to stigma or retaliation. The lack of data on bullying incidents also hampers prevention and intervention measures. The study designed and implemented a mobile application that uses natural language processing (NLP) for speech-to-text conversion, enabling anonymous and convenient reporting of bullying cases. The app ensures the anonymity of whistleblowers and facilitates the collection of accurate data on bullying incidents, helping schools respond with appropriate preventive measures. Software engineering methodologies are used with a focus on requirements analysis, system design, implementation, and application testing. NLP technology is used to interpret verbal instructions into text, with the Support Vector Machine (SVM) method for text classification, ensuring high accuracy in detecting bullying incidents. The trial application in several high schools showed the relevance and effectiveness of the application. Ethical and security considerations are top priorities, with an emphasis on whistleblower identity protection and data security. The test results showed that the application achieved 92% accuracy, 90% precision, and 88% recall, demonstrating its effectiveness in collecting bullying reports anonymously and accurately.

A SeNA-based study of in-service teachers’ interaction patterns in an online learning community

In-service teachers, constrained by work commitments, adopt online learning methods for their graduate studies, emphasizing the significance of exploring the construction of online learning communities. Analyzing collaborative conversation texts within the online learning process of the “Learning Science” course at a university in Beijing, this study employed social epistemic network analysis (SeNA) to investigate interaction patterns of in-service teachers and propose optimization strategies. The findings of this study show that in asynchronous discussions without intervention, the overall situation of collaborative conversations among in-service teachers tends to be low-level. The social interaction network shows polycentricity, categorizing members into core and peripheral interactors based on distinct social interaction characteristics, highlighting substantial disparities in epistemic networks. Core interactors prioritize engaging with others, yet their contributions primarily echo interactions, reflecting superficial knowledge dimensions. Conversely, specific peripheral interactors prioritize independent reflection, sharing content largely derived from personal experiences or knowledge, signifying a commitment to independent and in-depth thinking within deeper knowledge dimensions. Moreover, learners’ online interactions are influenced by individual characteristics, instructional evaluation and the learning community atmosphere.

Raspberry-Pi Based Physical Media to Audio Conversion device for Visually Impaired Individuals

The proposed product is a device for real-time scanning and conversion of text from physical media to audio for the aid of visually impaired individuals. The focus of the project is to make a device which brings the experience of visually impaired individuals as close to that of the ordinarily abled/educated as possible when it comes to access to resources, books, and physical reading material. This device is targeted towards libraries, reading rooms, and schools for visually impaired individuals. The prototype is developed using a FDM 3D printer with PLA material and using a laser cutting machine with MDF material to allow for maximum customisability to meet the end-user’s needs. The proposed device is equipped with a Raspberry Pi 4B+, a camera, two pushbuttons, two potentiometers and a head-phone. A variety of image processing techniques, bundled with open-source optical character recognition (OCR) software and text-to-speech libraries, are used to capture and process images of book pages and convert them to audio files, all while maintaining a physical user interface which can be navigated autonomously by the visually challenged. The product is capable of handling over 200 fonts from 8pt to 36pt size. The product is successfully tested on 15 users for approximately 4000 words.

Lettrism as Implicit Philosophy in 10th-century Jewish and Muslim Creation Theories

Abstract This article presents a comparative reading of two 10th-century Islamicate lettrist works: Kitāb Khawāṣṣ al-ḥurūf by Ibn Masarra and a commentary on the Sefer Yetsirah by Saʿadia Gaon. Close analysis of both treatises emphasizes that philosophy coexists with other ways of knowing in lettrist theories. While some lettrist authors overtly employ a philosophical structure, this paper argues that others reference and re-interpret philosophical concepts (such as primordial elements and prime matter) implicitly within their work. Through reading these texts in conversation, I suggest that a framework of “implicit philosophy” creates space for lettrism within the domain of philosophy and science, while also acknowledging the intertwining aspects of mysticism, esoterism, and occultism that weave both within and beyond the realm of philosophy. Thinking in terms of implicit philosophy opens up new ways of understanding and contextualizing lettrist works, both as individual texts and as constituents of a broader Islamicate lettrist milieu.

Grammatical structures of emoji in Japanese-language text conversations

Emojis have become a ubiquitous part of everyday text communication worldwide. Cohn et al. (Cognit Res Princ Implic 4(1):1–18, 2019) studied the grammatical structure of emoji usage among English speakers and found a correlation between the sequence of emojis used and English word order, tending towards an subject–verb–object (SVO) sequence. However, it remains unclear whether emoji usage follows a universal grammar or whether it is influenced by native language grammar. Therefore, this study explored the potential influence of Japanese grammar on emoji usage by Japanese speakers. Twenty adults, all native Japanese speakers, participated in pairs. In Experiment 1, participants engaged in conversations through Google Hangouts on iPads. The experiment consisted of four conversation rounds of approximately 8 min each. The first two rounds involved one participant using only written Japanese and the other using only emojis and punctuation, with roles reversed in the second round. The third round required both participants to use only emojis and punctuation. The results indicated that participants preferred subject–object–verb (SOV) or object–verb (OV) sequences, with OV patterns being more common. This pattern reflects a distinctive attribute of Japanese grammatical structure, marked by the frequent omission of the subject. Experiment 2 substituted emojis for words, showing nouns were more commonly replaced than verbs due to the difficulty in conveying complex meanings. Reduced subject replacements again emphasised Japanese grammatical structure. In essence, emoji usage reflects native language structures, but complexities are challenging to convey, resulting in simplified sequences. This study offers insights for enhancing emoji-based communication and interface design, with implications for translation and broader communication.

Sign language video to text conversion via optimised LSTM with improved motion estimation

ABSTRACT A method of communication for the deaf and the dumb is sign language (SL). Knowing SL allows speakers and listeners to converse effectively with deaf and dumb people. Yet, while they can acquire sign language (SL) to communicate with the dumb and the deaf, untrained persons are unable to speak with them. For such individuals, an SL to text system is more beneficial in enabling them to communicate more effectively with everyday people. To interact with the deaf and dumb, SL is a tactile gesture that employs the hands and eyes. This proposes a new model for translating SL video to text conversion or generation. Frame Conversion is the initial step, where the given input video is converted into frames. Subsequently, features considered are hierarchy of skeleton and Improved Motion estimation (ME). For generating the text, this work uses an optimised LSTM model, where training of weights is done by hybrid Combined Pelican and BES algorithm (CP-BES). Moreover, the mean value for dataset ISL-CSLTR has achieved the proposed method is 27.3%, 31.6%, 28.7%, 33.4%, 29.8%, 24.5%, 36.2%, and 34.8% greater than the conventional schemes like CNN, FSU-CNN, ASL – CNN, RNN, LSTM +POA, LSTM +ARCHOA, LSTM +HGS, LSTM +PRO and LSTM +BES.