Emoji-powered Sentiment and Emotion Detection from Software Developers’ Communication Data

2021 ◽  
Vol 30 (2) ◽  
pp. 1-48
Author(s):  
Zhenpeng Chen ◽  
Yanbin Cao ◽  
Huihan Yao ◽  
Xuan Lu ◽  
Xin Peng ◽  
...  
Keyword(s):  
Large Scale ◽  
Online Communication ◽  
Future Research ◽  
Technical Knowledge ◽  
Affective States ◽  
Emotion Detection ◽  
Open Domain ◽  
Software Developers ◽  
Domain Specific ◽  
Benchmark Datasets

Sentiment and emotion detection from textual communication records of developers have various application scenarios in software engineering (SE). However, commonly used off-the-shelf sentiment/emotion detection tools cannot obtain reliable results in SE tasks and misunderstanding of technical knowledge is demonstrated to be the main reason. Then researchers start to create labeled SE-related datasets manually and customize SE-specific methods. However, the scarce labeled data can cover only very limited lexicon and expressions. In this article, we employ emojis as an instrument to address this problem. Different from manual labels that are provided by annotators, emojis are self-reported labels provided by the authors themselves to intentionally convey affective states and thus are suitable indications of sentiment and emotion in texts. Since emojis have been widely adopted in online communication, a large amount of emoji-labeled texts can be easily accessed to help tackle the scarcity of the manually labeled data. Specifically, we leverage Tweets and GitHub posts containing emojis to learn representations of SE-related texts through emoji prediction. By predicting emojis containing in each text, texts that tend to surround the same emoji are represented with similar vectors, which transfers the sentiment knowledge contained in emoji usage to the representations of texts. Then we leverage the sentiment-aware representations as well as manually labeled data to learn the final sentiment/emotion classifier via transfer learning. Compared to existing approaches, our approach can achieve significant improvement on representative benchmark datasets, with an average increase of 0.036 and 0.049 in macro-F1 in sentiment and emotion detection, respectively. Further investigations reveal that the large-scale Tweets make a key contribution to the power of our approach. This finding informs future research not to unilaterally pursue the domain-specific resource but try to transform knowledge from the open domain through ubiquitous signals such as emojis. Finally, we present the open challenges of sentiment and emotion detection in SE through a qualitative analysis of texts misclassified by our approach.

A Comprehensive Taxonomy of Dynamic Texture Representation

2023 ◽  
Vol 55 (1) ◽  
pp. 1-39
Author(s):  
Thanh Tuan Nguyen ◽  
Thanh Phuong Nguyen
Keyword(s):  
Large Scale ◽  
Environmental Changes ◽  
State Of The Art ◽  
The State ◽  
Future Research ◽  
Research Activities ◽  
Potential Applications ◽  
Benchmark Datasets ◽  
Negative Impacts ◽  
Made In

Representing dynamic textures (DTs) plays an important role in many real implementations in the computer vision community. Due to the turbulent and non-directional motions of DTs along with the negative impacts of different factors (e.g., environmental changes, noise, illumination, etc.), efficiently analyzing DTs has raised considerable challenges for the state-of-the-art approaches. For 20 years, many different techniques have been introduced to handle the above well-known issues for enhancing the performance. Those methods have shown valuable contributions, but the problems have been incompletely dealt with, particularly recognizing DTs on large-scale datasets. In this article, we present a comprehensive taxonomy of DT representation in order to purposefully give a thorough overview of the existing methods along with overall evaluations of their obtained performances. Accordingly, we arrange the methods into six canonical categories. Each of them is then taken in a brief presentation of its principal methodology stream and various related variants. The effectiveness levels of the state-of-the-art methods are then investigated and thoroughly discussed with respect to quantitative and qualitative evaluations in classifying DTs on benchmark datasets. Finally, we point out several potential applications and the remaining challenges that should be addressed in further directions. In comparison with two existing shallow DT surveys (i.e., the first one is out of date as it was made in 2005, while the newer one (published in 2016) is an inadequate overview), we believe that our proposed comprehensive taxonomy not only provides a better view of DT representation for the target readers but also stimulates future research activities.

scholarly journals A Survey on Machine Reading Comprehension—Tasks, Evaluation Metrics and Benchmark Datasets

2020 ◽  
Vol 10 (21) ◽  
pp. 7640
Author(s):  
Changchang Zeng ◽  
Shaobo Li ◽  
Qin Li ◽  
Jie Hu ◽  
Jianjun Hu
Keyword(s):  
Reading Comprehension ◽  
Language Processing ◽  
Large Scale ◽  
Human Performance ◽  
Research Field ◽  
Evaluation Metrics ◽  
Future Research ◽  
Benchmark Datasets ◽  
Comprehensive Survey ◽  
Machine Reading

Machine Reading Comprehension (MRC) is a challenging Natural Language Processing (NLP) research field with wide real-world applications. The great progress of this field in recent years is mainly due to the emergence of large-scale datasets and deep learning. At present, a lot of MRC models have already surpassed human performance on various benchmark datasets despite the obvious giant gap between existing MRC models and genuine human-level reading comprehension. This shows the need for improving existing datasets, evaluation metrics, and models to move current MRC models toward “real” understanding. To address the current lack of comprehensive survey of existing MRC tasks, evaluation metrics, and datasets, herein, (1) we analyze 57 MRC tasks and datasets and propose a more precise classification method of MRC tasks with 4 different attributes; (2) we summarized 9 evaluation metrics of MRC tasks, 7 attributes and 10 characteristics of MRC datasets; (3) We also discuss key open issues in MRC research and highlighted future research directions. In addition, we have collected, organized, and published our data on the companion website where MRC researchers could directly access each MRC dataset, papers, baseline projects, and the leaderboard.

scholarly journals Adoption Barriers of IoT in Large Scale Pilots

Information ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 23
Author(s):  
Ali Padyab ◽  
Abdolrasoul Habibipour ◽  
Aya Rizk ◽  
Anna Ståhlbröst
Keyword(s):  
Diffusion Of Innovation ◽  
Large Scale ◽  
Smart Cities ◽  
Autonomous Driving ◽  
Relative Advantage ◽  
Future Research ◽  
Privacy And Security ◽  
Domain Specific ◽  
Smart Agriculture ◽  
Practical Recommendations

The pervasive connectivity of devices enabled by Internet of Things (IoT) technologies is leading the way in various innovative services and applications. This increasing connectivity comes with its own complexity. Thus, large scale pilots (LSPs) are designed to develop, test and use IoT innovations in various domains in conditions very similar to their operational scalable setting. One of the key challenges facing the diffusion of such innovations within the course of an LSP is understanding the conditions in which their respective users decide to adopt them (or not). Accordingly, in this study we explore IoT adoption barriers in four LSPs in Europe from the following domains: smart cities, autonomous driving, wearables and smart agriculture and farming. By applying Roger’s Diffusion of Innovation as a theoretical lens and using empirical data from workshops and expert interviews, we identify a set of common and domain specific adoption barriers. Our results reveal that trust, cost, perceived value, privacy and security are common concerns, yet shape differently across domains. In order to overcome various barriers, the relative advantage or value of using the innovation needs to be clearly communicated and related to the users’ situational use; while this value can be economic in some domains, it is more hedonic in others. LSPs were particularly challenged in applying established strategies to overcome some of those barriers (e.g., co-creation with end-users) due to the immaturity of the technology as well as the scale of pilots. Accordingly, we reflect on the theoretical choice in the discussion as well as the implications of this study on research and practice. We conclude with providing practical recommendations to LSPs and avenues for future research.

scholarly journals WikiAsp: A Dataset for Multi-domain Aspect-based Summarization

2021 ◽  
Vol 9 ◽  
pp. 211-225
Author(s):  
Hiroaki Hayashi ◽  
Prashant Budania ◽  
Peng Wang ◽  
Chris Ackerson ◽  
Raj Neervannan ◽  
...  
Keyword(s):  
Large Scale ◽  
Open Domain ◽  
Domain Specific ◽  
Product Features ◽  
Points Of Interest ◽  
Large Scale Dataset

Abstract Aspect-based summarization is the task of generating focused summaries based on specific points of interest. Such summaries aid efficient analysis of text, such as quickly understanding reviews or opinions from different angles. However, due to large differences in the type of aspects for different domains (e.g., sentiment, product features), the development of previous models has tended to be domain-specific. In this paper, we propose WikiAsp,1 a large-scale dataset for multi-domain aspect- based summarization that attempts to spur research in the direction of open-domain aspect-based summarization. Specifically, we build the dataset using Wikipedia articles from 20 different domains, using the section titles and boundaries of each article as a proxy for aspect annotation. We propose several straightforward baseline models for this task and conduct experiments on the dataset. Results highlight key challenges that existing summarization models face in this setting, such as proper pronoun handling of quoted sources and consistent explanation of time-sensitive events.

scholarly journals Implementasi Teori Penetrasi Sosial pada Pengguna Aplikasi Tinder

2021 ◽  
Vol 5 (2) ◽  
pp. 24-38
Author(s):  
Alvin Gus Abdurrahman ◽  
Chairun Nisa Dwi Putri ◽  
Irwansyah Irwansyah
Keyword(s):  
Interpersonal Relationships ◽  
Large Scale ◽  
Personal Information ◽  
Online Communication ◽  
General Information ◽  
Relationship Development ◽  
Future Research ◽  
Self Disclosure ◽  
Early Stages ◽  
The Impact

The Covid-19 pandemic has changed social behaviour in society. Especially in the process of building relationships with opposite sex, which is usually done face-to-face, is now turning online. This is the impact of the government's policy to limit large-scale social activities to prevent the development of the coronavirus. In 2020, the number of chat exchanges on the Tinder app increased by 23%. Nowadays people prefer to get to know others online before deciding to meet in person. Social Penetration Theory (TPS) can be used to analyse the process of communication and self-disclosure of an individual in building interpersonal relationships. This theory describes a person like an onion that has various layers in it because someone only provides general information in the early stages of a relationship then reveals more personal information when they get close and build trust. This research is different from previous work because it analyses TPS on Tinder application users during the Covid-19 pandemic. This study aims to review TPS on Tinder application users by testing the main assumptions of the theory in relationship development and self-disclosure. This study uses a qualitative approach. Data collection is done by searching for articles journals with keywords TPS and the use of Tinder application to build relationships. The results show that the self-disclosure mediated by digital technology can limit the general and personal information shared by its users. The limitation of it lies in the ability to read non-verbal messages. Gender factors influence how individuals build relationships in the early stages. Men tend to approach women aggressively using jokes or funny statements, while women rely more on subtle cues. Future research can focus on the communication of Tinder application users at different age ranges. This research is expected to be input for research on online communication, self-disclosure and relationship development.

Change of Attitude, Technology and Practice: Identifying the Change for Increased Value Creation with Customer Co-creation

2017 ◽  
Vol 5 (1) ◽  
pp. 70-82
Author(s):  
Soumi Paul ◽  
Paola Peretti ◽  
Saroj Kumar Datta
Keyword(s):  
Social Media ◽  
New Product Development ◽  
Value Creation ◽  
Large Scale ◽  
Customer Orientation ◽  
Customer Relationships ◽  
Future Research ◽  
Management Competencies ◽  
Business Decisions ◽  
Prime Concern

Building customer relationships and customer equity is the prime concern in today’s business decisions. The emergence of internet, especially social media like Facebook and Twitter, changed traditional marketing thought to a great extent. The importance of customer orientation is reflected in the axiom, “The customer is the king”. A good number of organizations are engaging customers in their new product development activities via social media platforms. Co-creation, a new perspective in which customers are active co-creators of the products they buy and use, is currently challenging the traditional paradigm. The concept of co-creation involving the customer’s knowledge, creativity and judgment to generate value is considered not only an upcoming trend that introduces new products or services but also fitting their need and increasing value for money. Knowledge and innovation are inseparable. Knowledge management competencies and capacities are essential to any organization that aspires to be distinguished and innovative. The present work is an attempt to identify the change in value creation procedure along with one area of business, where co-creation can return significant dividends. It is on extending the brand or brand category through brand extension or line extension. This article, through an in depth literature review analysis, identifies the changes in every perspective of this paradigm shift and it presents a conceptual model of company-customer-brand-based co-creation activity via social media. The main objective is offering an agenda for future research of this emerging trend and ensuring the way to move from theory to practice. The paper acts as a proposal; it allows the organization to go for this change in a large scale and obtain early feedback on the idea presented. 

Using the Change4Life Disney Branded 10-minute Shake Ups During the COVID-19 ‘Lockdown’

2020 ◽  
Author(s):  
Emily Budzynski-Seymour ◽  
James Steele ◽  
Michelle Jones
Keyword(s):  
Perceived Exertion ◽  
Affective Responses ◽  
Online Resources ◽  
Future Research ◽  
Affective States ◽  
Perceived Effort ◽  
Qualitative Feedback ◽  
Bidirectional Relationship ◽  
The Uk

Physical activity (PA) is considered essential to overall health yet it is consistently reported that children are failing to meet the recommended levels. Due to the bidirectional relationship between affective states and PA, affective responses are a potential predictor to long term engagement. Since late March 2020 the UK government enforced ‘lockdown’ measures to help control the spread of Coronavirus (COVID-19); however, this has impacted children’s PA. Using online resources at home to support PA is now common. The primary aim of this research was to investigate the use of the Change4Life 10-minute Shake Ups to support PA by examining the effects of Disney branding upon children’s (n=32) post activity affective responses and perceived exertion. The secondary was to investigate the effect of the lockdown on PA habits. Children had similar positive affective responses and perceived effort to activities; however, branding was considered to be a key contributing factor based upon qualitative feedback from parents. Children’s PA levels dropped slightly since ‘lockdown’ was imposed; though online resources have been utilised to support PA. The use of immersive elements such as characters and narrative in PA sessions, as well as utilising online resources during ‘lockdown’ appear potentially promising for future research.

Overview of Subsynchronous Oscillation in Grid-connected Wind Farm

2020 ◽  
Vol 13 (7) ◽  
pp. 969-979
Author(s):  
Xu Pei-Zhen ◽  
Lu Yong-Geng ◽  
Cao Xi-Min
Keyword(s):  
Power Systems ◽  
Wind Turbine ◽  
Large Scale ◽  
Wind Farm ◽  
Wind Farms ◽  
Induction Generator ◽  
Future Research ◽  
Stable Operation ◽  
Subsynchronous Oscillation ◽  
Different Types

Background: Over the past few years, the subsynchronous oscillation (SSO) caused by the grid-connected wind farm had a bad influence on the stable operation of the system and has now become a bottleneck factor restricting the efficient utilization of wind power. How to mitigate and suppress the phenomenon of SSO of wind farms has become the focus of power system research. Methods: This paper first analyzes the SSO of different types of wind turbines, including squirrelcage induction generator based wind turbine (SCIG-WT), permanent magnet synchronous generator- based wind turbine (PMSG-WT), and doubly-fed induction generator based wind turbine (DFIG-WT). Then, the mechanisms of different types of SSO are proposed with the aim to better understand SSO in large-scale wind integrated power systems, and the main analytical methods suitable for studying the SSO of wind farms are summarized. Results: On the basis of results, using additional damping control suppression methods to solve SSO caused by the flexible power transmission devices and the wind turbine converter is recommended. Conclusion: The current development direction of the SSO of large-scale wind farm grid-connected systems is summarized and the current challenges and recommendations for future research and development are discussed.

scholarly journals Mapping domain characteristics influencing Analytics initiatives: The example of Supply Chain Analytics

2020 ◽  
Vol 13 (1) ◽  
pp. 56
Author(s):  
Tino Herden
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Success Factors ◽  
Research Collaboration ◽  
Future Research ◽  
Structured Interviews ◽  
Chain Management ◽  
Domain Specific ◽  
The Status ◽  
The Impact

Purpose: Analytics research is increasingly divided by the domains Analytics is applied to. Literature offers little understanding whether aspects such as success factors, barriers and management of Analytics must be investigated domain-specific, while the execution of Analytics initiatives is similar across domains and similar issues occur. This article investigates characteristics of the execution of Analytics initiatives that are distinct in domains and can guide future research collaboration and focus. The research was conducted on the example of Logistics and Supply Chain Management and the respective domain-specific Analytics subfield of Supply Chain Analytics. The field of Logistics and Supply Chain Management has been recognized as early adopter of Analytics but has retracted to a midfield position comparing different domains.Design/methodology/approach: This research uses Grounded Theory based on 12 semi-structured Interviews creating a map of domain characteristics based of the paradigm scheme of Strauss and Corbin.Findings: A total of 34 characteristics of Analytics initiatives that distinguish domains in the execution of initiatives were identified, which are mapped and explained. As a blueprint for further research, the domain-specifics of Logistics and Supply Chain Management are presented and discussed.Originality/value: The results of this research stimulates cross domain research on Analytics issues and prompt research on the identified characteristics with broader understanding of the impact on Analytics initiatives. The also describe the status-quo of Analytics. Further, results help managers control the environment of initiatives and design more successful initiatives.

scholarly journals ‘CTRL’: an online, Dynamic Consent and participant engagement platform working towards solving the complexities of consent in genomic research

2021 ◽  
Author(s):  
Matilda A. Haas ◽  
Harriet Teare ◽  
Megan Prictor ◽  
Gabi Ceregra ◽  
Miranda E. Vidgen ◽  
...  
Keyword(s):  
Large Scale ◽  
Genomic Medicine ◽  
International Standards ◽  
Security And Privacy ◽  
Genomic Research ◽  
Return Of Results ◽  
Future Research ◽  
Design And Development ◽  
Participant Engagement ◽  
Dynamic Consent

AbstractThe complexities of the informed consent process for participating in research in genomic medicine are well-documented. Inspired by the potential for Dynamic Consent to increase participant choice and autonomy in decision-making, as well as the opportunities for ongoing participant engagement it affords, we wanted to trial Dynamic Consent and to do so developed our own web-based application (web app) called CTRL (control). This paper documents the design and development of CTRL, for use in the Australian Genomics study: a health services research project building evidence to inform the integration of genomic medicine into mainstream healthcare. Australian Genomics brought together a multi-disciplinary team to develop CTRL. The design and development process considered user experience; security and privacy; the application of international standards in data sharing; IT, operational and ethical issues. The CTRL tool is now being offered to participants in the study, who can use CTRL to keep personal and contact details up to date; make consent choices (including indicate preferences for return of results and future research use of biological samples, genomic and health data); follow their progress through the study; complete surveys, contact the researchers and access study news and information. While there are remaining challenges to implementing Dynamic Consent in genomic research, this study demonstrates the feasibility of building such a tool, and its ongoing use will provide evidence about the value of Dynamic Consent in large-scale genomic research programs.

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