Domain-Specific Keyword Extraction Using Joint Modeling of Local and Global Contextual Semantics

Domain-specific keyword extraction is a vital task in the field of text mining. There are various research tasks, such as spam e-mail classification, abusive language detection, sentiment analysis, and emotion mining, where a set of domain-specific keywords (aka lexicon) is highly effective. Existing works for keyword extraction list all keywords rather than domain-specific keywords from a document corpus. Moreover, most of the existing approaches perform well on formal document corpuses but fail on noisy and informal user-generated content in online social media. In this article, we present a hybrid approach by jointly modeling the local and global contextual semantics of words, utilizing the strength of distributional word representation and contrasting-domain corpus for domain-specific keyword extraction. Starting with a seed set of a few domain-specific keywords, we model the text corpus as a weighted word-graph. In this graph, the initial weight of a node (word) represents its semantic association with the target domain calculated as a linear combination of three semantic association metrics, and the weight of an edge connecting a pair of nodes represents the co-occurrence count of the respective words. Thereafter, a modified PageRank method is applied to the word-graph to identify the most relevant words for expanding the initial set of domain-specific keywords. We evaluate our method over both formal and informal text corpuses (comprising six datasets), and show that it performs significantly better in comparison to state-of-the-art methods. Furthermore, we generalize our approach to handle the language-agnostic case, and show that it outperforms existing language-agnostic approaches.

What Happened in ‘The HERizon Project’?—Process Evaluation of a Multi-Arm Remote Physical Activity Intervention for Adolescent Girls

This mixed-methods process evaluation examines the reach, recruitment, fidelity, adherence, acceptability, mechanisms of impact, and context of remote 12-week physical activity (PA) interventions for adolescent girls named The HERizon Project. The study was comprised of four arms—a PA programme group, a behaviour change support group, a combined group, and a comparison group. Data sources included intervention deliverer and participant logbooks (100 and 71% respective response rates, respectively), exit surveys (72% response rate), and semi-structured focus groups/interviews conducted with a random subsample of participants from each of the intervention arms (n = 34). All intervention deliverers received standardised training and successfully completed pre-intervention competency tasks. Based on self-report logs, 99% of mentors adhered to the call guide, and 100% of calls and live workouts were offered. Participant adherence and intervention receipt were also high for all intervention arms. Participants were generally satisfied with the intervention components; however, improvements were recommended for the online social media community within the PA programme and combined intervention arms. Autonomy, sense of accomplishment, accountability, and routine were identified as factors facilitating participant willingness to adhere to the intervention across all intervention arms. Future remote interventions should consider structured group facilitation to encourage a genuine sense of community among participants.

A Deep Neural Network Model for the Detection and Classification of Emotions from Textual Content

Emotion-based sentimental analysis has recently received a lot of interest, with an emphasis on automated identification of user behavior, such as emotional expressions, based on online social media texts. However, the majority of the prior attempts are based on traditional procedures that are insufficient to provide promising outcomes. In this study, we categorize emotional sentiments by recognizing them in the text. For that purpose, we present a deep learning model, bidirectional long-term short-term memory (BiLSMT), for emotion recognition that takes into account five main emotions (Joy, Sadness, Fear, Shame, Guilt). We use our experimental assessments on the emotion dataset to accomplish the emotion categorization job. The datasets were evaluated and the findings revealed that, when compared to state-of-the-art methodologies, the proposed model can successfully categorize user emotions into several classifications. Finally, we assess the efficacy of our strategy using statistical analysis. This research’s findings help firms to apply best practices in the selection, management, and optimization of policies, services, and product information.

The drip footwear brand: managing a successful start-up launch and rapid expansion during Covid-19 pandemic

Learning outcomes Students will be able to: describe the entrepreneurial traits required for successful business venturing; evaluate the entrepreneurial risks associated with a rapid business expansion in the early start-up phase of an entrepreneurial venture, especially in crisis; select and defend appropriate management systems that will contribute to the sustainability of a business post the crisis and rapid expansion; and evaluate the online social media optimisation strategies. Case overview/synopsis In July 2019, Lekau Sehoana launched branded sneakers called Drip. It took Lekau six weeks to sell the first 600 pairs of shoes from his car boot, not having applied any robust marketing strategies. During the interactions with customers, it became clear that there was a demand for a new South African sneakers brand. In December of the same year, he manufactured and within a few days, sold 1,200 sneakers. This rapid achievement was enough confirmation for Lekau that there was a need for locally manufactured and branded shoes. Based on this success, Lekau started to consider the launch of his own business. However, during the process of the formal launch, the world was suddenly experiencing the impact of the Covid-19 pandemic. During the planning stage regarding the mode of operation and the full business launch, in March 2020, South Africa was placed into the Covid-19 Alert Level 5 lockdown, complicating the decision-making process even further. Despite the extremely severe lockdown regulations that lasted more than a year, in May 2021, Lekau had already managed to open 11 stores in reputable malls and sold hundred thousands of his sneakers. This instant success, putting pressure on the manufacturing ability, distribution and costing structure, led to Lekau becoming concerned about having grown and still growing too fast too soon during a pandemic. His concern was what would happen when the country would move back to normal, without the constraints caused by the lockdown, would he be able to sustain the growth and how would he achieve this, and how would he be able to manage the fast-growing venture? Complexity academic level Entrepreneurship, Innovation, General Management and Marketing courses at the Postgraduate Diploma and Masters level. Supplementary materials Teaching notes are available for educators only. Subject code CCS 3: Entrepreneurship.

Pilot study suggests online media literacy programming reduces belief in false news in Indonesia

Amidst the threat of digital misinformation, we offer a pilot study regarding the efficacy of an online social media literacy campaign aimed at empowering individuals in Indonesia with skills to help them identify misinformation. We found that users who engaged with our online training materials and educational videos were more likely to identify misinformation than those in our control group (total N=1,000). Given the promising results of our preliminary study, we plan to expand efforts in this area, and build upon lessons learned from this pilot study.

Exploration of Spa Leisure Consumption Sentiment towards Different Holidays and Different Cities through Online Reviews: Implications for Customer Segmentation

With the improvements in per capita disposable income, and an increase in work-related pressure, demand for leisure consumption such as foot bath spas is constantly increasing. Analysis of leisure consumption sentiment is of great importance for the leisure service industry—to meet customer needs, improve service quality and improve customer relationship management. However, traditional sentiment analysis approaches only aimed to ascertain the overall sentiment of the customer, which is less effective for analyzing customer satisfaction on account of customer size, different customer locations, and different leisure holidays. Sentiment analysis via online reviews can assist different businesses, including foot bath spa services, to better inform the development of customer segmentation strategies and ensure optimal customer relationship management. Hence, the objective of this paper is to explore foot bath spa leisure consumption sentiment towards different holidays and different cities by applying data mining via online reviews, so as to help optimize customer segmentation. A novel general framework and related sentiment analysis methods were proposed and then conducted through a collection of datasets from customers’ textual reviews of foot bath spa merchants in three cities in China on the Meituan social media platform. Findings confirm that the proposed general framework and methods can be used to gain insights into the swing characteristics of sentiment towards different holidays and different cities, to better develop customer segmentation according to the city-holiday emoticon face patterns obtained through sentiment tendency analysis from online social media review data. The study results can help to develop better customer and marketing strategies, thereby creating sustainable competitive advantages, and can be extended to other fields to support sustainable development.