Search results

This study aims to construct a sentiment series generation method for danmu comments based on deep learning, and explore the features of sentiment series after clustering.

This study consisted of two main parts: danmu comment sentiment series generation and clustering. In the first part, the authors proposed a sentiment classification model based on BERT fine-tuning to quantify danmu comment sentiment polarity. To smooth the sentiment series, they used methods, such as comprehensive weights. In the second part, the shaped-based distance (SBD)-K-shape method was used to cluster the actual collected data.

The filtered sentiment series or curves of the microfilms on the Bilibili website could be divided into four major categories. There is an apparently stable time interval for the first three types of sentiment curves, while the fourth type of sentiment curve shows a clear trend of fluctuation in general. In addition, it was found that “disputed points” or “highlights” are likely to appear at the beginning and the climax of films, resulting in significant changes in the sentiment curves. The clustering results show a significant difference in user participation, with the second type prevailing over others.

Their sentiment classification model based on BERT fine-tuning outperformed the traditional sentiment lexicon method, which provides a reference for using deep learning as well as transfer learning for danmu comment sentiment analysis. The BERT fine-tuning–SBD-K-shape algorithm can weaken the effect of non-regular noise and temporal phase shift of danmu text.

Misinformation surrounding the Sustainable Development Goals (SDGs) has contributed to the formation of misbeliefs among the public. The purpose of this paper is to investigate public sentiment and misbeliefs about the SDGs on the YouTube platform.

The authors extracted 8,016 comments from YouTube videos associated with SDGs. The authors used a pre-trained Python library NRC lexicon for sentiment and emotion analysis, and to extract latent topics, the authors used BERTopic for topic modeling.

The authors found eight emotions, with negativity outweighing positivity, in the comment section. In addition, the authors identified the top 20 topics discussing various SDGs and SDG-related misbeliefs.

The authors reported topics related to public misbeliefs about SDGs and associated keywords. These keywords can be used to formulate social media content moderation strategies to screen out content that creates these misbeliefs. The result of hierarchical clustering can be used to devise and optimize response strategies by governments and policymakers to counter public misbeliefs.

This study represents an initial endeavor to gain a deeper understanding of the public’s misbeliefs regarding SDGs. The authors identified novel misbeliefs about SDGs that previous literature has not studied. Furthermore, the authors introduce an algorithm BERTopic for topic modeling that leverages transformer architecture for context-aware topic modeling.

Sarcasm is a linguistic expression that usually carries the opposite meaning of what is being said by words, thus making it difficult for machines to discover the actual meaning. It is mainly distinguished by the inflection with which it is spoken, with an undercurrent of irony, and is largely dependent on context, which makes it a difficult task for computational analysis. Moreover, sarcasm expresses negative sentiments using positive words, allowing it to easily confuse sentiment analysis models. This paper aims to demonstrate the task of sarcasm detection using the approach of machine and deep learning.

For the purpose of sarcasm detection, machine and deep learning models were used on a data set consisting of 1.3 million social media comments, including both sarcastic and non-sarcastic comments. The data set was pre-processed using natural language processing methods, and additional features were extracted and analysed. Several machine learning models, including logistic regression, ridge regression, linear support vector and support vector machines, along with two deep learning models based on bidirectional long short-term memory and one bidirectional encoder representations from transformers (BERT)-based model, were implemented, evaluated and compared.

The performance of machine and deep learning models was compared in the task of sarcasm detection, and possible ways of improvement were discussed. Deep learning models showed more promise, performance-wise, for this type of task. Specifically, a state-of-the-art model in natural language processing, namely, BERT-based model, outperformed other machine and deep learning models.

This study compared the performance of the various machine and deep learning models in the task of sarcasm detection using the data set of 1.3 million comments from social media.

This paper aims to examine sites of dissonance or consensus between global investor responses to the draft standards, International Financial Reporting Standards S1 (IFRS) (General Requirements for Disclosure of Sustainability-related Financial Information) and IFRS S2 (Climate-related Disclosures), issued by the International Sustainability Standards Board (ISSB).

A thematic content analysis was used to capture investor views expressed in their comment letters submitted in the consultation period (March to July 2022) in comparison to the ex ante position (issue of draft standards, March 2022) and ex post summary feedback (ISSB staff papers, September 2022) of the ISSB.

There was investor consensus in support of the ISSB and the development of the draft standards. However, there were sites of dissonance between investors and the ISSB, notably regarding the basis and focus of reporting (double or single/financial materiality and enterprise value); definitional clarity; emissions reporting; and assurance. Incrementally, the research further highlights that investors display heterogeneity of opinion.

The ISSB standards will provide a framework for future sustainability reporting. This research highlights the significance of such reporting to investors through their responses to the draft standards. The findings reveal sites of dissonance in the development and alignment of draft standards to user needs. The views of investors, as primary users, should help inform the development of sustainability-related standards by a global standard-setting body apposite to current policy and future reporting requirements, and their usefulness to users in practice.

To the best of the authors’ knowledge, this paper makes an original contribution to the comment letter literature, hitherto focused on financial reporting with a relative lack of investor engagement. Using thematic analysis, sites of dissonance are examined between the views of investors and the ISSB on their development of sustainability reporting standards.

Research on artificial intelligence (AI) and its potential effects on the workplace is increasing. How AI and the futures of work are framed in traditional media has been examined in prior studies, but current research has not gone far enough in examining how AI is framed on social media. This paper aims to fill this gap by examining how people frame the futures of work and intelligent machines when they post on social media.

We investigate public interpretations, assumptions and expectations, referring to framing expressed in social media conversations. We also coded the emotions and attitudes expressed in the text data. A corpus consisting of 998 unique Reddit post titles and their corresponding 16,611 comments was analyzed using computer-aided textual analysis comprising a BERTopic model and two BERT text classification models, one for emotion and the other for sentiment analysis, supported by human judgment.

Different interpretations, assumptions and expectations were found in the conversations. Three subframes were analyzed in detail under the overarching frame of the New World of Work: (1) general impacts of intelligent machines on society, (2) undertaking of tasks (augmentation and substitution) and (3) loss of jobs. The general attitude observed in conversations was slightly positive, and the most common emotion category was curiosity.

Findings from this research can uncover public needs and expectations regarding the future of work with intelligent machines. The findings may also help shape research directions about futures of work. Furthermore, firms, organizations or industries may employ framing methods to analyze customers’ or workers’ responses or even influence the responses. Another contribution of this work is the application of framing theory to interpreting how people conceptualize the future of work with intelligent machines.

Given the critical role of user-generated content (UGC) in e-commerce, exploring various aspects of UGC can aid in understanding user purchase intention and commodity recommendation. Therefore, this study investigates the impact of UGC on purchase decisions and proposes new recommendation models based on sentiment analysis, which are verified in Douban, one of the most popular UGC websites in China.

After verifying the relationship between various factors and product sales, this study proposes two models, collaborative filtering recommendation model based on sentiment (SCF) and hidden factors topics recommendation model based on sentiment (SHFT), by combining traditional collaborative filtering model (CF) and hidden factors topics model (HFT) with sentiment analysis.

The results indicate that sentiment significantly influences purchase intention. Furthermore, the proposed sentiment-based recommendation models outperform traditional CF and HFT in terms of mean absolute error (MAE) and root mean square error (RMSE). Moreover, the two models yield different outcomes for various product categories, providing actionable insights for organizers to implement more precise recommendation strategies.

The findings of this study advocate the incorporation of UGC sentimental factors into websites to heighten recommendation accuracy. Additionally, different recommendation strategies can be employed for different products types.

This study introduces a novel perspective to the recommendation algorithm field. It not only validates the impact of UGC sentiment on purchase intention but also evaluates the proposed models with real-world data. The study provides valuable insights for managerial decision-making aimed at enhancing recommendation systems.

Applying traditional grounded theory techniques, the present research reanalyzed secondary data from four previously conducted studies to explore how generativity is manifested in young adults.

A new conceptual model of generativity was developed to depict how generativity manifests among this age group.

This study's findings provide leadership educators with a refined approach to interacting with this construct while simultaneously increasing young adults’ potential ability to experience the benefits available to them through generativity at an earlier stage in their lives.

This study advances the field of leadership education by establishing foundational insight into the uniqueness of generativity’s development in young adulthood.

The objective of this paper is to determine the impact of digital nomads on destination marketing.

The article was designed using a six-layer research onion. The research design was developed with an interpretive paradigm using an inductive multi-qualitative approach.

According to the results obtained, it was found that digital nomads play an important role in destination marketing and are effective in creating intentions to visit destinations.

The study reveals a methodologically different approach to the theme by analysing multi-qualitative data. The theoretical contribution of the article is that it shows that digital nomads' YouTube video posts regarding destinations are more effective on other digital nomads and those who want to become digital nomads.

Using sentiment analysis (SA), this study aims to examine the impact of COVID-19 on mental health and virtual learning experiences among 1,125 students at a public Argentinean faculty.

A study was conducted during the COVID-19 pandemic, surveying 1,125 students to gather their opinions. The survey data was analysed using text mining tools and SA. SA was used to extract the students’ emotions, views and feelings computationally and identify co-occurrences and patterns in related words. The study also examines educational policies implemented after the pandemic.

The prevalent emotions expressed in the comments were trust, sadness, anticipation and fear. A combination of trust and fear resulted in submission. Negative comments often included the words “virtual”, “virtual classroom”, “virtual classes” and “professor”. Two significant issues were identified: teachers’ inexperience with virtual classes and inadequate server infrastructure, leading to frequent crashes. The most effective educational policies addressed vital issues related to the “virtual classroom”.

Text mining and SA are valuable tools for decision-making during uncertain times, such as the COVID-19 pandemic. They can also provide insights to recover quality assurance processes at universities impacted by health concerns or external shocks.

The paper makes two main contributions: it conducts a SA to gain insights from comments and analyses the relationship between emotions and sentiments to identify optimal educational policies. The study pioneers exploring the link between emotions, policies and the pandemic at a public university in Argentina. This area of research still needs to be explored.

Considering the ongoing discourse on diversity, equity and inclusion, brands aim to develop marketing campaigns that demonstrate respect for all individuals. Despite these intentions, many advertisements still provoke strong negative reactions from consumers due to brand transgressions in social media marketing campaigns that violate these values. The purpose of this paper is to analyze the repercussions that such social media marketing campaigns have on brands, categorizing these campaigns as brand transgressions in social media advertising.

This research uses a mixed-method design that includes semi-structured interviews (Study 1), a content analysis (Study 2) and an online experiment (Study 3).

This paper clarifies the elements that qualify as brand transgressions in advertising within the diversity, equity and inclusion discourse. The negative electronic word-of-mouth (e-WOM) associated with brand transgressions in advertising comprises negative emotions (e.g. anger, contempt, disgust and hate) and behavioural intentions to penalize the brand (e.g. negative word-of-mouth, brand avoidance and protest behaviours). The negative e-WOM stemming from these transgressions amplifies the adverse consequences for consumer–brand relationships by negatively influencing other consumers through sympathy towards the offended parties.

This paper offers brand managers guidelines for preventing and managing negative consumer reactions towards brands based on their responses to marketing campaigns that contradict the principles of diversity, equity and inclusion.

This paper contributes to the literature on brand transgressions related to diversity, equity and inclusion values by exploring their impact on consumer–brand relationships and highlighting the pivotal role of sympathy in perpetuating negative consequences.