Sentiment-enhanced learning model for online language learning system

Li, Li

doi:10.1007/s10660-017-9284-5

Cited by 15 publications

(7 citation statements)

References 49 publications

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“…The artificial brain function of the ANN tool is at a much more advanced level than traditional computer linear logic, which is able to establish a stable network connection weight between the business data input and the accounting element record. Following the establishment of a case library based on the learning of historical economic transactions, the accounting information configuration of future economic matters can be automatically realized and integrated into the enterprise reporting system [56].…”

Section: ) Artificial Neural Networkmentioning

confidence: 99%

“…The language model algorithm is used to judge the preliminary data extracted from internal and external information and then determine the regulatory compliance of the signed contracts (see Table II for an illustration of this approach) [58]. Third, it can alert users to interactions that may have negative outcomes (e.g., complaints or behavioural problems) and provide detailed information about the reasons for their occurrence [56]. BEAT is able to analyse over 30 different languages and 30 different behavioural indicators and can be customized to meet specific risk and user requirements [59].…”

Section: A Deloittementioning

confidence: 99%

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The Impact of Artificial Intelligence and Blockchain on the Accounting Profession

et al. 2020

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Recent developments in technology have introduced dramatic changes to the practice of the accounting profession. This paper provides a comprehensive review of current developments in big data, machine learning, artificial intelligence, and blockchain utilized in general business practice and by specialized practitioners in the accounting profession worldwide. This paper explores the evolution of the accounting profession following these recent technological developments and assesses the impact of future developments. Inherent challenges and opportunities posed by these new technologies pertaining to accounting professionals and accounting educators are also examined, including an increased demand for IT professionals with accounting experience as opposed to accounting major graduates. Considering the dramatic changes and developments of AI applications in accounting, this paper reflects how all these technologies and the associated requirements of job candidates will affect the desired capabilities of accounting graduates and provides further discussion regarding what higher institutions and their accounting graduates can do to adopt such changes.

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Section: ) Artificial Neural Networkmentioning

confidence: 99%

Section: A Deloittementioning

confidence: 99%

The Impact of Artificial Intelligence and Blockchain on the Accounting Profession

et al. 2020

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“…As an emerging information carrier, danmaku contains rich and real semantic information, which is an important corpus for sentiment analysis 4 , and the sentiment analysis of danmakus has important academic and commercial value. In the academic field, the sentiment analysis of danmakus helps to explore the emotional characteristics, expand the research field of sentiment analysis, and enrich the existing research theories and related technologies 5 ; In the commercial field, danmakus sentiment analysis can effectively provide feedback of different users toward the video content, and help video platforms optimize the recommendation of video content and the management strategy of danmakus 6 – 9 ; In the field of digital governance, danmakus sentiment analysis can be used to assess the abnormal emotion level of users, providing new methods for the detection of abnormal events on the Internet and the detection of users' mental health 10 .…”

Section: Introductionmentioning

confidence: 99%

Sentiment analysis of video danmakus based on MIBE-RoBERTa-FF-BiLSTM

Zhao,

Liu,

Wang

et al. 2024

Sci Rep

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Danmakus are user-generated comments that overlay on videos, enabling real-time interactions between viewers and video content. The emotional orientation of danmakus can reflect the attitudes and opinions of viewers on video segments, which can help video platforms optimize video content recommendation and evaluate users’ abnormal emotion levels. Aiming at the problems of low transferability of traditional sentiment analysis methods in the danmaku domain, low accuracy of danmaku text segmentation, poor consistency of sentiment annotation, and insufficient semantic feature extraction, this paper proposes a video danmaku sentiment analysis method based on MIBE-RoBERTa-FF-BiLSTM. This paper constructs a “Bilibili Must-Watch List and Top Video Danmaku Sentiment Dataset” by ourselves, covering 10,000 positive and negative sentiment danmaku texts of 18 themes. A new word recognition algorithm based on mutual information (MI) and branch entropy (BE) is used to discover 2610 irregular network popular new words from trigrams to heptagrams in the dataset, forming a domain lexicon. The Maslow’s hierarchy of needs theory is applied to guide the consistent sentiment annotation. The domain lexicon is integrated into the feature fusion layer of the RoBERTa-FF-BiLSTM model to fully learn the semantic features of word information, character information, and context information of danmaku texts and perform sentiment classification. Comparative experiments on the dataset show that the model proposed in this paper has the best comprehensive performance among the mainstream models for video danmaku text sentiment classification, with an F1 value of 94.06%, and its accuracy and robustness are also better than other models. The limitations of this paper are that the construction of the domain lexicon still requires manual participation and review, the semantic information of danmaku video content and the positive case preference are ignored.

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“…Sentiment discovery and analysis (SDA) for smart education has turned out to be an effective tool for treating users (e.g., learners, teachers, and managers) who are feeling lonely and lacking sentiment guidance (Li, ). By identifying a sentiment state and its changing rules, SDA is conducive to reducing the empathy gap between teachers and students, promoting the scientific education decision‐making (Troisi, Grimaldi, Loia, & Maione, ).…”

Section: Introductionmentioning

confidence: 99%

A review on sentiment discovery and analysis of educational big‐data

Han

Wu²,

Huang

et al. 2019

WIREs Data Min & Knowl

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Sentiment discovery and analysis (SDA) aims to automatically identify the underlying attitudes, sentiments, and subjectivity towards a certain entity such as learners and learning resources. Due to its enormous potential for smart education, SDA has been deemed as a powerful technique for identifying and classifying sentiments from multimodal and multisource data over the whole process of education. For big educational data streams, SDA faces challenges in unimodal feature selection, sentiment classification, and multimodal fusion. As such, a large body of studies in the literature explores diverse approaches to SDA for educational applications. This paper provides a self‐contained, uniform overview of the SDA techniques for education. In particular, we focus on prominent studies in unimodal sentiment features and classifications (e.g., text, audio, and visual). In addition, we present a novel SDA framework of multimodal fusions, together with description of their crucial components. Based on this framework, we review different approaches to SDA on education from the perspectives of approaches and applications. After comprehensively reviewing the SDA techniques on education, we present the trends and prospectives of the future SDA research under ubiquitous education contexts. This article is categorized under: Application Areas > Education and Learning

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Sentiment-enhanced learning model for online language learning system

Cited by 15 publications

References 49 publications

The Impact of Artificial Intelligence and Blockchain on the Accounting Profession

The Impact of Artificial Intelligence and Blockchain on the Accounting Profession

Sentiment analysis of video danmakus based on MIBE-RoBERTa-FF-BiLSTM

A review on sentiment discovery and analysis of educational big‐data

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