The interval grey QFD method for new product development: Integrate with LDA topic model to analyze online reviews

Huang, Shengqing; Zhang, Jie; Yang, Chaoxiang; Gu, Qiang; Li, Ming; Wang, Wenqiang

doi:10.1016/j.engappai.2022.105213

Cited by 22 publications

(3 citation statements)

References 53 publications

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“…Several studies have indicated that 20 topics provide the best performance [40]. LDA has been employed in text mining studies to explore online reviews and capture customers' perceptions across various research domains, including marketing [41], online education [42], and accommodation business [43]. Figure 2 illustrates the generative model of LDA, while Figure 3 displays a sample word cloud representing the generated topics.…”

Section: -1-deployed Methods (Lda)mentioning

confidence: 99%

Exploring Individuals’ Experiences with Security Attacks: A Text Mining and Qualitative Study

Abumalloh,

Alrahhal,

El-Haggar

et al. 2024

Emerg Sci J

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Cyber-attacks have become increasingly prevalent with the widespread integration of technology into various aspects of our lives. The surge in social media platform usage has prompted users to share their firsthand experiences with cyber-attacks. Despite this, previous literature has not extensively investigated individuals' experiences with these attacks. This study aims to comprehensively explore and analyze the content shared by cyber-attack victims in Saudi Arabia, encompassing text, video, and audio formats. The primary objective is to investigate the factors influencing victims' perceptions of the security risks associated with these attacks. Following data collection, preparation, and cleaning, Latent Dirichlet Allocation (LDA) is employed for topic modeling, shedding light on potential factors impacting victims. Sentiment analysis is then utilized to examine the nuanced negative and positive perceptions of individuals. NVivo is deployed for data inspection, facilitating the presentation of insightful inferences. Hierarchical clustering is implemented to explore distinct clusters within the textual dataset. The study's results underscore the critical importance of spreading awareness among individuals regarding the various tactics employed by cyber attackers. Doi: 10.28991/ESJ-2024-08-01-010 Full Text: PDF

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Section: -1-deployed Methods (Lda)mentioning

confidence: 99%

Exploring Individuals’ Experiences with Security Attacks: A Text Mining and Qualitative Study

Abumalloh,

Alrahhal,

El-Haggar

et al. 2024

Emerg Sci J

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“…Their approach to mining attributes from online reviews surpasses previous methods in terms of depth and precision. Huang et al [47] combined the Latent Dirichlet Allocation (LDA) topic model and Quality Function Deployment (QFD) to mine customer requirements and competition information from online reviews for new product development. Ji et al [48] proposed a large-scale group decision-making method to integrate the satisfaction of individual consumers on product attributes to form group satisfaction.…”

Section: Review Text-mining-based Product or Service Improvementmentioning

confidence: 99%

Text Mining and Multi-Attribute Decision-Making-Based Course Improvement in Massive Open Online Courses

Yang,

Liu,

Luo

et al. 2024

Applied Sciences

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As the leading platform of online education, MOOCs provide learners with rich course resources, but course designers are still faced with the challenge of how to accurately improve the quality of courses. Current research mainly focuses on learners’ emotional feedback on different course attributes, neglecting non-emotional content as well as the costs required to improve these attributes. This limitation makes it difficult for course designers to fully grasp the real needs of learners and to accurately locate the key issues in the course. To overcome the above challenges, this study proposes an MOOC improvement method based on text mining and multi-attribute decision-making. Firstly, we utilize word vectors and clustering techniques to extract course attributes that learners focus on from their comments. Secondly, with the help of some deep learning methods based on BERT, we conduct a sentiment analysis on these comments to reveal learners’ emotional tendencies and non-emotional content towards course attributes. Finally, we adopt the multi-attribute decision-making method TOPSIS to comprehensively consider the emotional score, attention, non-emotional content, and improvement costs of the attributes, providing course designers with a priority ranking for attribute improvement. We applied this method to two typical MOOC programming courses—C language and Java language. The experimental findings demonstrate that our approach effectively identifies course attributes from reviews, assesses learners’ satisfaction, attention, and cost of improvement, and ultimately generates a prioritized list of course attributes for improvement. This study provides a new approach for improving the quality of online courses and contributes to the sustainable development of online course quality.

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“…In the LDA technique, the number of topics should be determined, indicating that 20 topics have provided the best performance by several studies (Williams & Betak, 2018). LDA has been deployed in text mining studies to explore online reviews and incorporate customers' perceptions in several areas of research such as marketing (Huang et al, 2022), online education (Wei & Taecharungroj, 2022), and accommodation business (Sim et al, 2021). Figure 1 presents the generative model of the LDA.…”

Section: Topic Modeling (Lda)mentioning

confidence: 99%

Analyzing Big Social Data for Evaluating Environment-Friendly Tourism in Turkey

ALRAHHAL,

BOZKURT

2023

jista

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Tourism in Türkiye is fundamentally important for both the Turkish economy and travelers. Green tourism has gained increasing attention in the last few years. Analyzing big social data for evaluating environment-friendly tourism in Türkiye is important to gain an understanding of the factors impacting travelers' intention to echo-friendly hotels. To meet the goal of the study, the data was retrieved from the Tripadvisor website using a crawling technique. Machine learning techniques, particularly Latent Dirichlet Allocation (LDA), were utilized to discover satisfaction dimensions from the user-generated content. The k-means clustering approach was deployed for data segmentation. Finally, the online reviews classification model was trained and compared using Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The obtained results reveal several important dimensions that impact tourists' experience.

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The interval grey QFD method for new product development: Integrate with LDA topic model to analyze online reviews

Cited by 22 publications

References 53 publications

Exploring Individuals’ Experiences with Security Attacks: A Text Mining and Qualitative Study

Exploring Individuals’ Experiences with Security Attacks: A Text Mining and Qualitative Study

Text Mining and Multi-Attribute Decision-Making-Based Course Improvement in Massive Open Online Courses

Analyzing Big Social Data for Evaluating Environment-Friendly Tourism in Turkey

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