"Challenges and future in deep learning for sentiment analysis: a comprehensive review and a proposed novel hybrid approach"

Artificial Intelligence Review, Год журнала: 2024, Номер 57(3)

Опубликована: Фев. 19, 2024

Abstract Social media is used to categorise products or services, but analysing vast comments time-consuming. Researchers use sentiment analysis via natural language processing, evaluating methods and results conventionally through literature reviews assessments. However, our approach diverges by offering a thorough analytical perspective with critical analysis, research findings, identified gaps, limitations, challenges future prospects specific deep learning-based in recent times. Furthermore, we provide in-depth investigation into categorizing prevalent data, pre-processing methods, text representations, learning models, applications. We conduct evaluation of advances architectures, assessing their pros cons. Additionally, offer meticulous methodologies, integrating insights on applied tools, strengths, weaknesses, performance results, detailed feature-based examination. present discussion the challenges, drawbacks, factors contributing successful enhancement accuracy within realm analysis. A comparative article clearly shows that capsule-based RNN approaches give best an 98.02% which CNN RNN-based models. implemented various advanced deep-learning models across four benchmarks identify top performers. introduced innovative CRDC (Capsule Deep Bi structured RNN) model, demonstrated superior compared other methods. Our proposed achieved remarkable different databases: IMDB (88.15%), Toxic (98.28%), CrowdFlower (92.34%), ER (95.48%). Hence, this method holds promise for automated potential deployment.

Язык: Английский

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Customer profiling, segmentation, and sales prediction using AI in direct marketing

Neural Computing and Applications, Год журнала: 2023, Номер 36(9), С. 4995 - 5005

Опубликована: Дек. 23, 2023

Abstract In the current business environment, where customer is primary focus, effective communication between marketing and senior management vital for success. Effective profiling a cornerstone of strategic decision-making digital start-ups seeking sustainable growth satisfaction. This research investigates clustering customers based on recency, frequency, monetary (RFM) analysis employs validation metrics to derive optimal clusters. The K-means algorithm, coupled with Elbow method, Silhouette coefficient, Gap Statistics facilitates identification distinct segments. study unveils three clusters unique characteristics: new (Cluster A), best B), intermittent C). For platform-based Edutech start-ups, Cluster A underscores importance tailored learning content support, B emphasizes personalized incentives, C suggests re-engagement strategies. By understanding addressing diverse needs these clusters, can forge enduring connections, optimize engagement, fuel growth.

Язык: Английский

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Machine learning applied to tourism: A systematic review

Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery, Год журнала: 2024, Номер 14(5)

Опубликована: Июль 4, 2024

Abstract The application of machine learning techniques in the field tourism is experiencing a remarkable growth, as they allow to propose efficient solutions problems present this sector, by means an intelligent analysis data their specific context. increase work requires exhaustive through quantitative approach research activity, contributing deeper understanding progress field. Thus, different approaches will be analyzed, such planning, forecasting, recommendation, prevention, and security, among others. As result analysis, other findings, greater impact supervised tourism, more specifically those based on neural networks, has been confirmed. results study would researchers not only have most up‐to‐date accurate overview but also identify appropriate apply domain interest, well similar with which compare own solutions. This article categorized under: Application Areas > Society Culture Technologies Machine Learning Business Industry

Язык: Английский

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Mining online hotel reviews using big data and machine learning: An empirical study from an emerging country

Annals of Tourism Research Empirical Insights, Год журнала: 2025, Номер 6(1), С. 100170 - 100170

Опубликована: Март 6, 2025

Язык: Английский

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Emotions Matter: A Systematic Review and Meta-Analysis of the Detection and Classification of Students’ Emotions in STEM during Online Learning

Education Sciences, Год журнала: 2023, Номер 13(9), С. 914 - 914

Опубликована: Сен. 8, 2023

In recent years, the rapid growth of online learning has highlighted need for effective methods to monitor and improve student experiences. Emotions play a crucial role in shaping students’ engagement, motivation, satisfaction environments, particularly complex STEM subjects. this context, sentiment analysis emerged as promising tool detect classify emotions expressed textual visual forms. This study offers an extensive literature review using preferred reporting items systematic reviews meta-analyses (PRISMA) technique on The analyses applicability, challenges, limitations text- facial-based techniques educational settings by reviewing 57 peer-reviewed research articles out 236 articles, published between 2015 2023, initially identified through comprehensive search strategy. Through scrutiny process, these were selected based their relevance contribution topic. review’s findings indicate that holds significant potential improving experiences, encouraging personalised learning, promoting environment. Educators administrators can gain valuable insights into perceptions employing computational analyse interpret text facial expressions. However, also identifies several challenges associated with settings. These include accurate emotion detection interpretation, addressing cultural linguistic variations, ensuring data privacy ethics, reliance high-quality sources. Despite highlights immense transforming experiences subjects recommends further development area.

Язык: Английский

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Retail Industry Analytics: Unraveling Consumer Behavior through RFM Segmentation and Machine Learning

Опубликована: Май 30, 2024

Язык: Английский

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Hotel digital intelligence capability: dimension exploration and scale development

Journal of Hospitality and Tourism Technology, Год журнала: 2025, Номер unknown

Опубликована: Янв. 15, 2025

Purpose This study aims to explore the dimensional structure of hotel digital intelligence capability and develop a measurement scale. Design/methodology/approach adopts qualitative quantitative approaches conduct an exploratory inquiry into structural dimensions with help grounded theory. Based on this, several questionnaires were developed test scale verify its validity. Findings The results reveal that comprises four dimensions: data collection processing capability, customer service personalization decision support sustainable development capability. consists factors 13 items, reliability validity tests demonstrating ideal levels. Originality/value not only provides new perspective understand but also develops corresponding scale, laying solid theoretical basis for managers scientifically evaluate this achieve competitive advantage.

Язык: Английский

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The Role of Digital Footprints for Destination Competitiveness and Engagement: Utilizing Mobile Technology for Tourist Segmentation Integrating Personality Traits

International Journal of Tourism Research, Год журнала: 2025, Номер 27(1)

Опубликована: Янв. 1, 2025

ABSTRACT This study presents a novel approach to tourism market segmentation by integrating personality traits enhance traditional demographic methods. In partnership with Cumbria Tourism (local DMO), this conducted in Cumbria, UK, home of the Lake District National Park, research utilized 1217 quantitative surveys analyse visitor traits, motivations, and activities. Through factor cluster analysis, five unique segments were identified: Reserved Explorers, Culturally Curious, Diligent Adventurers, Social Balanced Explorers. Each segment displayed distinctive activities, further supplemented chi‐square tests that highlighted socio‐demographic differences. The findings underscore value incorporating through digital footprints for dynamic segmentation. methodology not only offers deeper insights into profiles, but it also aids developing customized marketing strategies products, determining activity preferences providing competitive edge destinations globally.

Язык: Английский

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Sociodemographic relationships of motivations, satisfaction, and loyalty in religious tourism: A study of the pilgrimage to the city Mecca

PLoS ONE, Год журнала: 2023, Номер 18(3), С. e0283720 - e0283720

Опубликована: Март 30, 2023

Religious tourism is a growing sector of the market because many social and cultural changes in 21st century. Pilgrimage centers worldwide are considered important at levels religion, heritage, culture tourism. Despite popularity journeys to pilgrimage their global importance, there still lack knowledge about dimensionality impact socio-demographic factors on visiting these centers. This study aims (i) establish motivational dimensions Mecca (ii) identify relationship between aspects pilgrims motivation (iii) determine pilgrims, satisfaction, loyalty. The research was carried out who had visited Mecca. sample consisted 384 online surveys. Factor analysis multiple regression method were applied analyze data. results show three dimensions: religious, social, cultural, shopping. Additionally, evidence age, marital status average daily expenditure per person with some variables. Similarly, found other variables such as satisfaction helps companies pay attention pilgrims' characteristics match them motivation, loyalty during planning process.

Язык: Английский

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Deep Learning for Table Detection and Structure Recognition: A Survey

ACM Computing Surveys, Год журнала: 2024, Номер 56(12), С. 1 - 41

Опубликована: Апрель 10, 2024

Tables are everywhere, from scientific journals, articles, websites, and newspapers all the way to items we buy at supermarket. Detecting them is thus of utmost importance automatically understanding content a document. The performance table detection has substantially increased thanks rapid development deep learning networks. goals this survey provide profound comprehension major developments in field Table Detection, offer insight into different methodologies, systematic taxonomy approaches. Furthermore, an analysis both classic new applications field. Lastly, datasets source code existing models organized reader with compass on vast literature. Finally, go over architecture utilizing various object structure recognition methods create effective efficient system, as well set trends keep up state-of-the-art algorithms future research. We have also public GitHub repository where will be updating most recent publications, open data, code. available https://github.com/abdoelsayed2016/table-detection-structure-recognition.

Язык: Английский

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