Search Results - "Automatic Keyword Extraction from Textual Data"

A Multimodal Latent-Features-Based Service Recommendation System for the Social Internet of Things

Authors: Amar Khelloufi Huansheng Ning Abdenacer Naouri et al.

Source: IEEE Transactions on Computational Social Systems. 11:5388-5403

Subject Terms: Big Data Analysis in Various Industries, FOS: Computer and information sciences, Internet of Things, Ontology-Based Healthcare Context Information Modeling, Health Professions, Computer Science - Information Retrieval, FOS: Economics and business, Health Information Management, Artificial Intelligence, Computer security, Context Information Model, Service (business), Health Sciences, Business, P2P Cloud Network Services, Social and Information Networks (cs.SI), Graph-Based, Marketing, Topic Modeling, Computer Science - Social and Information Networks, Computer science, Automatic Keyword Extraction from Textual Data, World Wide Web, Computer Science, Physical Sciences, Internet privacy, Bio-detection for Smart Health Service, Information Retrieval (cs.IR), The Internet, Information Systems

Access URL: http://arxiv.org/abs/2306.01163

TED Talks Comments Sentiment Classification Using Machine Learning Algorithms

Authors: Mohamed Elbadaoui Noreddine Gherabi Fatima Quanouni

Source: Revue d'Intelligence Artificielle. 38:885-892

Subject Terms: Artificial intelligence, Twitter Sentiment, Computer science, 7. Clean energy, Automatic Keyword Extraction from Textual Data, Machine Learning, Algorithm, Sentiment analysis, Sentiment Analysis and Opinion Mining, Artificial Intelligence, Emotion Recognition, Aspect-based Sentiment Analysis, Computer Science, Physical Sciences, Machine learning, Sentiment Analysis

Subject Detection of Algerian Posts for Opinion Analysis

Authors: Kheira Zineb Bousmaha Khaoula Hamadouche Nawel Cheurfaoui et al.

Source: Ingénierie des systèmes d information. 29:821-829

Subject Terms: FOS: Computer and information sciences, Text Mining, FOS: Political science, 4. Education, 16. Peace & justice, Computer science, Detection and Prevention of Phishing Attacks, Automatic Keyword Extraction from Textual Data, World Wide Web, Sentiment Analysis and Opinion Mining, Behavioral Analysis, 5. Gender equality, Artificial Intelligence, Aspect-based Sentiment Analysis, Subject (documents), Computer Science, Physical Sciences, Bot Detection, 8. Economic growth, Sentiment Analysis, Information retrieval, 10. No inequality, Political science, Information Systems

Applying self-attention model to learn both Empirical Risk Minimization and Invariant Risk Minimization for multimedia recommendation

Authors: Haitao Zhao Yangqi Huang Kun Zhao et al.

Source: Applied and Computational Engineering. 44:33-47

Subject Terms: FOS: Computer and information sciences, Artificial intelligence, Minification, Content-Based Recommendation, Trust-Aware Recommender Systems, Empirical risk minimization, Invariant (physics), Computer science, Automatic Keyword Extraction from Textual Data, World Wide Web, Sentiment Analysis and Opinion Mining, Context-Aware Recommender Systems, Recommender System Technologies, Multimedia, Artificial Intelligence, Emotion Recognition, Mathematical physics, Computer Science, Physical Sciences, Machine learning, FOS: Mathematics, User Modeling, Recommender system, Mathematics, Information Systems

TTC-QuAli: A Text-Table-Chart Dataset for Multimodal Quantity Alignment

Authors: Haoyu Dong Haochen Wang Anda Zhou et al.

Source: Proceedings of the 17th ACM International Conference on Web Search and Data Mining. :181-189

Subject Terms: Artificial intelligence, Table (database), Natural language processing, Statistics, Statistical Machine Translation and Natural Language Processing, Computer science, Automatic Keyword Extraction from Textual Data, Database, Artificial Intelligence, 13. Climate action, Computer Science, Physical Sciences, FOS: Mathematics, Information retrieval, Chart, 14. Life underwater, Textual Data, Mathematics, Natural Language Processing

Extracting Explicit and Implicit Aspects Using Deep Learning

Authors: Mikail Muhammad Azman Busst Kalaiarasi Sonai Muthu Anbananthen Subarmaniam Kannan et al.

Source: Emerging Science Journal, Vol 8, Iss 1, Pp 61-76 (2024)

Subject Terms: Artificial intelligence, Economics, Social Sciences, explicit aspects, Data science, Sentiment analysis, Task (project management), Artificial Intelligence, Part-of-Speech Tagging, Machine learning, deep learning, aspect extraction, T1-995, Information retrieval, Technology (General), H1-99, Natural language processing, 4. Education, implicit aspects, General Social Sciences, Computer science, Q300-390 Cybernetics, Management, Social sciences (General), Automatic Keyword Extraction from Textual Data, Sentiment Analysis and Opinion Mining, Computational Text Analysis in Social Sciences, Computer Science, Physical Sciences, SemEval

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Access URL: https://doaj.org/article/e0ef7309f0b641fabad844f3042968d1

Unravelling Emotional Tones: A Hybrid Optimized Model for Sentiment Analysis in Tamil Regional Languages

Authors: M. Sangeetha K. Nimala

Source: Journal of Machine and Computing. :114-126

Subject Terms: Tamil, Artificial intelligence, Ambiguity, Support vector machine, 02 engineering and technology, 01 natural sciences, 7. Clean energy, Sentiment analysis, Identification (biology), Artificial Intelligence, Aspect-based Sentiment Analysis, Global Positioning System, 0103 physical sciences, Multi-label Text Classification in Machine Learning, 0202 electrical engineering, electronic engineering, information engineering, Sentiment Analysis, Psychology, 10. No inequality, Biology, Hindi, Naive Bayes classifier, Adaptation (eye), Natural language processing, 4. Education, Botany, Ambiguity resolution, Linguistics, Computer science, FOS: Philosophy, ethics and religion, Programming language, Automatic Keyword Extraction from Textual Data, FOS: Psychology, Philosophy, Sentiment Analysis and Opinion Mining, Natural language, Emotion Recognition, GNSS applications, Computer Science, Physical Sciences, 8. Economic growth, FOS: Languages and literature, Telecommunications, Language identification, Neuroscience

Emo-SL Framework: Emoji Sentiment Lexicon Using Text-Based Features and Machine Learning for Sentiment Analysis

Authors: Manar Alfreihat Omar Almousa Yahya Tashtoush et al.

Source: IEEE Access, Vol 12, Pp 81793-81812 (2024)

Subject Terms: Artificial intelligence, Emoji sentiment lexicon for Arabic (Emo-SL), machine learning (ML), Natural language processing, data modeling and analysis, Computer science, TK1-9971, Automatic Keyword Extraction from Textual Data, Emoji, Social media, World Wide Web, Sentiment analysis, Sentiment Analysis and Opinion Mining, Artificial Intelligence, Computer Science, Physical Sciences, Machine learning, VADER model, Electrical engineering. Electronics. Nuclear engineering, social media analysis, Lexicon, Arabic-language tweets, Natural Language Processing

Access URL: https://doaj.org/article/784f5fc567c749a98a58589d923d3c16

Analyzing emotions in online classes: Unveiling insights through topic modeling, statistical analysis, and random walk techniques

Authors: Benyoussef Abdellaoui Ahmed Remaida Zineb Sabri et al.

Source: International Journal of Cognitive Computing in Engineering, Vol 5, Iss, Pp 221-236 (2024)

Subject Terms: Science, Random walk, Data science, Artificial Intelligence, Sentiment Analysis, FOS: Mathematics, Psychology, NMF, Facial emotion recognition, 10. No inequality, Educational Data Mining, Emotion detection, 4. Education, Statistics, Deep learning, QA75.5-76.95, Computer science, Learning Analytics, Topic modeling, Computer Science Applications, Automatic Keyword Extraction from Textual Data, FOS: Psychology, Sentiment Analysis and Opinion Mining, Online Learning, Statistical analysis, Electronic computers. Computer science, Emotion Recognition, Computer Science, Physical Sciences, 8. Economic growth, Emotion recognition, Educational Data Mining and Learning Analytics, Mathematics

Access URL: https://doaj.org/article/fe943aebe89f402096fe6cc7d7026bcb

Aspect Based Sentiment Analysis: Feature Extraction using Latent Dirichlet Allocation (LDA) and Term Frequency - Inverse Document Frequency (TF-IDF) in Machine Learning (ML)

Authors: Shakirah Mohd Sofi Ali Selamat

Source: Malaysian Journal of Information and Communication Technology (MyJICT). :169-179

Subject Terms: Artificial intelligence, Text Mining, Feature (linguistics), Latent Dirichlet allocation, Geometry, Pattern recognition (psychology), Twitter Sentiment, Quantum mechanics, Term (time), tf–idf, Topic model, Artificial Intelligence, Aspect-based Sentiment Analysis, Multi-label Text Classification in Machine Learning, Sentiment Analysis, FOS: Mathematics, Natural language processing, Physics, Linguistics, Computer science, FOS: Philosophy, ethics and religion, 3. Good health, Automatic Keyword Extraction from Textual Data, Philosophy, Sentiment Analysis and Opinion Mining, Computer Science, Physical Sciences, FOS: Languages and literature, Feature extraction, Inverse, Mathematics

Automated Abstractive Text Summarization Using Multidimensional Long Short-Term Memory

Authors: Anwar Khan

Source: Quaid-e-Awam University Research Journal of Engineering, Science & Technology. 21:01-11

Subject Terms: Data Quality Assessment and Improvement, Artificial intelligence, Topic Modeling, Natural language processing, Physics, Social Sciences, Automatic summarization, Management Science and Operations Research, Computer science, Quantum mechanics, Term (time), Long-term memory, Decision Sciences, Automatic Keyword Extraction from Textual Data, FOS: Psychology, Cognition, Artificial Intelligence, Computer Science, Physical Sciences, Information retrieval, Psychology, Textual Data, Text Classification, Natural Language Processing, Neuroscience

Student textual feedback sentiment analysis using machine learning techniques to improve the quality of education

Authors: Naseer Ahmed Mansoor Ahmed Khouro Afrasiyab Khan et al.

Source: Pakistan Journal of Engineering, Technology & Science. 11:32-40

Subject Terms: Artificial intelligence, Epistemology, Data science, Sentiment analysis, Artificial Intelligence, Machine learning, Psychology, 10. No inequality, Student Engagement, Educational Data Mining, Data-driven Education, Natural language processing, 4. Education, Computer science, Learning Analytics, Mathematics education, Computer Science Applications, FOS: Philosophy, ethics and religion, 3. Good health, Automatic Keyword Extraction from Textual Data, FOS: Psychology, Philosophy, Sentiment Analysis and Opinion Mining, Computer Science, Physical Sciences, Quality (philosophy), Student Performance Prediction, Educational Data Mining and Learning Analytics

Book Recommendation System based on Course Descriptions using Cosine Similarity

Authors: Vatinee Nuipian Jirawat Chuaykhun

Source: Proceedings of the 2023 7th International Conference on Natural Language Processing and Information Retrieval. :273-277

Subject Terms: FOS: Computer and information sciences, Artificial intelligence, Natural language processing, 4. Education, Application of Fuzzy Logic in Educational Assessment, Content-Based Recommendation, Trust-Aware Recommender Systems, Intelligent Tutoring Systems, Pattern recognition (psychology), Computer science, Automatic Keyword Extraction from Textual Data, Context-Aware Recommender Systems, Engineering, Aerospace engineering, Recommender System Technologies, Artificial Intelligence, Collaborative Filtering, Computer Science, Physical Sciences, Course (navigation), Image (mathematics), Information retrieval, Similarity (geometry), Cosine similarity, Information Systems

Answering Subjective Induction Questions on Products by Summarizing Multi-sources Multi-viewpoints Knowledge

Authors: Yufeng Zhang Mengxiang Wang Jianxing Yu

Source: 2023 IEEE International Conference on Data Mining (ICDM). :848-857

Subject Terms: FOS: Computer and information sciences, Artificial intelligence, Computer Science - Artificial Intelligence, Economics, Construct (python library), FOS: Political science, Variety (cybernetics), Perspective (graphical), Set (abstract data type), FOS: Law, Visual arts, Data science, Task (project management), Artificial Intelligence, Question answering, Information retrieval, Political science, Natural Language Processing, Computer Science - Computation and Language, Phone, Geography, Topic Modeling, Natural language processing, Linguistics, Statistical Machine Translation and Natural Language Processing, Computer science, FOS: Philosophy, ethics and religion, Management, Programming language, Automatic Keyword Extraction from Textual Data, Viewpoints, Philosophy, Artificial Intelligence (cs.AI), Computer Science, Physical Sciences, FOS: Languages and literature, Relevance (law), Benchmark (surveying), Law, Computation and Language (cs.CL), Art, Geodesy

Access URL: http://arxiv.org/abs/2309.05938

A BiLSTM-CF and BiGRU-based Deep Sentiment Analysis Model to Explore Customer Reviews for Effective Recommendations

Authors: Muhammad Rizwan Rashid Rana Asif Nawaz Tariq Ali et al.

Source: Engineering, Technology & Applied Science Research. 13:11739-11746

Subject Terms: Artificial intelligence, Sociology and Political Science, Feature (linguistics), FOS: Political science, Social Sciences, FOS: Law, 02 engineering and technology, Data science, Social media, Sentiment analysis, Artificial Intelligence, Aspect-based Sentiment Analysis, Machine learning, 0202 electrical engineering, electronic engineering, information engineering, Sentiment Analysis, Information retrieval, Political science, Preprocessor, Geography, Impact of Social Media on Consumer Behavior, Politics, Linguistics, Computer science, FOS: Philosophy, ethics and religion, Automatic Keyword Extraction from Textual Data, World Wide Web, Philosophy, Sentiment Analysis and Opinion Mining, Emotion Recognition, Computer Science, Physical Sciences, FOS: Languages and literature, Benchmark (surveying), Representation (politics), Law, Geodesy, Embedding

Improving Arabic Sentiment Analysis Using LSTM Based on Word Embedding Models

Authors: Youssra Zahidi Yassine Al-Amrani Yacine El Younoussi

Source: Vietnam Journal of Computer Science, Vol 10, Iss 03, Pp 391-407 (2023)

Subject Terms: Artificial intelligence, Word (group theory), Word embedding, Information technology, 02 engineering and technology, Sentiment analysis, deep learning (DL), Artificial Intelligence, Aspect-based Sentiment Analysis, 0202 electrical engineering, electronic engineering, information engineering, Sentiment Analysis, Natural Language Processing, FastText and Word2Vec, Arabic, Topic Modeling, long short-term memory (LSTM), Natural language processing, Word Representation, Deep learning, Linguistics, QA75.5-76.95, word embedding, T58.5-58.64, Computer science, FOS: Philosophy, ethics and religion, Automatic Keyword Extraction from Textual Data, Philosophy, Sentiment Analysis and Opinion Mining, Electronic computers. Computer science, Emotion Recognition, Computer Science, Physical Sciences, FOS: Languages and literature, Arabic sentiment analysis (ASA), Word2vec, Embedding

Access URL: https://doaj.org/article/b70ad1318fb444149bd628f3847d412d

Ontology based Feature Selection and Weighting for Text classification using Machine Learning

Authors: Djelloul Bouchiha Abdelghani Bouziane Noureddine Doumi

Source: Journal of Information Technology and Computing. 4:1-14

Subject Terms: FOS: Computer and information sciences, Artificial intelligence, Support vector machine, Class (philosophy), Feature (linguistics), 02 engineering and technology, Epistemology, Weighting, Pattern recognition (psychology), Feature vector, Web Data Extraction and Crawling Techniques, Machine Learning Algorithms, Selection (genetic algorithm), Artificial Intelligence, Multi-label Text Classification in Machine Learning, Machine learning, 0202 electrical engineering, electronic engineering, information engineering, Syntax, Feature Selection, Multi-label Learning, Content Adaptation, Text Classification, Preprocessor, Ontology, Natural language processing, 4. Education, Linguistics, Computer science, FOS: Philosophy, ethics and religion, Automatic Keyword Extraction from Textual Data, Philosophy, Computer Science, Physical Sciences, Feature selection, FOS: Languages and literature, Medicine, Radiology, Information Systems

Sentiment analysis of sarcasm detection in social media

Authors: S.K. Alaramma Adamu Adamu Habu Brian Yau et al.

Source: Gadau Journal of Pure and Allied Sciences. 2:76-82

Subject Terms: Irony, Artificial intelligence, Support vector machine, Social psychology, 01 natural sciences, Social media, Sentiment analysis, Feeling, Context (archaeology), Artificial Intelligence, Aspect-based Sentiment Analysis, Multi-label Text Classification in Machine Learning, Mood, Machine learning, 0502 economics and business, Sentiment Analysis, Psychology, Biology, 0105 earth and related environmental sciences, Sarcasm, Naive Bayes classifier, Natural language processing, 05 social sciences, Paleontology, Linguistics, 16. Peace & justice, Computer science, FOS: Philosophy, ethics and religion, Automatic Keyword Extraction from Textual Data, FOS: Psychology, World Wide Web, Philosophy, Sentiment Analysis and Opinion Mining, Emotion Recognition, Computer Science, Physical Sciences, FOS: Languages and literature

An unsupervised linguistic-based model for automatic glossary term extraction from a single PDF textbook

Authors: Ashraf Soliman

Source: Education and Information Technologies. 28:16089-16125

Subject Terms: FOS: Computer and information sciences, Artificial intelligence, Sentence, Extraction, 02 engineering and technology, Quantum mechanics, Mathematical analysis, Information Retrieval Techniques and Evaluation, Term (time), Artificial Intelligence, Computer security, 0202 electrical engineering, electronic engineering, information engineering, FOS: Mathematics, Key (lock), Text Classification, Language Models, Natural Language Processing, Domain (mathematical analysis), Topic Modeling, Natural language processing, Physics, 4. Education, Word Representation, Linguistics, Computer science, Language model, FOS: Philosophy, ethics and religion, Automatic Keyword Extraction from Textual Data, Philosophy, Natural language, Computer Science, Physical Sciences, FOS: Languages and literature, Chunking (psychology), Lexical analysis, Glossary, Mathematics, Information Systems

Building a Chatbot System to Analyze Opinions of English Comments

Authors: Hung T. Nguyen Nguyen Tan Quan Nguyen et al.

Source: Информатика и автоматизация, Vol 22, Iss 2, Pp 289-315 (2023)

Subject Terms: Artificial intelligence, Knowledge management, FOS: Political science, offensive comments, FOS: Law, Epistemology, 7. Clean energy, Data science, 12. Responsible consumption, behavioral culture, Social media, FOS: Economics and business, Sentiment analysis, Artificial Intelligence, Advertising, Aspect-based Sentiment Analysis, Service (business), Sentiment Analysis, Business, ontology, 10. No inequality, Political science, online, Publishing, Marketing, 9. Industry and infrastructure, chatbot, QA75.5-76.95, Computer science, Process (computing), FOS: Philosophy, ethics and religion, 3. Good health, Automatic Keyword Extraction from Textual Data, World Wide Web, Philosophy, Operating system, Sentiment Analysis and Opinion Mining, Chatbots, sentiment analysis, Electronic computers. Computer science, Computer Science, Physical Sciences, Artificial Intelligence in Service Industry, opinion mining, Quality (philosophy), Publication, Law, Chatbot

Access URL: https://doaj.org/article/a4a77aac8cc844c98f259227899d7659