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Information Retrieval and Social Media Mining
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This book presents diverse contributions related to some of the latest advances in the field of personalization and recommender systems, as well as social media and sentiment analysis. The work comprises several articles that address different problems in these areas by means of recent techniques such as deep learning, methods to analyze the structure and the dynamics of social networks, and modern language processing approaches for sentiment analysis, among others. The proposals included in the book are representative of some highly topical research directions and cover different application domains where they have been validated. These go from the recommendation of hotels, movies, music, documents, or pharmacy cross-selling to sentiment analysis in the field of telemedicine and opinion mining on news, also including the study of social capital on social media and dynamics aspects of the Twitter social network.
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Keywords
- collaborative filtering
- cross-sales
- Digital libraries
- ego network
- events
- feedback collection
- graph convolutional neural network
- History of engineering & technology
- homophily
- hybrid recommender systems
- Information retrieval
- Measurement
- multi-criteria
- music recommender systems
- n/a
- network dynamics
- open-access
- operationalization
- opinion mining
- opinion summarization
- over-expectation
- penalty
- Pharmacy
- popularity bias
- real-world data
- recommender system
- recommender systems
- scoping review
- semantic similarity measures
- sentiment analysis
- social capital
- Social influence
- Social media
- Social Trust
- streaming services
- Technology, engineering, agriculture
- Technology: general issues
- telehomecare
- Telemedicine
- telemonitoring
- Text Mining
- topic modeling
- under-expectation
- Utility
- Web-based questionnaire
- word embeddings
Links
DOI: 10.3390/books978-3-0365-0247-2Editions