Journal ArticleDOI
Big data analytics for disaster response and recovery through sentiment analysis
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A big data driven approach for disaster response through sentiment analysis that helps the emergency responders and rescue personnel to develop better strategies for effective information management of the rapidly changing disaster environment.About:
This article is published in International Journal of Information Management.The article was published on 2018-10-01. It has received 226 citations till now. The article focuses on the topics: Sentiment analysis & Big data.read more
Citations
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Journal ArticleDOI
Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda
TL;DR: The challenges associated with the use and impact of revitalised AI based systems for decision making are identified and a set of research propositions for information systems (IS) researchers are offered.
Journal ArticleDOI
Smart cities: Advances in research—An information systems perspective
TL;DR: This comprehensive review offers critical insight to the key underlying research themes within smart cities, highlighting the limitations of current developments and potential future directions.
Journal ArticleDOI
Towards Sustainable Energy: A Systematic Review of Renewable Energy Sources, Technologies, and Public Opinions
Atika Qazi,Fayaz Hussain,Nasrudin Abd Rahim,Glenn Hardaker,Daniyal M. Alghazzawi,Khaled Bashir Shaban,Khalid Haruna +6 more
TL;DR: The results of this study show that worldwide energy crises can be managed by integrating renewable energy sources in the power generation and the lack of public awareness is a major barrier to the acceptance of renewable energy technologies.
Journal ArticleDOI
Sentiment analysis using deep learning architectures: a review
TL;DR: This paper provides a detailed survey of popular deep learning models that are increasingly applied in sentiment analysis and presents a taxonomy of sentiment analysis, which highlights the power of deep learning architectures for solving sentiment analysis problems.
Journal ArticleDOI
Big data analytics and organizational culture as complements to swift trust and collaborative performance in the humanitarian supply chain
Rameshwar Dubey,Angappa Gunasekaran,Stephen J. Childe,David Roubaud,Samuel Fosso Wamba,Mihalis Giannakis,Cyril Foropon +6 more
TL;DR: In this paper, the authors conceptualized an original theoretical model to show, using the competing value model (CVM), how big data analytics capability (BDAC) under a moderating influence of organizational culture, affects swift trust (ST) and collaborative performance (CP).
References
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ReportDOI
Building a large annotated corpus of English: the penn treebank
TL;DR: As a result of this grant, the researchers have now published on CDROM a corpus of over 4 million words of running text annotated with part-of- speech (POS) tags, which includes a fully hand-parsed version of the classic Brown corpus.
Thumbs up? Sentiment Classiflcation using Machine Learning Techniques
TL;DR: In this paper, the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative, was considered and three machine learning methods (Naive Bayes, maximum entropy classiflcation, and support vector machines) were employed.
Proceedings ArticleDOI
Thumbs up? Sentiment Classification using Machine Learning Techniques
TL;DR: This work considers the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative, and concludes by examining factors that make the sentiment classification problem more challenging.
Book
Sentiment Analysis and Opinion Mining
TL;DR: Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language as discussed by the authors and is one of the most active research areas in natural language processing and is also widely studied in data mining, Web mining, and text mining.
Journal ArticleDOI
Beyond the hype
Amir H. Gandomi,Murtaza Haider +1 more
TL;DR: The need to develop appropriate and efficient analytical methods to leverage massive volumes of heterogeneous data in unstructured text, audio, and video formats is highlighted and the need to devise new tools for predictive analytics for structured big data is reinforced.