Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/23766
Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: Sentiment Big Data Flow Analysis by Means of Dynamic Linguistic Patterns
Authors: Poria, Soujanya
Cambria, Erik
Gelbukh, Alexander
Bisio, Federica
Hussain, Amir
Contact Email: ahu@cs.stir.ac.uk
Keywords: Big Data
Internet
computational linguistics
data flow analysis
data structures
natural languages
social networking (online)
text analysis
Behavioral science
Biological system modeling
Electronic circuits
Knowledge based systems
Learning systems
Linguistics
Pragmatics
Semantics
Sentiment analysis
artificial intelligence
big social data
common-sense computing
computational intelligence technique
dynamic linguistic pattern
dynamic polarity inference
human brain
natural language text
sentiment data flow analysis
social media marketing
statistical methods
unstructured data
word-level text analysis
Issue Date: Nov-2015
Publisher: IEEE
Citation: Poria S, Cambria E, Gelbukh A, Bisio F & Hussain A (2015) Sentiment Big Data Flow Analysis by Means of Dynamic Linguistic Patterns, IEEE Computational Intelligence Magazine, 10 (4), pp. 26-36.
Abstract: Emulating the human brain is one of the core challenges of computational intelligence, which entails many key problems of artificial intelligence, including understanding human language, reasoning, and emotions. In this work, computational intelligence techniques are combined with common-sense computing and linguistics to analyze sentiment data flows, i.e., to automatically decode how humans express emotions and opinions via natural language. The increasing availability of social data is extremely beneficial for tasks such as branding, product positioning, corporate reputation management, and social media marketing. The elicitation of useful information from this huge amount of unstructured data, however, remains an open challenge. Although such data are easily accessible to humans, they are not suitable for automatic processing: machines are still unable to effectively and dynamically interpret the meaning associated with natural language text in very large, heterogeneous, noisy, and ambiguous environments such as the Web. We present a novel methodology that goes beyond mere word-level analysis of text and enables a more efficient transformation of unstructured social data into structured information, readily interpretable by machines. In particular, we describe a novel paradigm for real-time concept-level sentiment analysis that blends computational intelligence, linguistics, and common-sense computing in order to improve the accuracy of computationally expensive tasks such as polarity detection from big social data. The main novelty of the paper consists in an algorithm that assigns contextual polarity to concepts in text and flows this polarity through the dependency arcs in order to assign a final polarity label to each sentence. Analyzing how sentiment flows from concept to concept through dependency relations allows for a better understanding of the contextual role of each concept in text, to achieve a dynamic polarity inference that outperforms state-of-the- art statistical methods in terms of both accuracy and training time.
Type: Journal Article
URI: http://hdl.handle.net/1893/23766
DOI Link: http://dx.doi.org/10.1109/MCI.2015.2471215
Rights: c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Affiliation: University of Stirling
Nanyang Technological University
Instituto Polit├ęcnico Nacional
University of Genoa
Computing Science - CSM Dept

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