Knowledge-based approaches to concept-level sentiment analysis

Erik Cambria; Bjorn Schuller; Bing Liu; Haixun Wang; Catherine Havasi

doi:10.1109/MIS.2013.45

Knowledge-based approaches to concept-level sentiment analysis

Erik Cambria
, Bjorn Schuller
, Bing Liu
, Haixun Wang
, Catherine Havasi

Chair of Health Informatics

National University of Singapore
UIC ECE-CSN-Lab
Microsoft Research Asia
Massachusetts Institute of Technology

Research output: Contribution to journal › Review article › peer-review

120 Scopus citations

Abstract

The guest editors introduce novel approaches to opinion mining and sentiment analysis that go beyond a mere word-level analysis of text and provide concept-level methods. Such approaches allow a more efficient passage from (unstructured) textual information to (structured) machine-processable data, in potentially any domain.

Original language	English
Article number	6547971
Pages (from-to)	12-14
Number of pages	3
Journal	IEEE Intelligent Systems
Volume	28
Issue number	2
DOIs	https://doi.org/10.1109/MIS.2013.45
State	Published - 2013

Keywords

concept-level sentiment analysis
data mining
intelligent systems
knowledge mining
online social data
opinion mining

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10.1109/MIS.2013.45

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title = "Knowledge-based approaches to concept-level sentiment analysis",

abstract = "The guest editors introduce novel approaches to opinion mining and sentiment analysis that go beyond a mere word-level analysis of text and provide concept-level methods. Such approaches allow a more efficient passage from (unstructured) textual information to (structured) machine-processable data, in potentially any domain.",

keywords = "concept-level sentiment analysis, data mining, intelligent systems, knowledge mining, online social data, opinion mining",

author = "Erik Cambria and Bjorn Schuller and Bing Liu and Haixun Wang and Catherine Havasi",

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AU - Cambria, Erik

AU - Schuller, Bjorn

AU - Liu, Bing

AU - Wang, Haixun

AU - Havasi, Catherine

PY - 2013

Y1 - 2013

N2 - The guest editors introduce novel approaches to opinion mining and sentiment analysis that go beyond a mere word-level analysis of text and provide concept-level methods. Such approaches allow a more efficient passage from (unstructured) textual information to (structured) machine-processable data, in potentially any domain.

AB - The guest editors introduce novel approaches to opinion mining and sentiment analysis that go beyond a mere word-level analysis of text and provide concept-level methods. Such approaches allow a more efficient passage from (unstructured) textual information to (structured) machine-processable data, in potentially any domain.

KW - concept-level sentiment analysis

KW - data mining

KW - intelligent systems

KW - knowledge mining

KW - online social data

KW - opinion mining

UR - https://www.scopus.com/pages/publications/84880172556

U2 - 10.1109/MIS.2013.45

DO - 10.1109/MIS.2013.45

M3 - Review article

AN - SCOPUS:84880172556

SN - 1541-1672

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SP - 12

EP - 14

JO - IEEE Intelligent Systems

JF - IEEE Intelligent Systems

IS - 2

M1 - 6547971

ER -

Knowledge-based approaches to concept-level sentiment analysis

Abstract

Keywords

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