Contextual semantics for sentiment analysis of Twitter
- Submitting institution
-
The University of Warwick
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 6008
- Type
- D - Journal article
- DOI
-
10.1016/j.ipm.2015.01.005
- Title of journal
- Information Processing & Management
- Article number
- -
- First page
- 5
- Volume
- 52
- Issue
- 1
- ISSN
- 0306-4573
- Open access status
- Out of scope for open access requirements
- Month of publication
- January
- Year of publication
- 2016
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
3
- Research group(s)
-
I - Artificial Intelligence and Human-Centred Computing
- Citation count
- 162
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The highly-cited paper proposed the first approach for performing semantic sentiment analysis on Twitter, published in the top journal in the field. The paper won the "Honourable Mention for Best Paper" award in the Information Processing and Management (IP&M) journal for the year of 2015. The contribution opened up a new research area on semantic sentiment analysis. It impacted work on text representation learning (de Rijke, University of Amsterdam), affective common-sense reasoning (Cumbria, Nanyang Technological University), extraction of sentiment stereotypes from Twitter (Carley, CMU), and leveraging social media for the prediction of asset price (Creamer, Columbia University).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -