Contextual semantics for sentiment analysis of Twitter
- Submitting institution
-
Aston University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 21462132
- Type
- D - Journal article
- DOI
-
10.1016/j.ipm.2015.01.005
- Title of journal
- Information Processing and 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
- March
- Year of publication
- 2015
- 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)
-
A - Aston Institute of Urban Technology and the Environment (ASTUTE)
- Citation count
- 162
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The paper proposed the first approach for performing semantic sentiment analysis on Twitter. The paper won the "Honourable Mention for Best Paper" award in the Information Processing & 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
- -
