Sentiment Analysis: Comparing the use of tools and the human analysis
Abstract
Sentiment Analysis emerged from the need to treat and evaluate texts, opinions and comments made by users on the Internet, in order to understand how they relate to a given entity. Several analytical methods have been developed in an attempt to better translate the uncertain and the subjectivity of human feelings. This research used data from interactions in a social network to identify the sentiments involved in each student comment. From the collected text different methods of sentiment analysis were used in order to identify which method had better results compared to the real ones. The comparison showed differences between these results and the real ones: while the used tools classified more than 40% of the comments as neutral, the analysis of the messages' author showed that 71% of the comments were positive. In the classification, the occurrence of outliers was identified as well as differences on the intensity of the sentiments acquired with each method. No approach, including the one performed by one of the researchers, was considered efficient enough as the highest level of accuracy obtained was less than 70%.
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Published In
May 2016
615 pages
ISBN:9788576693178
- General Chairs:
- Frank Siqueira,
- Patricia Vilain,
- Program Chairs:
- Claudia Cappelli,
- Raul Sidnei Wazlawick
Sponsors
- FAPESC: Santa Catarina State Research and Innovation Support Foundation
- FAPEU: Foundation for the Support of University Research and Outreach
- CAPES: Brazilian Higher Education Funding Council
- CNPq: National Council for Technological and Scientific Development
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- SIGAPP: ACM Special Interest Group on Applied Computing
- SIGMIS: ACM Special Interest Group on Management Information Systems
- AIS: Assoc for Information Systems
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Brazilian Computer Society
Porto Alegre, Brazil
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Published: 17 May 2016
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SBSI '16
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- FAPEU
- CAPES
- CNPq
SBSI '16: Brazilian Symposium on Information Systems
May 17 - 20, 2016
Santa Catarina, Florianopolis, Brazil
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SBSI '16 Paper Acceptance Rate 80 of 244 submissions, 33%;
Overall Acceptance Rate 181 of 557 submissions, 32%
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