Tower of babel: a crowdsourcing game building sentiment lexicons for resource-scarce languages
With the growing amount of textual data produced by online social media today, the demands for sentiment analysis are also rapidly increasing; and, this is true for worldwide. However, non-English languages often lack sentiment lexicons, a core resource in performing sentiment analysis. Our solution...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | text |
| Language: | English |
| Published: |
Institutional Knowledge at Singapore Management University
2013
|
| Subjects: | |
| Online Access: | https://ink.library.smu.edu.sg/sis_research/6096 https://ink.library.smu.edu.sg/context/sis_research/article/7099/viewcontent/Tower_of_Babel.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Singapore Management University |
| Language: | English |
| Summary: | With the growing amount of textual data produced by online social media today, the demands for sentiment analysis are also rapidly increasing; and, this is true for worldwide. However, non-English languages often lack sentiment lexicons, a core resource in performing sentiment analysis. Our solution, Tower of Babel (ToB), is a language-independent sentiment-lexicon-generating crowdsourcing game. We conducted an experiment with 135 participants to explore the difference between our solution and a conventional manual annotation method. We evaluated ToB in terms of effectiveness, efficiency, and satisfactions. Based on the result of the evaluation, we conclude that sentiment classification via ToB is accurate, productive and enjoyable. |
|---|