Multilingual Sentiment Analysis for a Swiss Gig
Loading...
Author (Corporation)
Publication date
27.08.2018
Typ of student thesis
Course of study
Collections
Type
04B - Conference paper
Editors
Wong, Ka Chun
Editor (Corporation)
Supervisor
Parent work
6th International Symposium on Computational and Business Intelligence (ISCBI 2018)
Special issue
DOI of the original publication
Link
Series
Series number
Volume
Issue / Number
Pages / Duration
Patent number
Publisher / Publishing institution
Place of publication / Event location
Basel
Edition
Version
Programming language
Assignee
Practice partner / Client
Abstract
We are developing a multilingual sentiment analysis solution for a Swiss human resource company working in the gig sector. To examine the feasibility of using machine learning in this context, we carried out three sentiment assignment experiments. As test data we use 963 hand annotated comments made by workers and their employers. Our baseline, machine learning (ML) on Twitter, had an accuracy of 0.77 with the Matthews correlation coefficient (MCC) of 0.32. A hybrid solution, Semantria from Lexalytics, had an accuracy of 0.8 with MCC of 0.42, while a tenfold cross-validation on the gig data yielded the accuracy of 0.87, F1 score 0.91, and MCC 0.65. Our solution did not require language assignment or stemming and used standard ML software. This shows that with more training data and some feature engineering, an industrial strength solution to this problem should be possible.
Keywords
sentiment analysis, machine learning application, natural language processing, gig economy
Subject (DDC)
Event
6th International Symposium on Computational and Business Intelligence (ISCBI 2018)
Exhibition start date
Exhibition end date
Conference start date
27.08.2018
Conference end date
29.08.2018
Date of the last check
ISBN
ISSN
Language
English
Created during FHNW affiliation
Yes
Strategic action fields FHNW
Publication status
Published
Review
Peer review of the complete publication
Open access category
License
Citation
Pustulka, E., Hanne, T., Blumer, E., & Frieder, M. (2018). Multilingual Sentiment Analysis for a Swiss Gig. In K. C. Wong (Ed.), 6th International Symposium on Computational and Business Intelligence (ISCBI 2018). https://doi.org/10.1109/iscbi.2018.00028