Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

12-2018

Abstract

The Linguistic Inquiry and Word Count (LIWC) tool is a psycholinguistics tool that has been widely used in both psychology and sociology research, and the LIWC scores derived from user-generated content are known to be good features for personality prediction [1], [2]. LIWC, however, is language specific as it relies on counting the percentage of predefined dictionary words occurring in the content. For content written in English Creoles which are languages based on English, the original English LIWC may not perform optimally due to its lack of words which are only used in the English Creoles. In this paper, we therefore study the learning of LIWC for an English Creole using word embeddings, a way to encode contextual meaning of words in a vector representation. We particularly focus on an English Creole known as Singlish (which is a popular English creole in Singapore and it contains words from non-English languages including Malay, Chinese, Chinese dialects, and Indian languages). Instead of a manual effort to construct LIWC for Singlish, we automate the construction of a Singlish-specific LIWC dictionary, called S-LIWC by learning a word embedding model using a large corpus of Singapore tweets, and extracting new words semantically similar to the LIWC dictionary words. We show that the S-LIWC can be used to predict LIWC summary variables. Moreover, we conduct a personality prediction experiment on Singapore university students using their Facebook status updates. Our results show that our personality prediction method using S-LIWC outperforms that using LIWC for most personality traits. We finally show some interesting case examples of explaining the weaknesses and strength of S-LIWC.

Keywords

Personality prediction, Language-specific psycholinguistics tool

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Software and Cyber-Physical Systems

Publication

2018 IEEE International Conference on Big Data (Big Data): Seattle, December 10-13: Proceedings

First Page

751

Last Page

760

ISBN

9781538650356

Identifier

10.1109/BigData.2018.8622010

Publisher

IEEE

City or Country

Piscataway, NJ

Additional URL

https://doi.org/10.1109/BigData.2018.8622010

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