Textual Variations Affect Human Judgements of Sentiment Values

Lee Teh, Phoey and Rayson, Paul and Pak, Irina and Piao, Scott and Sze Yin Ho, Jessica and Moore, Andrew and Cheah, Yu-N (2022) Textual Variations Affect Human Judgements of Sentiment Values. Electronic Commerce Research and Applications, 53: 101149. ISSN 1567-4223

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Abstract

Electronic word-of-mouth communication in the form of online reviews influences people’s product or service choices. People use text features to add or emphasise feelings and emotions in their text. The text emphasis can come in as capital letters, letter repetition, exclamation marks and emoticons. The existing literature has not paid sufficient attention to the effects of such textual variations on human text interpretation. This paper presents an analysis of text variations that can affect the interpretation of a text. A total of 1,041 online comments were collected, in which seven types of the most used textual variations were identified and simulated for hypothesis testing. Sentiment scores from 500 participants were collected to rate the value expressed for each of the textual variations. Statistical analysis showed that collected ratings are significant for the accurate calculation of sentiment values for short comments. Furthermore, the performance of ten existing sentiment tools was analysed based on seven textual variations. Results indicate that those tools should consider these textual variations to fully reflect a human interpretation on the text variations.

Item Type:

Journal Article

Journal or Publication Title:

Electronic Commerce Research and Applications

Additional Information:

This is the author’s version of a work that was accepted for publication in Electronic Commerce Research and Applications. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Electronic Commerce Research and Applications, 53, 2022 DOI: 10.1016/j.elerap.2022.101149

Uncontrolled Keywords:

/dk/atira/pure/subjectarea/asjc/1400/1406

Subjects:

?? sentiment analysistext analysisclassification algorithmstext miningnlp toolscomputer mediated cues (cmc)punctuationmarketingcomputer networks and communicationsmanagement of technology and innovationcomputer science applications ??

Departments:

Faculty of Science and Technology > School of Computing & Communications

ID Code:

169191

Deposited By:

ep_importer_pure

Deposited On:

21 Apr 2022 09:55

Refereed?:

Yes

Published?:

Published

Last Modified:

03 Jan 2024 00:29

URI:

https://eprints.lancs.ac.uk/id/eprint/169191