Hidden Sentiment Behind Letter Repetition in Online Reviews

Irina Pak, Phoey Lee Teh, Yu-N Cheah


Minimal research has been done on how letter repetition affects readers’ perception of expressed sentiment within a text. To the best of the researchers’ knowledge, no studies have tested samples of text with letter repetition using sentiment tools. The main aim of this paper is to investigate whether letter repetition in product reviews are perceived to have any sentiment value, based on ratings by individual participants and analyses using sentiment tools. This study collected and analysed 1,041 consumer reviews in the form of online comments using the UCREL Wmatrix system, and simulated emotional words within the comments to contain repeated letters. A group of 500 participants rated 15 positive comments and 15 negative comments and their respective simulated counterparts, while 32 sentiment tools are used to analyse a pair of positive comment and its simulated counterpart and a pair of negative comment and its simulated counterpart. Results indicate that readers perceive letter repetition to amplify a comment’s sentiment value, in which the effect was found more strongly in negative comments than positive comments. On the other hand, analyses using sentiment tools show that a majority of these tools are unable to detect letter repetition within a word and instead, treats the word as a spelling mistake. As consumers or online users, in general, have been found to use letter repetition to intensify and express their sentiments in their comments, this study’s findings suggest that letter repetition processing in any text-based mechanism needs to be enhanced. The outcome of this paper is useful for improving the measurement of sentiment analysis for the use of marketing applications.


Computer-Mediated Communication (CMC); Letter Repetition; Online Reviews; Product Reviews; Sentiment Tools; Text-Based Cue;

Full Text:



P. L. Teh, I. Pak, P. Rayson, and S. Piao, “Exploring fine-grained sentiment values in online product reviews,” in 2015 IEEE Confernece on Open Systems (ICOS), 2015, pp. 114–118.

J. Carey, “Paralanguage in computer mediated communication,” ACL ’80 Proc. 18th Annu. Meet. Assoc. Comput. Linguist., no. 54, pp. 67– 69, 1980.

B. Smith, “The use of communication strategies in computer-mediated communication,” System, vol. 31, no. 1, pp. 29–53, 2003.

E. Darics, “Non-verbal signalling in digital discourse: The case of letter repetition,” Discourse, Context Media, vol. 2, no. 3, pp. 141–148, 2013.

Y. M. Kalman and D. Gergle, “Letter repetitions in computer-mediated communication: A unique link between spoken and online language,” Comput. Human Behav., vol. 34, pp. 187–193, 2014.

Y. M. Kalman and D. Gergle, “Letter and Punctuation Mark Repeats as Cues in Computer-Mediated Communication,” in 95th annual meeting of the National Communication Association in Chicago, 2009.

Y. M. Kalman and D. Gergle, “CMC Cues Enrich Lean Online Communication: The Case of Letter and Punctuation Mark Repetitions,” MCIS 2010 Proc., p. 45, 2010.

I. Pak and P. L. Teh, “Machine Learning Classifiers : Evaluation of the Performance in Online Reviews,” Indian J. Sci. Technol., vol. 9, no. 45, pp. 1–9, 2016.

P. L. Teh, P. Rayson, I. Pak, S. Piao, and M. Y. Seow, “Reversing the Polarity with Emoticons,” in Natural Language Processing and Information Systems, Switzerland: Springer International Publishing Switzerland, 2016, pp. 453–458.

P. L. Teh, P. Rayson, S. Piao, and I. Pak, “Sentiment Analysis Tools Should Take Account of the Number of Exclamation Marks !!!,” in The 17th International Conference on Information Integration and Webbased Applications & Services, 2015, p. Artcile 35.

P. Rayson, “From key words to key semantic domains,” Int. J. Corpus Linguist., vol. 13, no. 4, pp. 519–549, 2008.

M. Thelwall, K. Buckley, G. Paltoglou, and D. Cai, “Sentiment Strength Detection in Short Informal Text,” Am. Soc. Informational Sci. Technol., vol. 61, no. 12, pp. 2544–2558, 2010.

M. Thelwall, “TensiStrength: Stress and relaxation magnitude detection for social media texts,” Inf. Process. Manag., vol. 0, pp. 1– 16, 2016.

V. Narayanan, I. Arora, and a Bhatia, “Fast and accurate sentiment classification using an enhanced Naive Bayes model,” Int. Data Eng. Autom. Learn. Lect. Notes Comput. Sci., vol. 8206, pp. 194–201, 2013.

P. Riefler, A. Diamantopoulos, and J. A. Siguaw, “Cosmopolitan consumers as a target group for segmentation,” J. Int. Bus. Stud., vol. 43, no. 3, pp. 285–305, 2012.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.

ISSN: 2180-1843

eISSN: 2289-8131