Abstract
Humans tend to use specific words to express their emotional states in written and oral communications. Scientists in the area of text mining and natural language processing have studied sentiment fingerprints residing in text to extract the emotional polarity of customers for a product or to evaluate the popularity of politicians. Recent research focused on micro-blogging has found notable similarities between Twitter feeds and SMS (short message service) text messages. This paper investigates the common characteristics of both formats for sentiment analysis purposes and verifies the correctness of the similarity assumption. A lexicon-based approach is used to extract and compute the sentiment scores of SMS messages found on smartphones. The data is presented along a timeline that depicts a sender’s emotional fingerprint. This form of analysis and visualization can enrich a forensic investigation by conveying potential psychological patterns from text messages.
Chapter PDF
Similar content being viewed by others
References
T. Almeida, J. Hidalgo and A. Yamakami, Contributions to the study of SMS spam filtering: New collection and results, Proceedings of the Eleventh ACM Symposium on Document Engineering, pp. 259–262, 2011.
A. Bermingham and A. Smeaton, Classifying sentiment in microblogs: Is brevity an advantage? Proceedings of the Nineteenth ACM International Conference on Information and Knowledge Management, pp. 1833–1836, 2010.
M. Choudhury, R. Saraf, V. Jain, A. Mukherjee, S. Sarkar and A. Basu, Investigation and modeling of the structure of texting language, International Journal of Document Analysis and Recognition, vol. 10(3-4), pp. 157–174, 2007.
S. Das and M. Chen, Yahoo! for Amazon: Sentiment extraction from small talk on the web, Management Science, vol. 53(9), pp. 1375–1388, 2007.
X. Ding, B. Liu and P. Yu, A holistic lexicon-based approach to opinion mining, Proceedings of the International Conference on Web Search and Web Data Mining, pp. 231–240, 2008.
D. Estival, T. Gaustad, S. Pham, W. Radford and B. Hutchinson, Author profiling for English emails, Proceedings of the Tenth Conference of the Pacific Association for Computational Linguistics, pp. 263–272, 2007.
A. Go, R. Bhayani and L. Huang, Twitter Sentiment Classification using Distant Supervision, CS224N Final Project Report, Department of Computer Science, Stanford University, Stanford, California, 2009.
L. Hansen, A. Arvidsson, F. Nielsen, E. Colleoni and M. Etter, Good friends, bad news-affect and virality in Twitter, in Future Information Technology, J. Park, L. Yang and C. Lee (Eds), Springer, Berlin-Heidelberg, Germany, pp. 34–43, 2011.
G. Laboreiro, L. Sarmento, J. Teixeira and E. Oliveira, Tokenizing micro-blogging messages using a text classification approach, Proceedings of the Fourth Workshop on Analytics for Noisy Unstructured Text Data, pp. 81–88, 2010.
C. Leong, Y. Lee and W. Mak, Mining sentiments in SMS texts for teaching evaluation, Expert Systems with Applications, vol. 39(3), pp. 2584–2589, 2012.
N. Li and D. Wu, Using text mining and sentiment analysis for online forums hotspot detection and forecast, Decision Support Systems, vol. 48(2), pp. 354–368, 2010.
P. Melville, W. Gryc and R. Lawrence, Sentiment analysis of blogs by combining lexical knowledge with text classification, Proceedings of the Fifteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1275–1284, 2009.
S. Mohammad, S. Kiritchenko and X. Zhu, NRC-Canada: Building the state-of-the-art in sentiment analysis of tweets, Proceedings of the Seventh International Workshop on Semantic Evaluation Exercises, 2013.
A. Pak and P. Paroubek, Twitter as a corpus for sentiment analysis and opinion mining, Proceedings of the Seventh International Conference on Language Resources and Evaluation, pp. 1320–1326, 2010.
B. Pang and L. Lee, Opinion mining and sentiment analysis, Foundations and Trends in Information Retrieval, vol. 2(1-2), pp. 1–135, 2008.
B. Pang, L. Lee and S. Vaithyanathan, Thumbs up? Sentiment classification using machine learning techniques, Proceedings of the Association for Computational Linguistics Conference on Empirical Methods in Natural Language Processing, vol. 10, pp. 79–86, 2002.
M. Porter, An algorithm for suffix stripping, Program: Electronic Library and Information Systems, vol. 14(3), pp. 130–137, 1980.
C. Strapparava and R. Mihalcea, Semeval-2007 Task 14: Affective text, Proceedings of the Fourth International Workshop on Semantic Evaluations, pp. 70–74, 2007.
M. Taboada, J. Brooke, M. Tofiloski, K. Voll and M. Stede, Lexicon-based methods for sentiment analysis, Computational Linguistics, vol. 37(2), pp. 267–307, 2011.
H. Yu and V. Hatzivassiloglou, Towards answering opinion questions: Separating facts from opinions and identifying the polarity of opinion sentences, Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 129–136, 2013.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 IFIP International Federation for Information Processing
About this paper
Cite this paper
Andriotis, P., Takasu, A., Tryfonas, T. (2014). Smartphone Message Sentiment Analysis. In: Peterson, G., Shenoi, S. (eds) Advances in Digital Forensics X. DigitalForensics 2014. IFIP Advances in Information and Communication Technology, vol 433. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44952-3_17
Download citation
DOI: https://doi.org/10.1007/978-3-662-44952-3_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-44951-6
Online ISBN: 978-3-662-44952-3
eBook Packages: Computer ScienceComputer Science (R0)