Messaging Activity Reconstruction with Sentiment Polarity Identification

  • Panagiotis AndriotisEmail author
  • George Oikonomou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9190)


Sentiment Analysis aims to extract information related to the emotional state of the person that produced a text document and also describe the sentiment polarity of the short or long message. This kind of information might be useful to a forensic analyst because it provides indications about the psychological state of the person under investigation at a given time. In this paper we use machine-learning algorithms to classify short texts (SMS), which could be found in the internal memory of a smartphone and extract the mood of the person that sent them. The basic goal of our method is to achieve low False Positive Rates. Moreover, we present two visualization schemes with the intention to provide the ability to digital forensic analysts to see graphical representations of the messaging activity of their suspects and therefore focus on specific areas of interest reducing their workload.


Smartphone Forensics Text-mining Short-text messages 


  1. 1.
    Almeida, T.A., Maria Gomez, J., Yamakami, A.: Contributions to the study of SMS spam filtering: new collection and results. In: DOCENG 2011: Proceedings of the 2011 ACM Symposium on Document Engineering, pp. 259–262, 1515 BROADWAY, New York, NY 10036–9998, USA (2011)Google Scholar
  2. 2.
    Andriotis, P., Oikonomou, G., Tryfonas, T.: Forensic analysis of wireless networking evidence of android smartphones. In: 2012 IEEE International Workshop on Information Forensics and Security (WIFS) pp. 109–114, 345 E 47th St, New York, NY 10017, USA (2012)Google Scholar
  3. 3.
    Andriotis, P., Takasu, A., Tryfonas, T.: Smartphone message sentiment analysis. In: Peterson, G., Shenoi, S. (eds.) Advances in Digital Forensics X. IFIP Advances in Information and Communication Technology, vol. 433, pp. 253–265. Springer, Heidelberg (2014). doi: 10.1007/978-3-662-44952-3_17 Google Scholar
  4. 4.
    Bastian, M., Heymann, S., Jacomy, M.: Gephi: an open source software for exploring and manipulating networks. In: ICWSM, pp. 361–362 (2009)Google Scholar
  5. 5.
    Beebe, N.L., Clark, J.G.: Digital forensic text string searching: improving information retrieval effectiveness by thematically clustering search results. Digit. Invest. 4(1), S49–S54 (2007)CrossRefGoogle Scholar
  6. 6.
    Bermingham, A., Smeaton, A.F.: Classifying sentiment in microblogs: is brevity an advantage? In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, pp. 1833–1836. ACM (2010)Google Scholar
  7. 7.
    Choudhury, M., Saraf, R., Jain, V., Mukherjee, A., Sarkar, S., Basu, A.: Investigation and modeling of the structure of texting language. Int. J. Doc. Anal. Recogn. 10(3–4), 157–174 (2007)CrossRefGoogle Scholar
  8. 8.
    Das, S.R., Chen, M.Y.: Yahoo! for Amazon: sentiment extraction from small talk on the web. Manage. Sci. 53(9), 1375–1388 (2007)CrossRefGoogle Scholar
  9. 9.
    Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240. ACM (2008)Google Scholar
  10. 10.
    Estival, D., Gaustad, T., Pham, S.B., Radford, W., Hutchinson, B.: Author profiling for english emails. In: Proceedings of the 10th Conference of the Pacific Association for Computational Linguistics, pp. 263–272 (2007)Google Scholar
  11. 11.
    Gansner, E., North, S.: An open graph visualization system and its applications to software engineering. Softw.-Pract. Experience 30(11), 1203–1233 (2000)CrossRefzbMATHGoogle Scholar
  12. 12.
    Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. CS224N Proj. R. 657, 1–12 (2009)Google Scholar
  13. 13.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The weka data mining software: an update. ACM SIGKDD Explor. Newsl. 11(1), 10–18 (2009)CrossRefGoogle Scholar
  14. 14.
    Han, B., Cook, P., Baldwin, T.: Lexical normalization for social media text. ACM Trans. Intell. Syst. Tech. 4(1), 5 (2013)CrossRefGoogle Scholar
  15. 15.
    Laboreiro, G., Sarmento, L., Teixeira, J., Oliveira, E.: Tokenizing micro-blogging messages using a text classification approach. In: Proceedings of the Fourth Workshop on Analytics for Noisy Unstructured Text Data, pp. 81–88. ACM (2010)Google Scholar
  16. 16.
    Lovins, J.B.: Development of a Stemming Algorithm. MIT Information Processing Group, Electronic Systems Laboratory, Cambridge (1968) Google Scholar
  17. 17.
    Martinez-Camara, E., Teresa Martin-Valdivia, M., Alfonso Urena-Lopez, L., Montejo-Raez, A.: Sentiment analysis in Twitter. Nat. Lang. Eng. 20(1), 1–28 (2014)CrossRefGoogle Scholar
  18. 18.
    Melville, P., Gryc, W., Lawrence, R.D.: Sentiment analysis of blogs by combining lexical knowledge with text classification. In: KDD-09: 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 1275–1283, 1515 Broadway, New York, NY 10036–9998, USA (2009)Google Scholar
  19. 19.
    Mohammad, S.M., Kiritchenko, S., Zhu, X.: NRC-canada: building the state-of-the-art in sentiment analysis of tweets. In: Proceedings of the Seventh International Workshop on Semantic Evaluation Exercises (SemEval-2013), Atlanta, Georgia, USA, June 2013Google Scholar
  20. 20.
    Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retrieval 2(1–2), 1–135 (2008)CrossRefGoogle Scholar
  21. 21.
    Suttles, J., Ide, N.: Distant supervision for emotion classification with discrete binary values. In: Gelbukh, A. (ed.) CICLing 2013, Part II. LNCS, vol. 7817, pp. 121–136. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  22. 22.
    Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M.: Lexicon-based methods for sentiment analysis. Comput. Linguist. 37(2), 267–307 (2011)CrossRefGoogle Scholar
  23. 23.
    Wang, H., Can, D., Kazemzadeh, A., Bar, F., Narayanan, S.: A system for real-time twitter sentiment analysis of 2012 US presidential election cycle. In: Proceedings of the ACL 2012 System Demonstrations, pp. 115–120. Association for Computational Linguistics (2012)Google Scholar
  24. 24.
    Wang, Z., Zhai, L., Ma, Y., Li, Y.: Analysis of public sentiment based on SMS content. In: Yuan, Y., Wu, X., Lu, Y. (eds.) ISCTCS 2012. CCIS, vol. 320, pp. 637–643. Springer, Heidelberg (2013) CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.University of BristolBristolUK

Personalised recommendations