P. Andriotis and G. Oikonomou, Messaging activity reconstruction with sentiment polarity identification, in Human Aspects of Information Security, Privacy and Trust, T. Tryfonas and I, pp.475-486, 2015.

P. Andriotis, G. Oikonomou, and T. Tryfonas, Forensic analysis of wireless networking evidence of Android smartphones, 2012 IEEE International Workshop on Information Forensics and Security (WIFS), pp.109-114, 2012.
DOI : 10.1109/WIFS.2012.6412634

P. Andriotis, A. Takasu, and T. Tryfonas, Smartphone message sentiment analysis, in Advances in Digital Forensics, pp.253-265, 2014.

N. Beebe and J. Clark, Digital forensic text string searching: Improving information retrieval effectiveness by thematically clustering search results, Digital Investigation, pp.49-54, 2007.
DOI : 10.1016/j.diin.2007.06.005

S. Bird, NLTK: The Natural Language Toolkit, Proceedings of the Association for Computational Linguistics Workshop on Effective Tools and Methodologies for Teaching Natural Language Processing and Computational Linguistics, pp.63-70, 2002.

E. Cambria, B. Schuller, Y. Xia, and C. Havasi, New Avenues in Opinion Mining and Sentiment Analysis, IEEE Intelligent Systems, vol.28, issue.2, pp.15-21, 2013.
DOI : 10.1109/MIS.2013.30

T. Chen and M. Kan, Creating a live, public short message service corpus: the NUS SMS corpus, Language Resources and Evaluation, vol.2, issue.9.1-2, pp.299-335, 2013.
DOI : 10.1145/1386790.1386829

N. Cheng, R. Chandramouli, and K. Subbalakshmi, Author gender identification from text, Digital Investigation, pp.78-88, 2011.
DOI : 10.1016/j.diin.2011.04.002

O. De-vel, A. Anderson, M. Corney, and G. Mohay, Mining e-mail content for author identification forensics, ACM SIGMOD Record, vol.30, issue.4, pp.55-64, 2001.
DOI : 10.1145/604264.604272

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 , WSDM '08, pp.231-240, 2008.
DOI : 10.1145/1341531.1341561

M. Gamon, Sentiment classification on customer feedback data, Proceedings of the 20th international conference on Computational Linguistics , COLING '04, pp.841-847, 2004.
DOI : 10.3115/1220355.1220476

A. Go, R. Bhayani, and L. Huang, Twitter Sentiment Classification using Distant Supervision, CS224N Final Project Report, 2009.

B. Han, P. Cook, and T. Baldwin, Lexical normalization for social media text, ACM Transactions on Intelligent Systems and Technology, vol.4, issue.1, 2013.
DOI : 10.1145/2414425.2414430

F. Iqbal, H. Binsalleeh, B. Fung, and M. Debbabi, Mining writeprints from anonymous e-mails for forensic investigation, Digital Investigation, pp.56-64, 2010.
DOI : 10.1016/j.diin.2010.03.003

X. Jin, Y. Li, T. Mah, and J. Tong, Sensitive webpage classification for content advertising, Proceedings of the 1st international workshop on Data mining and audience intelligence for advertising , ADKDD '07, pp.28-33, 2007.
DOI : 10.1145/1348599.1348604

P. Juola, Authorship attribution, Foundations and Trends in Information Retrieval, pp.233-334, 2006.

C. Kobus, F. Yvon, G. Damnati, and S. Normalizing, Are two metaphors better than one?, Proceedings of the Twenty-Second International Conference on Computational Linguistics, pp.441-448, 2008.

W. Ling, C. Dyer, and A. , Black and I. Trancoso, Paraphrasing 4 microblog normalization, Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp.73-84, 2013.

S. Mohammad, S. Kiritchenko, X. Zhu, and . Nrc-canada, Building the state-of-the-art in sentiment analysis of tweets, Proceedings of the Seventh International Workshop on Semantic Evaluation Exercises, 2013.

O. Owoputi, B. O-'connor, C. Dyer, K. Gimpel, N. Schneider et al., Improved part-of-speech tagging for online conversational text with word clusters, Proceedings of the Conference of the North American Chapter of the Association of Computational Linguistics, pp.380-390, 2013.

B. Pang and L. Lee, Opinion Mining and Sentiment Analysis, Foundations and Trends?? in Information Retrieval, vol.2, issue.1???2, pp.1-135, 2008.
DOI : 10.1561/1500000011

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, pp.79-86, 2002.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion et al., scikit-learn: Machine learning in Python, Journal of Machine Learning Research, vol.12, pp.2825-2830, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905

A. Stolerman, R. Overdorf, S. Afroz, and R. Greenstadt, Breaking the Closed-World Assumption in Stylometric Authorship Attribution, Advances in Digital Forensics X, pp.185-205, 2014.
DOI : 10.1007/978-3-662-44952-3_13
URL : https://hal.archives-ouvertes.fr/hal-01393771

J. Suttles and N. Ide, Distant Supervision for Emotion Classification with Discrete Binary Values, Proceedings of the Fourteenth International Conference on Computational Linguistics and Intelligent Text Processing, pp.121-136, 2013.
DOI : 10.1007/978-3-642-37256-8_11
URL : http://www.cs.vassar.edu/~ide/papers/cicling13.pdf

P. Turney, Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews, Proceedings of the Fortieth Annual Meeting of the Association for Computational Linguistics, pp.417-424, 2002.

L. Venkata-subramaniam, S. Roy, T. Faruquie, and S. Negi, A survey of types of text noise and techniques to handle noisy text, Proceedings of the Third Workshop on Analytics for Noisy Unstructured Text Data, pp.115-122, 2009.

H. Wang, D. Can, A. Kazemzadeh, F. Bar, and S. Narayanan, A system for real-time Twitter sentiment analysis of the 2012 U.S. presidential election cycle, Proceedings of the Association for Computational Linguistics 2012 System Demonstrations, pp.115-120, 2012.