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Sentiment Analysis of Case Suspects In Digital Forensics and Legal Analytics
Sundar Krishnan, Narasimha Shashidhar, Cihan Varol, ABM Rezbaul Islam
Pages - 1 - 15     |    Revised - 30-06-2022     |    Published - 01-08-2022
Volume - 13   Issue - 1    |    Publication Date - August 2022  Table of Contents
Sentiment Analysis, Suspect Profiling, Legal Case Evidence, Electronic Stored Information, eDiscovery, Digital Forensic Analytics, Digital Forensics, Supervised Learning, Unsupervised learning, Neural Networks, Legal Analytics, Machine Learning, Natural Language Processing.
Sentiments of suspects in a legal case or digital forensic investigation can be of use when profiling their state of mind or feelings. Such information can help case investigators to plot their actions against case timelines, understand their instincts and build psychological profiles. In this research, the authors first assemble a fictional dataset of electronic evidence and store in a SQL Database. Next, they leverage different analytical techniques, automation, and Natural Language Processing (NLP) to propose an approach to plot sentiments of case. This information is then presented via a custom software for case investigators who can use it to pick a suspect from the case and obtain their sentiments against various electronic evidence sources within the case load that they were associated with.
Agarwal, R. (2018). Twitter hate speech. Retrieved February 14, 2021, from https://www.kaggle.com/vkrahul/twitter-hate-speech.
Al-Rowaily, K., Abulaish, M., Al-Hasan Haldar, N., & Al-Rubaian, M. (2015). BiSAL - A bilingual sentiment analysis lexicon to analyze Dark Web forums for cyber security. Digital Investigation, 14, 53-62. https://doi.org/10.1016/J.DIIN.2015.07.006.
Bencina, J. (2017). Facebook News Scraper. Retrieved February 14, 2021, from https://github.com/jbencina/facebook-news.
Bogawar, P. S., & Bhoyar, K. K. (2016). Soft computing approaches to classification of emails for sentiment analysis. ACM International Conference Proceeding Series, 25-26-Augu. https://doi.org/10.1145/2980258.2980304.
Budiman, K., Zaatsiyah, N., Niswah, U., & Faizi, F. M. N. (2020). Analysis of Sexual Harassment Tweet Sentiment on Twitter in Indonesia using Naive Bayes Method through National Institute of Standard and Technology Digital Forensic Acquisition Approach. Journal of Advances in Information Systems and Technology, 2(2), 21-30. Retrieved from https://journal.unnes.ac.id/sju/index.php/jaist/article/view/44305.
Casepoint. (2020). Case Study: How a Multi-Billion Dollar Corporation Reaped Major Cost Savings in an Internal Investigation Leveraging Casepoint Case Study: How a Multi-Billion Dollar Corporation Reaped Major Cost Savings in an Internal Investigation Leveraging Casepoint C.
Cheng, N., Chandramouli, R., & Subbalakshmi, K. P. (2011). Author gender identification from text. Digital Investigation, 8(1), 78-88. https://doi.org/10.1016/J.DIIN.2011.04.002.
Dictionary.com. (n.d.). Sentiment Definition & Meaning | Dictionary.com. Retrieved August 3, 2021, from https://www.dictionary.com/browse/sentiment.
Esuli, A., & Sebastiani, F. (2006). SENTIWORDNET: A Publicly Available Lexical Resource for Opinion Mining. In Proceedings of the Fifth International Conference on Language Resources and Evaluation. European Language Resources Association (ELRA). Retrieved from http://www.lrec-conf.org/proceedings/lrec2006/pdf/384_pdf.
First GOP Debate Twitter Sentiment. (2018). Retrieved February 14, 2021, from https://www.kaggle.com/crowdflower/first-gop-debate-twitter-sentiment.
How AI is transforming eDiscovery industry | Casepoint. (n.d.). Retrieved October 24, 2021, from https://www.casepoint.com/blog/ai-transforming-ediscovery/.
How Artificial Intelligence Saves Money on eDiscovery | IDISCOVER Global. (2018). Retrieved August 5, 2021, from https://idiscoverglobal.com/how-artificial-intelligence-saves-money-on-ediscovery/.
How Legal Teams Can Leverage Technology to Minimize eDiscovery Costs. (2018). Retrieved August 5, 2021, from https://www.jndla.com/blog/how-legal-teams-can-leverage-technology-minimize-ediscovery-costs.
Hussain, S. M., Kanakam, P., Suryanarayana, D., & Gupta, S. (2017). Forensics Data Analysis for Behavioral Pattern with Cognitive Predictive Task. Communications in Computer and Information Science, 827, 549-557. https://doi.org/10.1007/978-981-10-8657-1_41.
Hutto, C., & Gilbert, E. (2014). VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text. Proceedings of the International AAAI Conference on Web and Social Media, 8(1), 216-225. Retrieved from https://ojs.aaai.org/index.php/ICWSM/article/view/14550.
Khdr, A. J., & Varol, C. (2019). Age and Gender Identification by SMS Text Messages. 2018 International Conference on Artificial Intelligence and Data Processing, IDAP 2018. https://doi.org/10.1109/IDAP.2018.8620780.
Kocsis, R. N. (2006). What Is Criminal Profiling? In Criminal Profiling (pp. 1-11). Humana Press. https://doi.org/10.1007/978-1-59745-109-3_1.
Krishnan, S. (2019). eDiscovery Challenges in Healthcare. International Journal of Information Security Science, 8(2), 30-43.
Krishnan, S. (n.d.). Project • GitHub. Retrieved May 6, 2022, from https://github.com/kshsus.
Krishnan, S., & Shashidhar, N. (2021). Interplay of Digital Forensics in eDiscovery. International Journal of Computer Science and Security (IJCSS), 15(2), 19. Retrieved from https://www.cscjournals.org/library/manuscriptinfo.php?mc=IJCSS-1602.
Mcguire, J. C., & Leung, W. S. (2018). Enhancing Digital Forensic Investigations Into Emails Through Sentiment Analysis. In European Conference on Cyber Warfare and Security;
Merriam-Webster. (n.d.). Definition of Sentiment by Merriam-Webster. Retrieved August 3, 2021, from https://www.merriam-webster.com/dictionary/sentiment#synonym-discussion.
Orebaugh, A., & Allnutt, D. J. (2009). Data Mining Instant Messaging Communications to Perform Author Identification for Cybercrime Investigations. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, 31 LNICST, 99-110. https://doi.org/10.1007/978-3-642-11534-9_10.
Pennington, J., Socher, R., & Manning, C. (2014). {G}lo{V}e: Global Vectors for Word Representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing ({EMNLP}) (pp. 1532-1543). Doha, Qatar: Association for Computational Linguistics. https://doi.org/10.3115/v1/D14-1162.
Pstxy: Outlook .pst and .ost file reader for .Net. (n.d.). Retrieved February 14, 2021, from https://github.com/pantilesoft/pantilesoft.github.io.
Rafi, M. S. (2008). SMS Text Analysis: Language, Gender and Current Practices. SSRN Electronic Journal. https://doi.org/10.2139/SSRN.3827793.
Ron J, R. J. (n.d.). The Use Of Artificial Intelligence In Digital Forensics. Retrieved April 27, 2022, from https://www.exterro.com/blog/the-use-of-artificial-intelligence-in-digital-forensics.
Salah, R., & Gayar, N. El. (2019). Sentiment Analysis using Unlabeled Email data. In International Conference on Computational Intelligence and Knowledge Economy (ICCIKE). EasyChair. Retrieved from https://ieeexplore.ieee.org/abstract/document/9004372.
Sentiment analysis - Wikipedia. (n.d.). Retrieved August 3, 2021, from https://en.wikipedia.org/wiki/Sentiment_analysis.
Shalini, K., Ravikurnar, A., Vineetha, R. C., Aravinda, R. D., Annd, K. M., & Soman, K. P. (2018). Sentiment Analysis of Indian Languages using Convolutional Neural Networks. 2018 International Conference on Computer Communication and Informatics, ICCCI 2018. https://doi.org/10.1109/ICCCI.2018.8441371.
Shiha, M. O., & Ayvaz, S. (n.d.). The Effects of Emoji in Sentiment Analysis. https://doi.org/10.17706/ijcee.2017.9.1.360-369.
Sivasangari, V., Mohan, A. K., Suthendran, K., & Sethumadhavan, M. (2018). Isolating Rumors Using Sentiment Analysis. Journal of Cyber Security and Mobility, 7(1), 181-200-181-200. https://doi.org/10.13052/2245-1439.7113.
Stachl, C., Au, Q., Schoedel, R., Gosling, S. D., Harari, G. M., Buschek, D., … Bühner, M. (2020). Predicting personality from patterns of behavior collected with smartphones. Proceedings of the National Academy of Sciences, 117(30), 17680-17687. https://doi.org/10.1073/PNAS.1920484117.
Studiawan, H., Sohel, F., & Payne, C. (2020). Sentiment Analysis in a Forensic Timeline with Deep Learning. IEEE Access, 8, 60664-60675. https://doi.org/10.1109/ACCESS.2020.2983435.
Technologies, Z. (n.d.-a). EDRM and ZL Launch New Enron Email Data Set - ZL Tech. Retrieved October 25, 2021, from https://www.zlti.com/press-releases/edrm-and-zl-launch-new-enron-email-data-set/.
TextBlob: Simplified Text Processing - TextBlob 0.16.0 documentation. (n.d.). Retrieved October 10, 2021, from https://textblob.readthedocs.io/en/dev/.
Twitter US Airline Sentiment. (2019). Retrieved February 14, 2021, from https://www.kaggle.com/crowdflower/twitter-airline-sentiment/activity.
UCI Machine Learning Repository: SMS Spam Collection Data Set. (n.d.). Retrieved October 25, 2021, from https://archive.ics.uci.edu/ml/datasets/SMS+Spam+Collection#.
Uses of AI in ediscovery | EDRM. (n.d.). Retrieved October 24, 2021, from https://edrm.net/wiki/3-uses-of-ai-in-ediscovery/.
Wang, Y., Kim, K., Lee, B., & Youn, H. Y. (2018). Word clustering based on POS feature for efficient twitter sentiment analysis. Human-Centric Computing and Information Sciences 2018 8:1, 8(1), 1-25. https://doi.org/10.1186/S13673-018-0140-Y.
WordNet | A Lexical Database for English. (2010). Retrieved October 10, 2021, from https://wordnet.princeton.edu/.
Zhang, K., Cheng, Y., Xie, Y., Honbo, D., Agrawal, A., Palsetia, D., … Choudhary, A. (2011). SES: Sentiment elicitation system for social media data. In Proceedings - IEEE International Conference on Data Mining, ICDM (pp. 129-136). https://doi.org/10.1109/ICDMW.2011.153.
Zhang, L., Hua, K., Wang, H., Qian, G., & Zheng, L. (2014). Sentiment Analysis on Reviews of Mobile Users. Procedia Computer Science, 34, 458-465. https://doi.org/10.1016/J.PROCS.2014.07.013.
Mr. Sundar Krishnan
Department of Computer Science, Sam Houston State University, Huntsville, TX - United States of America
Associate Professor Narasimha Shashidhar
Department of Computer Science, Sam Houston State University, Huntsville, TX - United States of America
Professor Cihan Varol
Department of Computer Science, Sam Houston State University, Huntsville, TX - United States of America
Dr. ABM Rezbaul Islam
Department of Computer Science, Sam Houston State University, Huntsville, TX - United States of America