Casey, E., 2019. The chequered past and risky future of digital forensics. Aust. J. Forensic Publishers Inc., Hanover, MA, USA. Sci. 51, 1-16. https://doi.org/10.1080/00450618.2018.1554090. Chatfield, C., 2004. The Analysis of Time Series: An Introduction. CRC Press, Florida, United States. Covington, M., McFall, J., 2010. Cutting the Gordian knot: the moving-average type token ratio (MATTR). J. Quant. Linguist. 17, 94-100. https://doi.org/10.1080/ 09296171003643098. Coyac-Torres, J.E., Sidorov, G., Aguirre-Anaya, E., Hernández-Oregón, G., 2023. CyberSci. Int. 32, 200905. https://doi.org/10.1016/j.fsidi.2020.200905. attack detection in social network messages based on convolutional neural networks and NLP techniques. Mach. Learn. Knowl. Extr. 5, 1132-1148. https://doi.org/10. Fergadiotis, G., Wright, H., West, T., 2013. Measuring lexical diversity in narrative dis course of people with aphasia. Am. J. Speech-Lang. Pathol. 22, S397-S408. https:// doi.org/10.1044/1058-0360(2013/12-0083). Harris, M., Levene, M., 2021. SamtlaAPI: free your data. Online. http://www.samtla. com/. (Accessed 15 April 2024). Harris, M., Levene, M., Mudinas, A., 2024. Time series analysis of sentiment: a comparison of the US and UK coronavirus subreddits. Int. J. Inf. Technol. Decis. Mak. 23, 57-88. https://doi.org/10.1142/S0219622023400035. Holt, T., Bossler, A., Seigfried-Spellar, K., 2015. Cybercrime and digital forensics: an in troduction. https://doi.org/10.4324/9781315296975. Hussein, D.D., 2018. A survey on sentiment analysis challenges. J. King Saud Univ., Eng. Sci. 30, 330-338. https://doi.org/10.1016/j.jksues.2016.04.002. https://www. sciencedirect.com/science/article/pii/S1018363916300071. Krishnan, S., Shashidhar, N., Varol, C., Islam, A., 2022. Sentiment analysis of case suspects in digital forensics and legal analytics. Int. J. Secur. 13. Lafferty, J.D., McCallum, A., Pereira, F.C.N., 2001. Conditional random fields: proba bilistic models for segmenting and labeling sequence data. In: Proceedings of the Eighteenth International Conference on Machine Learning. Morgan Kaufmann Pub lishers Inc., San Francisco, CA, USA, pp. 282-289. http://dl.acm.org/citation.cfm? id=645530.655813. Liu, B., 2015. Sentiment Analysis: Mining Opinions, Sentiments, and Emotions. Cambridge University Press. Mudinas, A., Zhang, D., Levene, M., 2012. Combining lexicon and learning based ap proaches for concept-level sentiment analysis. In: Proceedings of the First Interna tional Workshop on Issues of Sentiment Discovery and Opinion Mining (WISDOM ‘12), vol. 5. Association for Computing Machinery, New York, NY, USA, pp. 1-8. Mudinas, A., Zhang, D., Levene, M., 2018. Bootstrap domain-specific sentiment classifiers from unlabeled corpora. Trans. Assoc. Comput. Linguist. 6, 269-285. https://doi.org/ 10.1162/tacl_a_00020. Nadeau, D., Sekine, S., 2007. A survey of named entity recognition and classification. Lingvist. Investig. 30, 3-26. 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. Sutton, C., McCallum, A., 2012. An Introduction to Conditional Random Fields. Now Torruella, J., Capsada, R., 2013. Lexical statistics and tipological structures: a mea sure of lexical richness. In: Selected Papers from the 5th International Conference on Corpus Linguistics (CILC2013). Proc., Soc. Behav. Sci. 95, 447-454. https:// doi.org/10.1016/j.sbspro.2013.10.668. Tully, G., Cohen, N., Compton, D., Davies, G., Isbell, R., Watson, T., 2020. Quality stan dards for digital forensics: learning from experience in England and Wales. Forensic