In the digital age, the significant increase in information generation and processing is accompanied by a growing threat of unauthorized access, illegal distribution, and use. One of the most promising strategies for protecting information from various cyber threats and malicious attacks is the use of Natural Language Processing (NLP) processors. This article focuses on the methodology of data protection in the context of utilizing Natural Language Processing for sentiment analysis and trend detection. Emphasis is placed on the relevance of using NLP to address tasks related to text content analysis for identifying suspicious or dangerous information. The article covers the stages of text data collection and processing, including data gathering from various sources such as social media, news portals, forums, and blogs. Subsequently, preliminary processing is performed, involving noise removal, tokenization, stemming, and lemmatization of the text to prepare the data for further analysis. The application of NLP allows for the identification of keywords, topics, sentiment, and text structure, facilitating categorization and trend identification in public opinion. Additionally, a mathematical model for detecting phishing indicators is presented, along with an example of identifying suspicious text features. It is noted that the use of cryptographic methods can effectively secure processed data, reducing the risk of unauthorized access or misuse. The article provides a detailed description of data protection methods in the process of sentiment analysis using NLP and underscores the necessity of employing cryptographic techniques to ensure the security of processed text data.
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