Methods for Building Successful chatbot Communication in the Discourse of Sales in the Field of Digital Goods (Mobile Phones) on the Example of English and Russian Language Materials

Introduction. Digitalization processes have been actively penetrating the life of a modern person in the last decade. Artificial intelligence in various forms and formats creates new linguistic knowledge about the communication process. By creating new features and rules of speech interaction in various types of network discourse, the problems of achieving the success of speech acts built through chatbots remain ineradicable. This problem is especially acute in the field of advertising and PR, where communication with target auditors and target groups of the public is one of the most important tools for achieving the company's goals.

Methodology and sources. A preliminary assessment of the effectiveness and potential of chatbot communication necessitates this. Using the method of linguistic modeling, you can create conditions and prescribe certain “rules” for successful interaction between a person and a chatbot. To create models for the Russian-speaking and English-speaking spheres, it is necessary to conduct a frame analysis and construct concepts of concepts that dominate in advertising discourse, or rather its variety: the discourse of sales in the field of digital goods (cell phones). To do this, it is necessary to conduct a corpus analysis of texts: the texts of oral and written speech in the corpus collected independently will be analyzed, and the results of the sample in the NOW corpora (in English-corpora) and NCRL will be analyzed. Also, for the compilation of models, communication and conversion analyzes will be required.

Results and discussion. As a result of the study, the article presents not only possible communication models that function in the discourse of sales in the field of digital goods (cell phones), as well as leading the greatest number of speech contacts to success, but also a universal algorithm for parsing chatbot communication in other discourses. In the course of the study, it was possible to obtain confirmation of the assumption of a significant difference between the English-language and Russian-language models of achieving speech success in chatbot communication.

Conclusion. Preparation of a communication model updated from the point of view of a certain discourse and comparison of research data through the materials of two languages will help to identify similarities and differences for each area, and, among other things, will ensure an increase in the efficiency of the communication process built through chatbots in a business environment.

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