The proliferation of internet media has dramatically altered communication patterns and self-expression, with a significant portion of the global population relying on online sources for information. This study investigates the role of emotive language in shaping public opinion and building public relations within this digital landscape. The rise of internet-based media has created new discursive practices, where users interact in real time and engage in debates, fostering public expression and knowledge sharing. However, this ease of access and expression also raises concerns about manipulation and the influence of emotional appeals on readers' understanding and interpretation of news. The research focuses on the emotive vocabulary used in Kazakh internet media and its impact on readers' cognitive perception of media coverage. The researchers aim to understand how different types of online media, focusing on those with varying content (politics, entertainment), utilize emotive language to influence their audiences. This research is significant because it highlights the need to understand how emotional appeals in media can shape public opinion, particularly in the context of the internet's pervasive influence on information consumption and decision-making.

Existing research emphasizes the complex and expressive nature of media texts within the digital world, highlighting the shift in language use facilitated by online platforms. Studies have investigated new forms of communication and self-expression arising from digital media, noting the significance of extra-linguistic elements, particularly emotions and feelings, in conveying meaning. There's also acknowledgment of the diverse linguistic and non-linguistic strategies used in online self-expression, many well-suited to the internet's free-flowing environment. However, previous studies on media discourse often lacked a thorough investigation into the nuanced interplay between different levels of language (phonology, morphology, semantics, syntax) within emotional expressions. The current research addresses this gap by adopting a multi-faceted approach to analyze the emotive vocabulary in Kazakh online media, including an exploration of lexical, semantic, stylistic and syntactic levels. The reviewed literature reveals a need for deeper analyses of the complex interrelations of expressive language components to fully understand meaning construction in online media.

This study employs a contextual approach to analyze emotive vocabulary from the top three Kazakh online media sources: Zakon.kz (politics), Kazinform (news), and Sputnik Kazakhstan (entertainment). These sources were selected based on their high readership and varied content, allowing for a comparison of emotive language use across different topics. The researchers collected a corpus of 23,894 texts, focusing on threads conducive to spontaneous interactions. Data collection involved manual copy-pasting of messages from selected threads between October 2020 and May 2021. The choice to copy-paste and exclude context was due to the study's focus on the emotive vocabulary itself, as opposed to the nuances of context. User anonymity was maintained throughout. The analysis used a mixed approach, combining automated emotive-semantic analysis with manual verification. The Tropes software was utilized for automated analysis, leveraging its ability to eliminate lexical/semantic ambiguity, integrate lexicons and semantic networks, and perform various linguistic analyses. Tropes’ Emotaix scenario was employed to analyze emotional and affective lexicon, enabling the detection of emotional lexical units regardless of literal or figurative meaning and allowing for an understanding of valence shifts within lexical items. The software aided in semantic classification of emotive lexemes and keyword extraction. The quantitative method involved counting emotive units to identify frequently used lexemes and their emotional categories. Qualitative analysis was used to further examine the context surrounding the identified emotive lexemes and categorize topics based on standard emotion classifications.

The analysis revealed that emotions represent a multidimensional category, often expressed through various linguistic representations (nouns, verbs, adjectives, adverbs, phrases, sentences). The study identified a core set of affective vocabulary items frequently used to represent emotions, focusing on their lexical combinations to convey affect or affective evaluation. The research utilized Van Dijk's three-component dimension (cognitive, emotional, and behavioral) to analyze the referential dimension of emotive lexicon. The findings revealed differences in emotional expression across the three media sources. Zakon.kz (politics-oriented) showed a predominance of negative emotions like sadness and fear. Kazinform (world news) displayed a mix of fear and joy. Sputnik Kazakhstan (entertainment) predominantly featured positive emotions, such as joy and hope. The results indicate that the type of media content and its topic significantly influence the emotional tone conveyed through emotive vocabulary. The identified emotive lexemes spanned various parts of speech (nouns, adjectives, verbs, adverbs, phrases). The study further explored basic emotions (joy, sadness, fear, hope) and their complex manifestations, highlighting the multimodality, polyphony, hypertextuality, heterogeneity, and orality that often characterize online emotional expressions. This highlights how diverse expressive processes contribute to message construction and influence the reader.

This study demonstrates the significant role of emotive language in shaping readers' perception and understanding of information presented in online media. The findings highlight the strategic use of emotive vocabulary by different media outlets, tailored to their specific content and target audience. The variation in emotional tone across the examined media sources suggests that content writers employ emotive language to achieve specific communicative goals. The study underscores the need to consider both linguistic and extra-linguistic elements when analyzing online media discourse, as emotive expression goes beyond mere lexical choices. The results contribute to a deeper understanding of how emotions influence the interpretation of media texts and how such influences may differ based on content and reader culture. The findings are relevant to journalism, media studies, and communication research, shedding light on the persuasive power of emotive language and the potential for manipulation in online environments.

This research provides valuable insights into the use of emotive language in Kazakh internet media and its impact on audience perception. The study identifies key emotive lexemes and analyzes their contextual usage, revealing differences across various media platforms. The findings highlight the importance of considering both linguistic and extra-linguistic factors in analyzing online communication and understanding how emotions influence information processing. Future research could investigate the broader application of emotive language across different cultural contexts and further explore the interplay between emotive language, reader engagement, and the potential for online manipulation. This research supports the development of teaching materials in various fields, including philology, psychology, journalism, and semiotics.

The study's limitations include the potential ambiguity of emotive items and the difficulty in definitively determining their impact on reader behavior. The focus on three specific Kazakh media outlets limits the generalizability of the findings to other contexts. The use of a manual copy-paste method for data collection, though effective in this case, is time-consuming and may not be scalable to larger datasets. The analysis, focusing primarily on lexical and syntactic features, might not fully capture the complexity of multimodal expression in online communication.