This study conducts a systematic comparative critical discourse analysis of news reports from prominent American (CNN) and Russian (RT) media sources covering the Russia-Ukraine conflict. Utilizing the theoretical frameworks of Norman Fairclough's multidimensional model and Teun van Dijk's socio-cognitive approach, the research examines the underlying ideological assumptions and discursive strategies employed by the two contrasting news channels. Quantitative analysis of discursive techniques and linguistic features provides insights into how each channel selectively utilizes language to convey distinct ideological positions. The findings demonstrate how media discourse constructs and normalizes particular ideological representations of political conflicts. Detailed linguistic analysis reveals that RT reports largely reflect pro-Russian ideological stances, legitimizing Russian actions through authorization of official sources, while portraying the West as an antagonist through metaphorical framing. Conversely, CNN coverage predominantly espouses pro-Western ideological views supporting Ukraine, manifested in more frequent delegitimization of alternative perspectives, nominalization to obscure agency of Western actors, and use of synonyms framing conservative criticism negatively. Thus, despite applying similar overarching discursive methods, each channel subtly employs rhetorical, grammatical and lexical strategies to convey ideologicallyoriented representations aligned with particular geopolitical interests. This research contributes to understanding how media discourse can implicitly embed certain worldviews and underlying belief systems through strategic linguistic andPage 22 of 32 rhetorical devices. Critical discourse analysis of media texts is crucial for identifying hidden mechanisms of persuasion and manipulation within news narratives
Sentiment analysis is one of the major fields in natural language processing whose main task is to extract sentiments, opinions, attitudes, and emotions from a subjective text. And for its importance in decision making and in people's trust with reviews on web sites, there are many academic researches to address sentiment analysis problems. Deep Learning (DL) is a powerful Machine Learning (ML) technique that has emerged with its ability of feature representation and differentiating data, leading to state-of-the-art prediction results. In recent years, DL has been widely used in sentiment analysis, however, there is scarce in its implementation in the Arabic language field. Most of the previous researches address other l
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Coaches and analysts face a significant challenge of inaccurate estimation when analyzing Men's 100 Meter Sprint Performance, particularly when there is limited data available. This necessitates the use of modern technologies to address the problem of inaccurate estimation. Unfortunately, current methods used to estimate Men's 100 Meter Sprint Performance indexes in Iraq are ineffective, highlighting the need to adopt new and advanced technologies that are fast, accurate, and flexible. Therefore, the objective of this study was to utilize an advanced method known as artificial neural networks to estimate four key indexes: Accelerate First of 10 meters, Speed Rate, Time First of 10 meters, and Reaction Time. The application of artifi
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