Parts of speech

This study was intended first to categorize the L2 learners in terms of their learning style preferences and second to investigate if their learning preferences are related to lexical inferencing. Moreover, strategies used for lexical inferencing and text related issues of text density and parts of speech were studied to determine their moderating effects and the best predictors of lexical inferencing. To this end, a posttest group design with 142 students studying engineering was adopted for the study. Perceptual style preferences questionnaire was administered to identify the students’ major learning styles, followed by strategy training for deriving the meaning of unknown words. Finally, lexical inferencing texts were given to the students to study and extract the meaning of unknown words and concurrently determine the type of strategy used for lexical inferencing. The results indicated that a great proportion of students belonged to the kinesthetic category of styles while the predominant treatments in the class were audio-visually structured. The analysis also revealed that tactile, kinesthetic, and group categories of style preferences are meaningfully related. Moreover, it was found that learning style preferences lead to statistically different lexical ineferncing. As for the strategies, the ‘syntactic knowledge analysis’ showed the highest correlation with ‘auditory learners’. Lexical density and parts of speech were also shown to moderate the effect of perceptual style preferences on lexical ability. On the whole, strategy and perceptual style preferences were found to be the two best predictors of successful lexical inferencing.

As data and context continue to expand, a vast amount of textual content, including books, blogs, and papers, is produced and distributed electronically. Analyzing such large amounts of content manually is a time-consuming task. Automatic detection of feelings and emotions in these texts is crucial, as it helps to identify the emotions conveyed by the author, understand the author's writing style, and determine the target audience for these texts. The Qur’an, regarded as the word of God and a divine miracle, serves as a comprehensive guide and a reflection of human life. Detecting emotions and feelings within the content of the Qur’an contributes to a deeper understanding of God's commandments. Recent advancements, particularly the application of transformer-based language models in natural language processing, have yielded state-of-the-art results that are challenging to surpass easily. In this paper, we propose a method to enhance the accuracy and generality of these models by incorporating syntactic features such as Parts Of Speech (POS) and Dependency Parsing tags. Our approach aims to elevate the performance of emotion detection models, making them more robust and applicable across diverse contexts. For model training and evaluation, we utilized the Isear dataset, a well-established and extensive dataset in this field. The results indicate that our proposed model achieves superior performance compared to existing models, achieving an accuracy of 77% on this dataset. Finally, we applied the newly proposed model to recognize the feelings and emotions conveyed in the Itani English translation of the Qur’an. The results revealed that joy has the most significant contribution to the emotional content of the Holy Qur’an.