The implementation of learning literary studies courses can be directed at efforts to implement education and student character development. Development of teaching materials is essentially part of overall curriculum development. If you expect graduates of a certain level of education to have the desired character qualifications, a curriculum designed to achieve that goal must also be developed based on the principles of character values. To be precise, the developed learning materials must also meet these demands. That is, the design and development of teaching materials must be deliberately designed to produce graduates who are cultured and of character. Character education is one of the supporters of Indonesia's national education goals. When the lecturers in the class have implemented their learning with character education (contained in the RPS lecturer Poetry Study course), however teaching material also needs to be developed with a character education approach. Therefore, in this study developed teaching materials for the study of poetry based on character education.
Deviation is one of the characteristics that the poet uses in the construction of the poetic text and through it to express thoughts, feelings and emotions, but this expression in a language completely different from ordinary language, so the poet resorts to using an interesting language. Meanwhile, semantic deviation plays a significant role in giving poetry aesthetics. In this regard, our research entitled (Semantic Deviations in Abdulqadir Saeed's Poetic Texts) attempts to answer the question of whether he has been able to artistically express semantic deviations in his poetic texts. Finally, the results, sources used and a summary of the study are presented in English.
Deep learning approaches have become popular in sentiment analysis because of their competitive performance. The downside of this approach is that they do not provide understandable explanations on how the sentiment values are calculated. Previous approaches that used sentiment lexicons for sentiment analysis can do that, but their performance is lower than deep learning approaches. Therefore, it is natural to wonder if the two approaches can be combined to exploit their advantages. In this chapter, we present a neuro-symbolic approach that combines both symbolic and deep learning approaches for sentiment analysis tasks. The symbolic approach exploits sentiment lexicon and shifter patterns—which cover the operations of inversion/reversal, intensification, and attenuation/downtoning. The deep learning approach used a pre-trained language model (PLM) to construct sentiment lexicon. Our experimental result shows that the proposed approach leads to promising results, substantially better than the results of a pure lexicon-based approach. Although the results did not reach the level of the deep learning approach, a great advantage is that sentiment prediction can be accompanied by understandable explanations. For some users, it is very important to see how sentiment is derived, even if performance is a little lower.
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