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We present a neural-network-driven model for annotating frustration intensity in customer support tweets, based on representing tweet texts using a bag-of-words encoding after processing with subword segmentation together with non-lexical features. T he model was evaluated on tweets in English and Latvian languages, focusing on aspects beyond the pure bag-of-words representations used in previous research. The experimental results show that the model can be successfully applied for texts in a non-English language, and that adding non-lexical features to tweet representations significantly improves performance, while subword segmentation has a moderate but positive effect on model accuracy. Our code and training data are publicly available.
We concentrate in this research on a question which relates the two principles: justice & ( the promise & warning) , which is the question of frustration of work & falling it , it means a measure of human works value . The problem focus on the ret ires view for the interest of (Favors & Bad works ) through comparison of liabilities positions of ( Ashaera & Imamiah ). This research consist of : Introduction , conclusion & six main parts . The first part is the terminological meaning of (Frustration &Penance) , those concepts mean falling . The second part : discusses the problem in Almutazela's thought , within two positions : the first one is the position of (Abu Ali Aljeba'ee) who said in frustration& penance with the pure sense . The second position is the position of (Abu Hashem Aljeba'ee) how compares between the two meanings : ( Reward & Punishment ) , and the victories of the most a preponderance in comparison of the lowest preponderance . The Third & Forth part concentrates on Almutazela intellect & transportism evidences on the frustration and atonement and the evidences of the liabilities on the same question .
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