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 .