Aspect-level sentiment classification (ALSC) aims at identifying the sentiment polarity of a specified aspect in a sentence. ALSC is a practical setting in aspect-based sentiment analysis due to no opinion term labeling needed, but it fails to interp
ret why a sentiment polarity is derived for the aspect. To address this problem, recent works fine-tune pre-trained Transformer encoders for ALSC to extract an aspect-centric dependency tree that can locate the opinion words. However, the induced opinion words only provide an intuitive cue far below human-level interpretability. Besides, the pre-trained encoder tends to internalize an aspect's intrinsic sentiment, causing sentiment bias and thus affecting model performance. In this paper, we propose a span-based anti-bias aspect representation learning framework. It first eliminates the sentiment bias in the aspect embedding by adversarial learning against aspects' prior sentiment. Then, it aligns the distilled opinion candidates with the aspect by span-based dependency modeling to highlight the interpretable opinion terms. Our method achieves new state-of-the-art performance on five benchmarks, with the capability of unsupervised opinion extraction.
Emoji (the popular digital pictograms) are sometimes seen as a new kind of artificial and universally usable and consistent writing code. In spite of their assumed universality, there is some evidence that the sense of an emoji, specifically in regar
d to sentiment, may change from language to language and culture to culture. This paper investigates whether contextual emoji sentiment analysis is consistent across Arabic and European languages. To conduct this investigation, we, first, created the Arabic emoji sentiment lexicon (Arab-ESL). Then, we exploited an existing European emoji sentiment lexicon to compare the sentiment conveyed in each of the two families of language and culture (Arabic and European). The results show that the pairwise correlation between the two lexicons is consistent for emoji that represent, for instance, hearts, facial expressions, and body language. However, for a subset of emoji (those that represent objects, nature, symbols, and some human activities), there are large differences in the sentiment conveyed. More interestingly, an extremely high level of inconsistency has been shown with food emoji.
This study aims to assess the feelings of children toward dental treatment in the
department of pediatric dentistry in order to point the reason of dental fear. The sample
includes 385 children (191 males, 194 females, aged 4-12 years) come to facu
lty of
dentistry between 1/10/2015 – 1/6/2016. The questionnaire form was adapted. Data were
analyzed using statistical program SPSS.
SMA was: fear = 2.96 , anxiety = 2.82 , trouble = 2.74 , like (desire, preference) =
3.56 , feelingsafter treatment = 3.37 , discomfort of the transaction = 1.82 , desire to repeat
the visit = 3.97. The result showed most of the children were not afraid, not worried, not
upset, and they like to repeat. While 9.9% trouble and 6.5% don't like to repeat it. We
concluded that the dental services in p.d department are effective and acceptable.
