Abstract:
The aim of the present research is to evaluate the child’s nutritional
method (2-5 years old) which is based on his resistance of the food highly rich
with nutritional elements and his acceptance of the food of a low nutritional
value in addition to his having forbidden food with other mates and making
use of all mates when having food, in establishing the sound social values and
affection since child hood. The required statistical equation have been used
by the researcher namely (Z test).
The sample of the present study consists of (26) children who were selected
intentionally and randomly from the kindergartens of Al-Bayaa region and the
college of Education for women. The questionnaires were distributed among
the samples mother. After sorting the answers, it was noticed that the foods
refused by the child included : milk, eggs, meat, fish, rice, potatoes, soup,
bread, eggplants, green pepper, cucumber, tomatoes, banana and orange.
The reasons behind the children’s refusal mediated between the highest
percentage (100%) for the children who had no desire and the lowest
percentage (20%) for fear of the shape of food as a fish for not having such
types of food by the cartoon films, while the other percentages (30-80%)
oscillated for the food taste, lack of appetite, inability to crew, food smell,
creating accuses, the child’s health but when the child was brought together
with his mates, he was able to have the same rejected food in a high degree.
The results of the study concluded that the increase of the child’s acceptance
of the food leads to some positive indicators among which achieving the
sound social, educational and nutritional aims especially under the
circumstances our country lives in and this is the first step towards
establishing the national values for the children of the neighborhood and the
kinder gather friends away from any wrong concept that might dissociate
them.
Aspect-based sentiment analysis is the most important research topic conducted to extract and categorize aspect-terms from online reviews. Recent efforts have shown that topic modelling is vigorously used for this task. In this paper, we integrated word embedding into collapsed Gibbs sampling in Latent Dirichlet Allocation (LDA). Specifically, the conditional distribution in the topic model is improved using the word embedding model that was trained against (customer review) training dataset. Semantic similarity (cosine measure) was leveraged to distribute the aspect-terms to their related aspect-category cognitively. The experiment was conducted to extract and categorize the aspect terms from SemEval 2014 dataset.
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