scientific article, Natural Language Processing
scientific article, Natural Language Processing e7mYx
In this scientific article, we build an application related to the evaluation of customer comments on the restaurant's side. For this purpose, the team uses a dataset established by the team with a consensus of 85\%. This dataset is collected on reputable food review websites so that it can create an objective and precise dataset as possible. After acquiring the dataset, the team used PySpark primarily with key support libraries such as BigDL and Analytics zoo. The result is to create an application with many different models to achieve the most general results, but the highest accuracy is 0. 71 with the RNN and CountVectorizer models. 
In this scientific article, we build an application related to the evaluation of customer comments on the restaurant's side. For this purpose, the team 
uses
 a dataset established by the team with a consensus of 85\%. This dataset 
is collected
 on reputable food review websites 
so
 that it can create an objective and precise dataset as possible. After acquiring the dataset, the team 
used
 PySpark 
primarily
 with key support libraries such as 
BigDL
 and Analytics zoo. The result is to create an application with 
many
 different
 models to achieve the most general results, 
but
 the highest accuracy is 0. 71 with the RNN and 
CountVectorizer
 models. 
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