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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