1.
Arabic

2.
Most of the developed techniques for topic modeling are language agnostic.
The models can train on any vocabulary. However, once trained, they can be used only with
documents having the same fixed vocabulary specific to the training.
A trained model cannot handle unknown tokens and cannot be easily applied to other languages.
Moreover, in Neural Topic Models deploying various embeddings to represent the input corpus,
these models’ performance depends on the quality of the obtained embeddings specific to the
training language.
This language dependence creates different challenges for different languages that dictate
different handling.

3.
Recent advancements in applying topic modeling to Arabic texts include:
•Improved pre-processing techniques:
•Advanced models:.
•Incorporation of Sentiment analysis:
•Incorporation of Named Entity Recognition:
•Incorporation of Word Embedding:
•Handling unstructured data:
•Handling dialectal Arabic:

4.
•The first stage is dataset acquisition.
•The second stage is for preprocessing the datasets. Preprocessing includes tokenization,
removing punctuation, removing stopwords, tagging, and constructing n-grams. Text normalization
is essential.
•In this study, and for simplicity, only 1-gram tokens were included, and all but noun tokens were
removed.
•This preprocessing results in a smaller number of tokens and slightly shorter documents which
challenges the neural models whose performance is impacted by the corpus’s vocabulary size and
the length of the documents [19]. The preprocessing steps were implemented using CAMEL tools
[15].