Abstract:
Automatically generating or predicting tags for movies can help recommendation
engines improve retrieval of similar movies, and help viewers know what to expect
from a movie in advance. It improves the search results of a movie recommender
system by predicting high weighted tags from a movie's plot synopsis. We propose
a model in which we at rst perform pre-processing of data(stopwords eradication,
stemming of data etc.) and then tokenize the data by a technique called BERT
and then vectorize it by TF-IDF process and then input those pre-processed data
to a deep learning technique to give us a prediction tag scores from a set of tags
for movies. We compare our system's result with an already proposed model
with emotion
ow encoded neural network and found that our model's perfor-
mance shows improvement in result(TL, TR and F1 measure) specially due to
pre-processing of data and for using the techniques like BERT and TF-IDF.
