  The rise of big data analytics on top of NLP increases the computational
burden for text processing at scale. The problems faced in NLP are very high
dimensional text, so it takes a high computation resource. The MapReduce allows
parallelization of large computations and can improve the efficiency of text
processing. This research aims to study the effect of big data processing on
NLP tasks based on a deep learning approach. We classify a big text of news
topics with fine-tuning BERT used pre-trained models. Five pre-trained models
with a different number of parameters were used in this study. To measure the
efficiency of this method, we compared the performance of the BERT with the
pipelines from Spark NLP. The result shows that BERT without Spark NLP gives
higher accuracy compared to BERT with Spark NLP. The accuracy average and
training time of all models using BERT is 0.9187 and 35 minutes while using
BERT with Spark NLP pipeline is 0.8444 and 9 minutes. The bigger model will
take more computation resources and need a longer time to complete the tasks.
However, the accuracy of BERT with Spark NLP only decreased by an average of
5.7%, while the training time was reduced significantly by 62.9% compared to
BERT without Spark NLP.
