Download PDFOpen PDF in browser

Fine-grained Sentiment Classification using BERT

EasyChair Preprint no. 1762

4 pagesDate: October 24, 2019


Sentiment classification is an important process in understanding people's perception towards a product, service, or topic. Many natural language processing models have been proposed to solve the sentiment classification problem. However, most of them have focused on binary sentiment classification. In this paper, we use a promising deep learning model called BERT to solve the fine-grained sentiment classification task. Experiments show that our model outperforms other popular models for this task without sophisticated architecture. We also demonstrate the effectiveness of transfer learning in natural language processing in the process.

Keyphrases: computational linguistic, Fine-grained sentiment classification, language model, machine learning, Natural Language Processing, neural network, pretrained bert model, pretraining, sentiment classification, Sentiment Classifier, stanford sentiment treebank, Transfer Learning, word embedding, word vector

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Manish Munikar and Sushil Shakya and Aakash Shrestha},
  title = {Fine-grained Sentiment Classification using BERT},
  howpublished = {EasyChair Preprint no. 1762},

  year = {EasyChair, 2019}}
Download PDFOpen PDF in browser