Multi-class sentiment classification using BERT algorithm
Abstract
Sentiment analysis is more popular on microblogging platforms like Twitter etc.,The results
of a number of research indicate that the state of sentiment does not communicate sentiment
dependent on the context of the user. This is due to the fact that variable durations and
confusing emotional information are involved. Comparison to when it was used with
Word2vec and when it was used with no variation, the experimental data show that the
combination of BERT with CNN, BERT with RNN, and BERT with BiLSTM performs very
well in terms of accuracy rate, precision rate, recall rate, and F1-score. This is the case when
comparisons are made between the three combinations. Their results showed that the
suggested model performed better than other models, with a maximum accuracy of 88.75%.
Keywords: sentiment analysis; deep learning; tweets; BERT; LSTM; CNN
Multi-class sentiment classification using BERT algorithm
Abstract Sentiment analysis is more popular on microblogging platforms like Twitter etc.,The results of a number of research indicate that the state of sentiment does not communicate sentiment dependent on the context of the user. This is due to the fact that variable durations and confusing emotional information are involved. Comparison to when it was used with Word2vec and when it was used with no variation, the experimental data show that the combination of BERT with CNN, BERT with RNN, and BERT with BiLSTM performs very well in terms of accuracy rate, precision rate, recall rate, and F1-score. This is the case when comparisons are made between the three combinations. Their results showed that the suggested model performed better than other models, with a maximum accuracy of 88.75%. Keywords: sentiment analysis; deep learning; tweets; BERT; LSTM; CNN
Multi-class sentiment classification using BERT algorithm
Abstract Sentiment analysis is more popular on microblogging platforms like Twitter etc.,The results of a number of research indicate that the state of sentiment does not communicate sentiment dependent on the context of the user. This is due to the fact that variable durations and confusing emotional information are involved. Comparison to when it was used with Word2vec and when it was used with no variation, the experimental data show that the combination of BERT with CNN, BERT with RNN, and BERT with BiLSTM performs very well in terms of accuracy rate, precision rate, recall rate, and F1-score. This is the case when comparisons are made between the three combinations. Their results showed that the suggested model performed better than other models, with a maximum accuracy of 88.75%. Keywords: sentiment analysis; deep learning; tweets; BERT; LSTM; CNN
Multi-class sentiment classification using BERT algorithm
Abstract Sentiment analysis is more popular on microblogging platforms like Twitter etc.,The results of a number of research indicate that the state of sentiment does not communicate sentiment dependent on the context of the user. This is due to the fact that variable durations and confusing emotional information are involved. Comparison to when it was used with Word2vec and when it was used with no variation, the experimental data show that the combination of BERT with CNN, BERT with RNN, and BERT with BiLSTM performs very well in terms of accuracy rate, precision rate, recall rate, and F1-score. This is the case when comparisons are made between the three combinations. Their results showed that the suggested model performed better than other models, with a maximum accuracy of 88.75%. Keywords: sentiment analysis; deep learning; tweets; BERT; LSTM; CNN
Multi-class sentiment classification using BERT algorithm
Abstract Sentiment analysis is more popular on microblogging platforms like Twitter etc.,The results of a number of research indicate that the state of sentiment does not communicate sentiment dependent on the context of the user. This is due to the fact that variable durations and confusing emotional information are involved. Comparison to when it was used with Word2vec and when it was used with no variation, the experimental data show that the combination of BERT with CNN, BERT with RNN, and BERT with BiLSTM performs very well in terms of accuracy rate, precision rate, recall rate, and F1-score. This is the case when comparisons are made between the three combinations. Their results showed that the suggested model performed better than other models, with a maximum accuracy of 88.75%. Keywords: sentiment analysis; deep learning; tweets; BERT; LSTM; CNN
Multi-class sentiment classification using BERT algorithm
Abstract Sentiment analysis is more popular on microblogging platforms like Twitter etc.,The results of a number of research indicate that the state of sentiment does not communicate sentiment dependent on the context of the user. This is due to the fact that variable durations and confusing emotional information are involved. Comparison to when it was used with Word2vec and when it was used with no variation, the experimental data show that the combination of BERT with CNN, BERT with RNN, and BERT with BiLSTM performs very well in terms of accuracy rate, precision rate, recall rate, and F1-score. This is the case when comparisons are made between the three combinations. Their results showed that the suggested model performed better than other models, with a maximum accuracy of 88.75%. Keywords: sentiment analysis; deep learning; tweets; BERT; LSTM; CNN
