Deep Learning Approaches for Multi-Label Incidents Classification from Twitter Textual Information

Anggraeni, Sherly Rosa; Ranggianto, Narandha Arya; Ghozali, Imam; Fatichah, Chastine; Purwitasari, Diana

doi:10.20473/jisebi.8.1.31-41

Cited by 10 publications

(3 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Deep neural network-based multi-label text classification techniques have recently gained popularity due to the rapid growth of deep learning, such as: long short-term memory (LSTM) [9], bidirectional long short-term memory (BiLSTM) [10], convolutional neural network (CNN) [2], [11]- [13] and recurrent convolutional neural network (RCNN) [12]. Apart from the development of various types of models to solve the multi-label text classification, the feature representation that can be used are also evolving.…”

Section: Introductionmentioning

confidence: 99%

Multi-label text classification of Indonesian customer reviews using bidirectional encoder representations from transformers language model

Nissa

Yulianti

2023

IJECE

View full text Add to dashboard Cite

Customer review is a critical resource to support the decision-making process in various industries. To understand how customers perceived each aspect of the product, we can first identify all aspects discussed in the customer reviews by performing multi-label text classification. In this work, we want to know the effectiveness of our two proposed strategies using bidirectional encoder representations from transformers (BERT) language model that was pre-trained on the Indonesian language, referred to as IndoBERT, to perform multi-label text classification. First, IndoBERT is used as feature representation to be combined with convolutional neural network-extreme gradient boosting (CNN-XGBoost). Second, IndoBERT is used both as the feature representation as well as the classifier to directly solve the classification task. Additional analysis is performed to compare our results with those using multilingual BERT model. According to our experimental results, our first model using IndoBERT as feature representation shows significant performance over some baselines. Our second model using IndoBERT as both feature representation and classifier can significantly enhance the effectiveness of our first model. In summary, our proposed models can improve the effectiveness of the baseline using Word2Vec-CNN-XGBoost by 19.19% and 6.17%, in terms of accuracy and F-1 score, respectively.

show abstract

Section: Introductionmentioning

confidence: 99%

Multi-label text classification of Indonesian customer reviews using bidirectional encoder representations from transformers language model

Nissa

Yulianti

2023

IJECE

View full text Add to dashboard Cite

show abstract

“…We can take public opinion from social media, one of which is Twitter. Twitter is a good source of information because it publishes news and information from various sources, be it facts or personal opinions [2]. Opinions expressed by the public on Twitter can be in the form of positive, negative, or neutral responses or sentiments.…”

Section: Introductionmentioning

confidence: 99%

Aspect-based Sentiment and Correlation-based Emotion Detection on Tweets for Understanding Public Opinion of Covid-19

Salsabila

Tyas

Romadhona

et al. 2023

J. Inf. Syst. Eng. Bus. Intell.

View full text Add to dashboard Cite

Background: During the Covid-19 period, the government made policies dealing with it. Policies issued by the government invited public opinion as a form of public reaction to these policies. The easiest way to find out the public’s response is through Twitter’s social media. However, Twitter data have limitations. There is a mix between facts and personal opinions. It is necessary to distinguish between these. Opinions expressed by the public can be both positive and negative, so correlation is needed to link opinions and their emotions. Objective: This study discusses sentiment and emotion detection to understand public opinion accurately. Sentiment and emotion are analyzed using Pearson correlation to determine the correlation. Methods: The datasets were about public opinion of Covid-19 retrieved from Twitter. The data were annotated into sentiment and emotion using Pearson correlation. After the annotation process, the data were preprocessed. Afterward, single model classification was carried out using machine learning methods (Support Vector Machine, Random Forest, Naïve Bayes) and deep learning method (Bidirectional Encoder Representation from Transformers). The classification process was focused on accuracy and F1-score evaluation. Results: There were three scenarios for determining sentiment and emotion, namely the factor of aspect-based and correlation-based, without those factors, and aspect-based sentiment only. The scenario using the two aforementioned factors obtained an accuracy value of 97%, while an accuracy of 96% was acquired without them. Conclusion: The use of aspect and correlation with Pearson correlation has helped better understand public opinion regarding sentiment and emotion more accurately. Keywords: Aspect-based sentiment, Deep learning, Emotion detection, Machine learning, Pearson correlation, Public opinion.

show abstract

“…In earlier research [7], the discussion concerning the classification of events revolved around the application of deep learning methods to categorize incidents shared in Bahasa Indonesia on Twitter. The investigation compared three different methods and underscored that the combination of CNN with NeuroNER produced the most favorable outcomes for multi-label classification.…”

Section: Introductionmentioning

confidence: 99%

Fine-Tuning IndoBERT for Indonesian Exam Question Classification Based on Bloom's Taxonomy

Baharuddin,

Naufal

2023

J. Inf. Syst. Eng. Bus. Intell.

View full text Add to dashboard Cite

Background: The learning assessment of elementary schools has recently incorporated Bloom's Taxonomy, a structure in education that categorizes different levels of cognitive learning and thinking skills, as a fundamental framework. This assessment now includes High Order Thinking Skill (HOTS) questions, with a specific focus on Indonesian topics. The implementation of this system has been observed to require teachers to manually categorize or classify questions, and this process typically requires more time and resources. To address the associated difficulty, automated categorization and classification are required to streamline the process. However, despite various research efforts in questions classification, there is still room for improvement in terms of performance, particularly in precision and accuracy. Numerous investigations have explored the use of Deep Learning Natural Language Processing models such as BERT for classification, and IndoBERT is one such pre-trained model for text analysis. Objective: This research aims to build classification system that is capable of classifying Indonesian exam questions in multiple-choice form based on Bloom's Taxonomy using IndoBERT pre-trained model. Methods: The methodology used includes hyperparameter fine-tuning, which was carried out to identify the optimal model performance. This performance was subsequently evaluated based on accuracy, F1 Score, Precision, Recall, and the time required for the training and validation of the model. Results: The proposed Fine Tuned IndoBERT Model showed that the accuracy rate was 97%, 97% F1 Score, 97% Recall, and 98% Precision with an average training time per epoch of 1.55 seconds and an average validation time per epoch of 0.38 seconds. Conclusion: Fine Tuned IndoBERT model was observed to have a relatively high classification performance, and based on this observation, the system was considered capable of classifying Indonesian exam questions at the elementary school level. Keywords: IndoBERT, Fine Tuning, Indonesian Exam Question, Model Classifier, Natural Language Processing, Bloom’s Taxonomy

show abstract

Deep Learning Approaches for Multi-Label Incidents Classification from Twitter Textual Information

Cited by 10 publications

References 32 publications

Multi-label text classification of Indonesian customer reviews using bidirectional encoder representations from transformers language model

Multi-label text classification of Indonesian customer reviews using bidirectional encoder representations from transformers language model

Aspect-based Sentiment and Correlation-based Emotion Detection on Tweets for Understanding Public Opinion of Covid-19

Fine-Tuning IndoBERT for Indonesian Exam Question Classification Based on Bloom's Taxonomy

Contact Info

Product

Resources

About