Multi-Label Classification of Text Documents Using Deep Learning

Mohammed, Hamza Haruna; Doğdu, Erdoğan; Görür, Abdül Kadir; Choupani, Roya

doi:10.1109/bigdata50022.2020.9378266

Cited by 7 publications

(7 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The F1 score penalizes incorrect class predictions in proposed models. The study [34] performed better than the study [33] in terms of the F1 score.…”

Section: Resultsmentioning

confidence: 60%

“…As a result of word insertion using the Keras embedding layer, the hybrid CNNLSTM method outperformed all accuracy results of the study [29]. At the same time, the proposed study provided a good performance by providing better results in terms of precision, recall, F1 score, and study precision [34]. In this study, the parameters of the CNNLSTM model were also determined by experimental studies.…”

Section: Discussionmentioning

confidence: 84%

“…It is stated that when the BiLSTM model is used with GloVe word insertion, it provides better results than the LSTM and CNN methods. Another study in which the results obtained with the hybrid CNNLSTM model were compared in the article is [34]'s study. According to this study, the LSTM method is stated to give a better result than the CNN and GRU methods.…”

Section: Resultsmentioning

confidence: 99%

“…With the proposed deep learning model, a 99.41% test success rate has been achieved. The success rate was compared with studies [34] and [33] available in the literature. As a result of word insertion using the Keras embedding layer, the hybrid CNNLSTM method outperformed all accuracy results of the study [29].…”

Section: Discussionmentioning

confidence: 99%

See 3 more Smart Citations

CNN Ve LSTM Tabanli Hi̇bri̇t Bi̇r Deri̇n Öğrenme Modeli̇ İle Çok Eti̇ketli̇ Meti̇n Anali̇zi̇

ÇETİNER

2022

Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi

View full text Add to dashboard Cite

In this article, it is aimed to categorize meaningful content from uncontrolled growing written social sharing data using natural language processing. Uncategorized data can disturb social sharing users with an increasing user network due to deprecating and negative content. For the stated reason, a hybrid model based on CNN and LSTM has been proposed to automatically classify all written social sharing content, both positive and negative, into defined target tags. With the proposed hybrid model, it is aimed at automatically classifying the content of the social sharing system into different categories by using the simplest embedding layer, keras. As a result of the experimental studies carried out, a better result was obtained than in the different studies in the literature using the same data set with the proposed method. The obtained performance results show that the proposed method can be applied to different multilabel text analysis problems.

show abstract

“…The F1 score penalizes incorrect class predictions in proposed models. The study [34] performed better than the study [33] in terms of the F1 score.…”

Section: Resultsmentioning

confidence: 60%

Section: Discussionmentioning

confidence: 84%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

CNN Ve LSTM Tabanli Hi̇bri̇t Bi̇r Deri̇n Öğrenme Modeli̇ İle Çok Eti̇ketli̇ Meti̇n Anali̇zi̇

ÇETİNER

2022

Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi

View full text Add to dashboard Cite

show abstract

“…For multi-label classi cation, several methods are available. Problem transformation approaches and employing Adapted algorithms are examples of methods used in multi-label classi cation (Mohammed et al, 2020).…”

Section: Deep Learning Approach For Multi-label Text Emotion Classi C...mentioning

confidence: 99%

Multi-label Emotion Classification on Social Media Comments using Deep learning

Bayu,

tegegn

2024

Preprint

View full text Add to dashboard Cite

Social media is an online platform that people use to develop social networks or relationships with others. Every day, millions of people use different social media to express their thoughts, emotions, and experiences. An emotion is a complex psychological event that involves a mixture of reactions occurring in the human body and brain, usually triggered by a mental content(Almeida et al., 2018). Multi-label text emotion classification is the problem that aims to identify all possible emotions from a given text that best represents the author's mental state. Many researches have been done on text emotion classification in English, Arabic, and Chinese language. However, most of them focus on single-label emotion classification which is unable to identify all present emotions in the given instance. To the best of our knowledge, there is no research conducted on multi-label emotion classification for Amharic text. In addition to this, there is no available dataset to conduct multi-label emotion classification research. These reasons motivate us to do research on multi-label Amharic text emotion classification. for this research we collected 18000 datasets from different social platforms YouTube, Facebook, and Twitter. The dataset is annotated by psychologists and other professionals. We use word2vec and one hot encoding to prepare the feature vector. We train and test four deep-learning approaches such as LSTM, BILSTM, CNN, and GRU. We perform the experiment by feeding one hot encoding and word2vec features to these for deep learning models and achieve the best accuracy with one hot vector. We achieve test accuracy of 53.1%, 54.5%, 54%, and 39.7% for LSTM, BILSTM, CNN, and GRU respectively. For the future we conduct this research using a large dataset with transformer models (BRT, ROBERTA, and XLNET) and test the performance of these models on Amharic text multi-label emotion classification.

show abstract

A multi-stage multi-objective GWO based feature selection approach for multi-label text classification

Dhal

Azad

2022

2022 2nd International Conference on Intelligent Technologies (CONIT)

View full text Add to dashboard Cite

Multi-Label Classification of Text Documents Using Deep Learning

Cited by 7 publications

References 28 publications

CNN Ve LSTM Tabanli Hi̇bri̇t Bi̇r Deri̇n Öğrenme Modeli̇ İle Çok Eti̇ketli̇ Meti̇n Anali̇zi̇

CNN Ve LSTM Tabanli Hi̇bri̇t Bi̇r Deri̇n Öğrenme Modeli̇ İle Çok Eti̇ketli̇ Meti̇n Anali̇zi̇

Multi-label Emotion Classification on Social Media Comments using Deep learning

A multi-stage multi-objective GWO based feature selection approach for multi-label text classification

Contact Info

Product

Resources

About