A Novel Approach for Emotion Detection and Sentiment Analysis for Low Resource Urdu Language Based on CNN-LSTM

Ullah, Farhat; Chen, Xin; Shah, Syed Bilal Hussain; Mahfoudh, Saoucène; Hassan, Muhammad Abul; Saeed, Nagham

doi:10.3390/electronics11244096

Cited by 16 publications

(8 citation statements)

References 33 publications

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“…In this study, they used the LSTM for text classification tasks, LSTM can embed variable text regions. they incorporate LSTM region embedding with convolutional layers to produce maximum results, and LSTM’s combined with CNN is more beneficial than the regional embedding approach [ 38 , 39 ]. This study developed a fusion deep learning structure that first learned the sentiment embedding vector from the CNN and then used the Multi-Objective Optimisation (MOO) model to produce a set of optimisation features, and last used SVM to classify the optimised vector sentiment.…”

Section: Related Workmentioning

confidence: 99%

Domain adaptive learning for multi realm sentiment classification on big data

Ijaz,

Anwar,

Safran

et al. 2024

PLoS ONE

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Machine learning techniques that rely on textual features or sentiment lexicons can lead to erroneous sentiment analysis. These techniques are especially vulnerable to domain-related difficulties, especially when dealing in Big data. In addition, labeling is time-consuming and supervised machine learning algorithms often lack labeled data. Transfer learning can help save time and obtain high performance with fewer datasets in this field. To cope this, we used a transfer learning-based Multi-Domain Sentiment Classification (MDSC) technique. We are able to identify the sentiment polarity of text in a target domain that is unlabeled by looking at reviews in a labelled source domain. This research aims to evaluate the impact of domain adaptation and measure the extent to which transfer learning enhances sentiment analysis outcomes. We employed transfer learning models BERT, RoBERTa, ELECTRA, and ULMFiT to improve the performance in sentiment analysis. We analyzed sentiment through various transformer models and compared the performance of LSTM and CNN. The experiments are carried on five publicly available sentiment analysis datasets, namely Hotel Reviews (HR), Movie Reviews (MR), Sentiment140 Tweets (ST), Citation Sentiment Corpus (CSC), and Bioinformatics Citation Corpus (BCC), to adapt multi-target domains. The performance of numerous models employing transfer learning from diverse datasets demonstrating how various factors influence the outputs.

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Section: Related Workmentioning

confidence: 99%

Domain adaptive learning for multi realm sentiment classification on big data

Ijaz,

Anwar,

Safran

et al. 2024

PLoS ONE

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“…This section includes an overview of previous research done in the field of emotion detection using deep learning algorithms including CNNs and Word2Vec embeddings. Ullah et al [5] presents the implementation of different word embedding techniques and applies the CNN to get the best model. It has applied TF-IDF, bag-of-word, and skip-gram word embedding and then used the various deep learning algorithms such as long short-term memory (LSTM), artificial neural network (ANN) and recurrent neural network (RNN) along with CNN.…”

Section: Related Workmentioning

confidence: 99%

Emotion detection using Word2Vec and convolution neural networks

Jadon,

Kumar

2024

IJEECS

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<div>Emotion detection from text plays a very critical role in different domains, including customer service, social media analysis, healthcare, financial services, education, human-to-computer interaction, psychology, and many more. Nowadays, deep learning techniques become popular due to their capabilities to capture inherent complex insights and patterns from raw data. In this paper, we have used the Word2Vec embedding approach that takes care of the semantic and contextual understanding of text making it more realistic while detecting emotions. These embeddings act as input to the convolution neural network (CNN) to capture insights using feature maps. The Word2Vec and CNN models applied to the international survey on emotion antecedents and reactions (ISEAR) dataset outperform the models in the literature in terms of accuracy and F1-score as model evaluation metrics. The proposed approach not only obtains high accuracy in emotion detection tasks but also generates interpretable representations that contribute to the understanding of emotions in textual data. These findings carry significant implications for applications in diverse domains, such as social media analysis, market research, clinical assessment and counseling, and tailored recommendation systems.</div>

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“…Based on the above summary comparison, in the field of sentiment analysis [24], deep learning methods that have been developed in recent years [25][26][27] can automatically and quickly extract relevant features from large-scale text data and capture deep semantic information more easily, with better classification results. However, there are still limitations in word vector representation and the neural network feature extraction processes in deep learning methods [28][29][30], which may lead to incomplete feature extraction or failure to adequately capture semantic information, thus affecting the classification results. To address this problem, this paper constructed BERT and optimized an improved CNN-LSTM model as BERT-ETextCNN-ELSTM (BERT-Enhanced Convolution Neural Networks-Enhanced Long Short-Term Memory) to improve comment sentiment analysis with improved accuracy and efficiency.…”

Section: Related Studiesmentioning

confidence: 99%

Sentiment Analysis of Comment Data Based on BERT-ETextCNN-ELSTM

Deng

Yin

et al. 2023

Electronics

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With the rapid popularity and continuous development of social networks, users’ communication and interaction through platforms such as microblogs and forums have become more and more frequent. The comment data on these platforms reflect users’ opinions and sentiment tendencies, and sentiment analysis of comment data has become one of the hot spots and difficulties in current research. In this paper, we propose a BERT-ETextCNN-ELSTM (Bidirectional Encoder Representations from Transformers–Enhanced Convolution Neural Networks–Enhanced Long Short-Term Memory) model for sentiment analysis. The model takes text after word embedding and BERT encoder processing and feeds it to an optimized CNN layer for convolutional operations in order to extract local features of the text. The features from the CNN layer are then fed into the LSTM layer for time-series modeling to capture long-term dependencies in the text. The experimental results proved that compared with TextCNN (Convolution Neural Networks), LSTM (Long Short-Term Memory), TextCNN-LSTM (Convolution Neural Networks–Long Short-Term Memory), and BiLSTM-ATT (Bidirectional Long Short-Term Memory Network–Attention), the model proposed in this paper was more effective in sentiment analysis. In the experimental data, the model reached a maximum of 0.89, 0.88, and 0.86 in terms of accuracy, F1 value, and macro-average F1 value, respectively, on both datasets, proving that the model proposed in this paper was more effective in sentiment analysis of comment data. The proposed model achieved better performance in the review sentiment analysis task and significantly outperformed the other comparable models.

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A Novel Approach for Emotion Detection and Sentiment Analysis for Low Resource Urdu Language Based on CNN-LSTM

Cited by 16 publications

References 33 publications

Domain adaptive learning for multi realm sentiment classification on big data

Domain adaptive learning for multi realm sentiment classification on big data

Emotion detection using Word2Vec and convolution neural networks

Sentiment Analysis of Comment Data Based on BERT-ETextCNN-ELSTM

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