Deep Attentional Bidirectional LSTM for Arabic Sentiment Analysis In Twitter

Elfaik, Hanane; Nfaoui, El Habib

doi:10.1109/esmarta52612.2021.9515751

Cited by 8 publications

(3 citation statements)

References 36 publications

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“…The output achieved is the input for our sentiment classification neural network model, which is a deep learning sequence model. The model has an architecture composed of the following layers: an embedding layer created using the word-2vec model to convert tweets into word vectors (Mikolov et al 2013); a Bi-LSTM neural network layer to capture the semantic meaning of the text (Elfaik and Nfaoui 2021;Chandra et al 2021); an attention mechanism layer to extract the most relevant words; a dense layer which adds a fully connected layer in the model and that the argument passed specifies the amount of nodes in that layer; and finally, a last dense layer with a sigmoid activation function to achieve the emotion classification (Fig. 1).…”

Section: Analysis Methodsmentioning

confidence: 99%

Nuclear energy: Twitter data mining for social listening analysis

Bilbao

Jaca-Madariaga

Belver

et al. 2023

Soc. Netw. Anal. Min.

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Knowing the presence, attitude and sentiment of society is important to promote policies and actions that influence the development of different energy sources and even more so in the case of an energy source such as nuclear, which has not been without controversy in recent years. The purpose of this paper was to conduct a social listening analysis of nuclear energy using Twitter data mining. A total of 3,709,417 global tweets were analyzed through the interactions and emotions of Twitter users throughout a crucial year: 6 months before and 6 months after the beginning of Russian invasion of Ukraine and the first attack on the Zaporizhzhia NPP. The research uses a novel approach to combine social network analysis methods with the application of artificial neural network models. The results reveal the digital conversation is influenced by the Russian invasion of Ukraine. However, tweets containing personal opinions of influential people also manage to enter the digital conversation, defining the magnitude and direction of the debate. The digital conversation is not constructed as a public argument. Generally, it is a conversation with non-polarized communities (politics, business, science and media); neither armed conflict or military threats against Zaporizhzhia NPP succeed in rousing anti-nuclear voices, even though these events do modify the orientation of the sentiment in the language used, making it more negative.

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Section: Analysis Methodsmentioning

confidence: 99%

Nuclear energy: Twitter data mining for social listening analysis

Bilbao

Jaca-Madariaga

Belver

et al. 2023

Soc. Netw. Anal. Min.

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“…Other studies ( Sabba et al, 2022 ; Ali, Abd El Hamid & Youssif, 2019 ; Rehman et al, 2019 ; Yenter & Verma, 2017 ; Gandhi et al, 2021 ) also developed deep-learning CNN and LSTM models on movie datasets for sentiment classification. LISAC Laboratory ( Elfaik & Nfaoui, 2021 ) used NLP techniques and the BiLSTM model for sentiment extraction. To extract meaningful and high-quality information from the tweets, they used word2vec with word embedding.…”

Section: Related Workmentioning

confidence: 99%

What people think about fast food: opinions analysis and LDA modeling on fast food restaurants using unstructured tweets

Mujahid

Rustam

Alasim

et al. 2023

PeerJ Computer Science

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With the rise of social media platforms, sharing reviews has become a social norm in today’s modern society. People check customer views on social networking sites about different fast food restaurants and food items before visiting the restaurants and ordering food. Restaurants can compete to better the quality of their offered items or services by carefully analyzing the feedback provided by customers. People tend to visit restaurants with a higher number of positive reviews. Accordingly, manually collecting feedback from customers for every product is a labor-intensive process; the same is true for sentiment analysis. To overcome this, we use sentiment analysis, which automatically extracts meaningful information from the data. Existing studies predominantly focus on machine learning models. As a consequence, the performance analysis of deep learning models is neglected primarily and of the deep ensemble models especially. To this end, this study adopts several deep ensemble models including Bi long short-term memory and gated recurrent unit (BiLSTM+GRU), LSTM+GRU, GRU+recurrent neural network (GRU+RNN), and BiLSTM+RNN models using self-collected unstructured tweets. The performance of lexicon-based methods is compared with deep ensemble models for sentiment classification. In addition, the study makes use of Latent Dirichlet Allocation (LDA) modeling for topic analysis. For experiments, the tweets for the top five fast food serving companies are collected which include KFC, Pizza Hut, McDonald’s, Burger King, and Subway. Experimental results reveal that deep ensemble models yield better results than the lexicon-based approach and BiLSTM+GRU obtains the highest accuracy of 95.31% for three class problems. Topic modeling indicates that the highest number of negative sentiments are represented for Subway restaurants with high-intensity negative words. The majority of the people (49%) remain neutral regarding the choice of fast food, 31% seem to like fast food while the rest (20%) dislike fast food.

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“…Generally, various methods based on DL, such as Convolution Neural Networks (CNN) [23], Recurrent Neural Networks (RNN) [24], Hierarchical Attention Networks (HAN) [25], Support Vector Machine (SVM) [26], Residual Learning with Simplified CNN Extractor [27], distant, subjective supervision [28], adaptive recursive neural network [29], Random Forest (RF), Decision Tree (DT) [30], Bidirectional Long Short-Term Memory (Bi-LSTM), a hybrid of CNN and Bi-LSTM, Naive Bayes (NB) [31], Emotion Tokens, BiGRU-CNN model [32], Improved Negation Handling, and other effective intelligent methods for classification of the Turkish, Chinese, Thai, Covid, business, and medical-based Twitter datasets for sentiment analysis [33,34]. For Arabic language tweets, in the datasets analysis for various tasks such as classification or prediction, the researchers have used such Deep Attentional Bidirectional LSTM, Chi-Square and K-Nearest Neighbor, Convolutional Neural Networks, Narrow Convolutional Neural Networks (NCNN), CNN and RNN, Bidirectional LSTM, SVM, KNN, Decision Trees, NB, and others for Arabic Sentiment Analysis using the Twitter dataset for solving different tasks [35,36].…”

Section: Introductionmentioning

confidence: 99%

Using the AraBERT Model for Customer Satisfaction Classification of Telecom Sectors in Saudi Arabia

Aftan

Shah

2023

Brain Sciences

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Customer satisfaction and loyalty are essential for every business. Feedback prediction and social media classification are crucial and play a key role in accurately identifying customer satisfaction. This paper presents sentiment analysis-based customer feedback prediction based on Twitter Arabic datasets of telecommunications companies in Saudi Arabia. The human brain, which contains billions of neurons, provides feedback based on the current and past experience provided by the services and other related stakeholders. Artificial Intelligent (AI) based methods, parallel to human brain processing methods such as Deep Learning (DL) algorithms, are famous for classifying and analyzing such datasets. Comparing the Arabic Dataset to English, it is pretty challenging for typical methods to outperform in the classification or prediction tasks. Therefore, the Arabic Bidirectional Encoder Representations from Transformers (AraBERT) model was used and analyzed with various parameters such as activation functions and topologies and simulated customer satisfaction prediction takes using Arabic Twitter datasets. The prediction results were compared with two famous DL algorithms: Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). Results show that these methods have been successfully applied and obtained highly accurate classification results. AraBERT achieved the best prediction accuracy among the three ML methods, especially with Mobily and STC datasets.

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Deep Attentional Bidirectional LSTM for Arabic Sentiment Analysis In Twitter

Cited by 8 publications

References 36 publications

Nuclear energy: Twitter data mining for social listening analysis

Nuclear energy: Twitter data mining for social listening analysis

What people think about fast food: opinions analysis and LDA modeling on fast food restaurants using unstructured tweets

Using the AraBERT Model for Customer Satisfaction Classification of Telecom Sectors in Saudi Arabia

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