Aspect-Based Sentiment Analysis for Social Multimedia: A Hybrid Computational Framework

Rana, Muhammad Rizwan Rashid; Rehman, Saif Ur; Nawaz, Asif; Ali, Tariq; Imran, Azhar; Alzahrani, Abdulkareem; Almuhaimeed, Abdullah

doi:10.32604/csse.2023.035149

Cited by 6 publications

(4 citation statements)

References 42 publications

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“…LSIBA-ENN [35] introduces a novel model designed to analyze online product reviews. ARM-BERT [36] proposed POS-ARM with BERT for ABSA. Finally, LeBERT [37] integrates N-grams, BERT lexicon, and CNN for sentiment classification in reviews.…”

Section: B Resultsmentioning

confidence: 99%

A BiLSTM-CF and BiGRU-based Deep Sentiment Analysis Model to Explore Customer Reviews for Effective Recommendations

Rana,

Nawaz,

Ali

et al. 2023

Eng. Technol. Appl. Sci. Res.

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The advancement of technology has led to the rise of social media forums and e-commerce platforms, which have become popular means of communication, and people can express their opinions through comments and reviews. Increased accessibility to online feedback helps individuals make informed decisions about product purchases, services, and other decisions. This study used a sentiment analysis-based approach to improve the functionality of the recommendations from user reviews and consider the features (aspects and opinions) of products and services to understand the characteristics and attributes that influence the performance of classification algorithms. The proposed model consists of data preprocessing, word embedding, character representation creation, feature extraction using BiLSTM-CF, and classification using BiGRU. The proposed model was evaluated on different multidomain benchmark datasets demonstrating impressive performance. The proposed model outperformed existing models, offering more promising performance results in recommendations.

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Section: B Resultsmentioning

confidence: 99%

A BiLSTM-CF and BiGRU-based Deep Sentiment Analysis Model to Explore Customer Reviews for Effective Recommendations

Rana,

Nawaz,

Ali

et al. 2023

Eng. Technol. Appl. Sci. Res.

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“…A hybrid approach [36] integrates aspect extraction, association rule mining, and Bidirectional Encoder Representations from Transformers (BERT) for improved sentiment analysis with accuracy 89%.…”

Section: Literature Reviewmentioning

confidence: 99%

CNN and LSTM based hybrid deep learning model for sentiment analysis on Arabic text reviews

Ahmad,

Saqib,

Syed

2024

Mehran Univ. res. j. eng. technol.

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Companies with diverse product offerings rely on customer reviews to gauge product reception. Following a purchase, customers often share their opinions on the website. Prospective buyers, prior to deciding, typically peruse these reviews to inform their choices. Analysing such feedback, whether positive or negative, holds paramount importance for companies seeking to improve product quality. Researchers are actively exploring methods to categorize comments based on sentiment scores. Notably, customers may express their reviews in Arabic text. Despite challenges such as the structure and morphology of Arabic text, a scarcity of machine-readable Arabic dictionaries, and limited tools for handling Arabic text, minimal progress has been made in the analysis of Arabic reviews. While some attempts have been undertaken, they have achieved suboptimal accuracy. In response, the authors propose a hybrid deep learning model comprising a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) with GlobalMaxPooling. Through multiple iterations, the authors fine-tuned the proposed model and applied it to the publicly available Arabic Reviews dataset, achieving a notable 95% accuracy, precision, recall, and F1 score. The results indicate that, when compared to alternative models, the proposed model exhibits superior accuracy.

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“…Researchers have successfully applied machine learning algorithms to split sentiments in a document [15,20]. However, as the feature set of data grows larger, the temporal complexity of these strategies grows.…”

Section: 4mentioning

confidence: 99%

Enhancing healthcare services recommendation through sentiment analysis

Rana,

Nawaz,

Ali

et al. 2023

Acta Universitatis Sapientiae, Informatica

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As technology advances, most people use social media sites like Twitter, Facebook, and Flickr to share information and communicate with others. The volume of free-text data is growing daily due to the widespread use of these social media platforms. These platforms contain a substantial amount of unstructured information. Patient opinions expressed on social media platforms play a significant role in healthcare improvement and impact health-related policymaking. In this research, we introduce a machine learning approach for the optimal identification of healthcare-related features. This approach is based on a novel synthetic method. Additionally, we employ an entropy-based technique to classify free-text comments from hospital data into positive, negative or neutral. The experimental results and evaluations show 85%, 82.3%, 78.2% and 87% accuracy between ratings of health care. We observed that there is a minor association between our technique, expert opinion and patient interviews. Through the use of machine learning techniques, we achieve an accuracy level that suggests we are capable of providing an accurate and reasonable assessment of the ideal healthcare center for a patient. Our proposed novel framework predicts the healthcare experience at hospitals based on patient reviews posted on social media. This innovative approach outperforms traditional methods, such as surveys and expert opinions.

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Aspect-Based Sentiment Analysis for Social Multimedia: A Hybrid Computational Framework

Cited by 6 publications

References 42 publications

A BiLSTM-CF and BiGRU-based Deep Sentiment Analysis Model to Explore Customer Reviews for Effective Recommendations

A BiLSTM-CF and BiGRU-based Deep Sentiment Analysis Model to Explore Customer Reviews for Effective Recommendations

CNN and LSTM based hybrid deep learning model for sentiment analysis on Arabic text reviews

Enhancing healthcare services recommendation through sentiment analysis

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