Enhancing Suggestion Detection in Online User Reviews through Integrated Information Retrieval and Deep Learning Approaches

• Published in: Journal of web engineering 2024-01, Vol.23 (3), p.431-464
• Main Authors: Hadizadeh, Zahra, Nazari, Amin, Mansoorizadeh, Muharram
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Classification; Datasets; Deep learning; Distance measurement; Feature selection; Information retrieval; Language; Machine learning; Multilayer perceptrons; Natural language; Neural networks; Product reviews; Recommender systems; Sentiment analysis; Software; Statistical analysis; Support vector machines; User generated content
• ISSN: 1540-9589; 1544-5976
• DOI: 10.13052/jwe1540-9589.2335

In the aftermath of the COVID-19 pandemic, using web platforms as a communication medium and decision-making tool in online commerce has become widely acknowledged. User-generated comments, reflecting positive and negative sentiments towards specific items, serve as invaluable indicators, offering recommendations for product and organizational improvements. Consequently, the extraction of suggestions from mined opinions can enhance the efficacy of companies and organizations in this domain. Prevailing research in suggestion mining predominantly employs rule-based methodologies and statistical classifiers, relying on manually identified features. However, a recent trend has emerged wherein researchers explore solutions grounded in deep learning tools and techniques. This study aims to employ information retrieval techniques for the automated identification of suggestions. To this end, various methodologies, including distance measurement approaches, multilayer perceptron neural networks, support vector machines, regression logistics, convolutional neural networks utilizing TF-IDF, Bag of Words (BOW), and Word2Vec vectors, along with keyword extraction, have been integrated. The proposed approach is assessed using the SemEval2019 dataset to extract suggestions from the textual content of online user reviews. The obtained results demonstrate a notable enhancement in the F1 score, reaching 0.76 compared to prior research. The experiments further suggest that information retrieval-based approaches exhibit promising potential for this specific task.