ACOM: Arabic Comparative Opinion Mining in Social Media Utilizing Word Embedding, Deep Learning Model, & LLM-GPT

Reliance on social networks has become an integral part of modern daily activities. Social networks are crowded with vast numbers of comments, opinions, and beliefs about different aspects of people’s daily lives. Opinions can directly express positive or negative perspectives toward a specific entity or its aspects, or they can be comparative opinions that present the differences or similarities between two or more entities, such as “product A is better than product B.” Comparative opinions have significant potential to offer new opportunities across various fields such as marketing, education, and e-commerce. Thus, analyzing comparative opinions is essential. Indeed, comparative opinion mining has been applied in several languages, including Arabic. Nevertheless, comparative opinion mining in Arabic is still in its early stages and requires more contributions and investigations. Therefore, this work introduces the ACOM approach for Arabic Comparative Opinion Mining. First, we collected the ACOM corpus in Arabic from the X platform, focusing on comparative opinions in the technology domain. This paper provides a benchmark comparison of several deep-learning models by employing different approaches, including Long Short-Term Memory (LSTM), Bidirectional LSTMs (BiLSTM), and Convolutional Neural Networks (CNN). In the deep learning model, the generated representation vector was used to construct the embedding layer as the first layer. To this end, we adopted two pretrained word embedding techniques to investigate their effectiveness on the ACOM corpus: Word2vec and Bidirectional Encoder Representations from Transformers (BERT). The main highlight of this paper is leveraging the most advanced innovation in AI, the GPT-3 model, as a classifier model. Our experimental results show that BERT and BiLSTM achieved impressive performance on the ACOM task with 91% accuracy and a 90% F1-score. Additionally, this paper discusses the impact of fine-tuning specific hyperparameters by providing a comprehensive examination.