Farid Ariai - Department of Computer Engineering, University of Tehran, Kish International Campus, Kish, Iran
Maryam Tayefeh Mahmoudi - Data Analysis & Processing Research Group, IT Research Faculty, ICT Research Institute, Tehran, Iran.
Ali Moeini - School of Engineering Science, College of Engineering, University of Tehran, Tehran, Iran.

In the era of pervasive internet use and the dominance of social networks, researchers face significant challenges in Persian text mining, including the scarcity of adequate datasets in Persian and the inefficiency of existing language models. This paper specifically tackles these challenges, aiming to amplify the efficiency of language models tailored to the Persian language. Focusing on enhancing the effectiveness of sentiment analysis, our approach employs an aspect-based methodology utilizing the ParsBERT model, augmented with a relevant lexicon. The study centers on sentiment analysis of user opinions extracted from the Persian website 'Digikala.' The experimental results not only highlight the proposed method's superior semantic capabilities but also showcase its efficiency gains with an accuracy of ۸۸.۲% and an F۱ score of ۶۱.۷. The importance of enhancing language models in this context lies in their pivotal role in extracting nuanced sentiments from user-generated content, ultimately advancing the field of sentiment analysis in Persian text mining by increasing efficiency and accuracy.

Opinion Mining, Sentiment Analysis, aspect-based sentiment analysis, lexical semantic disambiguation, WordNet