Sentiment analysis, the process of gauging user attitudes and emotions through their textual data, including social media posts and other forms of communication, is a valuable tool for informed decision-making. In other words, a statement conveys positivity, negativity, or neutrality, sentiment analysis offers insights into public sentiment regarding a product, individual, event, or other significant topics. This research focuses on the effectiveness of sentiment analysis techniques, using Machine Learning (ML) and Natural Language Processing (NLP) especially pre-trained language models for Persian, in assessing users’ satisfaction with their partner, using data collected from X (formerly Twitter). Our motivation stems from traditional in-person surveys, which periodically analyze societal challenges in Iran. The limitations of these surveys led us to explore Artificial Intelligence (AI) as an alternative solution for addressing contemporary social issues. We collected Persian tweets and utilized data annotation techniques to label them according to our research question, forming the dataset. Our goal also was to provide a benchmark of Persian tweets on this specific topic. To evaluate our dataset, we employed several classification methods to achieve our goal, including classical ML models, Deep Neural Networks, and pre-trained language models for Persian. Following a comprehensive evaluation, our results show that BERTweet-FA (one of the pre-trained language models for Persian) emerged as the best performer among the classifiers for assessing users’ satisfaction. This point indicates the ability of language models to understand conversational Persian text and perform sentiment analysis, even in a low-resource language like Persian.