Sentiment analysis in telecommunications: a systematic review of applications and challenges

Abstract

The increasing amount of digital content on online platforms creates both opportunities and challenges for telecommunications operators aiming to improve customer experience. Sentiment analysis (SA), a subfield of natural language processing (NLP), allows for the extraction of subjective information from data generated by users. This paper presents a systematic literature review (SLR), conducted in accordance with Kitchenham’s guidelines, examining the applications and challenges of SA within the telecommunications sector. A structured methodology was used to select and analyze over 100 studies published between 2017 and 2025. The review categorizes SA methods and evaluates their application in practical telecom contexts, including customer satisfaction assessment, churn prediction, service monitoring, and reputation management. Findings indicate that machine learning and deep learning models can achieve performance levels up to 97% in specific experimental contexts. However, such results may vary depending on the datasets, evaluation protocols, and application contexts. Key challenges identified include handling informal language, domain dependency, multilingualism, sarcasm detection. Finally, the review highlights future directions such as real-time sentiment tracking, multimodal analysis, and the integration of federated learning for privacy-preserving customer analytics. This review provides a foundational reference for researchers and practitioners aiming to deploy effective sentiment-driven systems in the telecom industry.