@article{ImrKelGomPeerJCS2026,
  title = {A syntax-injected approach for faster and more accurate sentiment analysis},
  author = {Imran, Muhammad and Kellert, Olga and G\'{o}mez-Rodr\'{i}guez, Carlos},
  journal = {PeerJ Computer Science},
  volume = {12},
  pages = {e3519},
  year = {2026},
  month = {jan},
  day = {30},
  publisher = {PeerJ Inc.},
  issn = {2376-5992},
  doi = {10.7717/peerj-cs.3519},
  url = {https://doi.org/10.7717/peerj-cs.3519},
  keywords = {Sentiment analysis, Sequence labeling parsing, Syntactic knowledge, Opinion mining},
  abstract = {Sentiment Analysis (SA) is a crucial aspect of Natural Language Processing (NLP), focusing on identifying and interpreting subjective assessments in textual content. Syntactic parsing is useful in SA as it improves accuracy and provides explainability; however, it often becomes a computational bottleneck due to slow parsing algorithms. This article proposes a solution to this bottleneck by using a Sequence Labeling Syntactic Parser (SELSP) to integrate syntactic information into SA via a rule-based sentiment analysis pipeline...}
}