New publication at OSACT7 202

@inproceedings{Abusaleh:et:al:2026:sarf,
  title     = {TTLab at AraSentEval: SARF (صرف) Sentiment Analysis via Root-based
               Fusion for Multi-Dialectal Arabic},
  author    = {Abusaleh, Ali and Verma, Bhuvanesh and Mehler, Alexander},
  booktitle = {Proceedings of the 7th Workshop on Open-Source Arabic Corpora
               and Processing Tools (OSACT7), co-located with the Language Resources
               and Evaluation Conference (LREC 2026)},
  eventdate = {May, 2026},
  location  = {Palma, Mallorca, Spain},
  year      = {2026},
  keywords  = {NLP, Sentiment Analysis, Arabic analysis, new-data-spaces, circlet, satek},
  abstract  = {Arabic sentiment analysis is challenged by morphological complexity
               and lexical variation across Arabic dialects, compounded by subjectivity
               in how speakers and writers express sentiment. In this paper,
               we present our submission for the AraSentEval 2026 Shared Task
               on Arabic Dialect Sentiment Analysis. We propose SARF (صرف) a
               multi-view architectural framework that integrates surface-level
               context with stemmed and rooted morphological perspectives using
               a shared MARBERTv2 encoder. Our system employs a hybrid BERT-CNN-BiLSTM-Attention
               architecture to capture both local sentiment n-grams and global
               sequential dependencies. Experimental results show that while
               individual morphological normalization strategies (stemming or
               rooting) may degrade performance, their joint integration via
               cross-morphological attention provides robust features across
               diverse dialects. Our final system achieved a competitive macro-F1-score
               of 0.9263, ranking 2nd out of 15 participating teams.},
  note      = {accepted}
}