SATEK

@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}
}

@inproceedings{Verma:Mehler:2026,
  title     = {Predicting Topic (Co-)Occurrence Using Topic Networks Built from
               the Project Gutenberg Corpus},
  booktitle = {Proceedings of the 15th International Conference on Language Resources
               and Evaluation (LREC 2026)},
  year      = {2026},
  author    = {Verma, Bhuvanesh and Mehler, Alexander},
  keywords  = {Topic Evolution, Topic Network,Time-aware Networks, Temporal Autocorrelation, Project Gutenberg, satek},
  abstract  = {Although temporal topic modeling has been widely applied to scientific
               and legal texts, literary corpora have largely been overlooked
               in this regard. To address this issue, we analyze topic evolution
               in a subset of the Project Gutenberg (PG) corpus. We model this
               subset as a sequence of topic networks that capture the emergence,
               persistence, and interaction of thematic structures over decades.
               Using supervised topic representations, we predict nodes (topics)
               and edges (topic pairings) to forecast future topics and their
               co-occurrence. Our experiments demonstrate moderate to strong
               temporal persistence in topic connectivity patterns across three
               topic systems, with ROC-AUC and AP values consistently above 0.85.
               We find that the temporal span of topic networks significantly
               impacts predictive performance: longer spans improve the stability
               and recall of topic presence, while shorter spans better capture
               evolving topic relationships. Overall, our findings demonstrate
               the predictability of topics in literary texts over time.},
  note      = {accepted}
}

@inproceedings{Verma:et:al:2026,
  title     = {Predicting Convincingness in Political Speech: How Emotional Tone
               Shapes Persuasive Strength},
  booktitle = {Proceedings of the 15th Workshop on Computational Approaches to
               Subjectivity, Sentiment, \& Social Media Analysis},
  year      = {2026},
  author    = {Verma, Bhuvanesh and Marreddy, Mounika and Mehler, Alexander},
  keywords  = {Argument Detection, Argument Quality Assessment,Topic Modelling, Persuasiveness, Convincingness, Emotion Analysis, Argument Mining, satek},
  abstract  = {Emotional tone plays a central role in persuasion, yet its impact
               on computational assessments of political argument quality in
               real world election campaign speeches remains understudied. In
               this work, we investigate whether positive emotional framing correlates
               with higher perceived convincingness in political arguments. We
               fine-tune language models on argument quality datasets and test
               their ability to transfer convincingness predictions to real-world
               campaign speeches. Using a corpus of U.S. presidential campaign
               speeches, we analyze emotional polarity in relation to predicted
               persuasive strength to test whether positively framed arguments
               are judged more convincing than neutral or negative ones. Our
               empirical analysis shows that political parties rely heavily on
               argumentation during their election campaigns. Also, we found
               the evidence that politicians strategically employ emotional cues
               within their arguments during these campaign speeches, with positive
               emotions being more strongly associated with persuasive strength,
               for example in topics such as USMCA’s Effect on American Jobs
               and Agriculture, Border Control Policies, Progressive Tax Reforms.
               At the same time, we find that negative emotions have a weaker
               yet still non-negligible influence on voter persuasion in topics
               such as City Crime and Civil Unrest and White Supremacist Violence
               (Charlottesville Incident).},
  note      = {accepted}
}