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Mounika Marreddy

Mounika Marreddy has 4 papers in our library, in 2026, most of them on Sentiment & emotion, Psycholinguistics & language acquisition; often with Alexander Mehler, Bhuvanesh Verma.

4 papers in our library · 2026

OpenAlex · ORCID · Wikidata · h-index 7 · 38 works · 114 citations OpenAlex, 2026-10-14

📄 4 works in our library · ⭐ Paper Stars ranking: #11801

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓Goethe University FrankfurtFrankfurt am Main · Germany2026 · 4 paperscurrent🎓International Institute of Information Technology, HyderabadHyderabad · India2019–2025 · 19 papers🎓University of BonnBonn · Germany2024–2025 · 5 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 more institutions on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 9The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 4 worksLanguage and Linguistics · 4 worksArtificial Intelligence · 3 worksComputer Science · 3 worksExperimental and Cognitive Psychology · 1 workPhilosophy · 1 workPsychology · 1 workSocial Sciences · 1 workSociology and Political Science · 1 work

🧭 Topics 2Research subjects and languages the work is about.Sentiment & emotion · 2 worksPsycholinguistics & language acquisition · 1 work

 Works by year

2020s

20264 papers

 Papers

The most cited first (OpenAlex and our library).

  1. DualAxis AI at SemEval-2026 Task 3: Dimensional Aspect-Based Sentiment Analysis (2026)
    This paper tackles Dimensional Aspect-Based Sentiment Analysis (DimABSA) from SemEval-2026 Task 3. In this setting, sentiment toward an aspect is encoded as real-valued valence and arousal (each on a 1–9 scale) instead of a positive/negative label. The authors, a Goethe University Frankfurt team, build reproducible…
  2. Predicting Convincingness in Political Speech: How Emotional Tone Shapes Persuasive Strength (2026)
    The paper asks whether emotional tone is linked to how convincing political arguments appear to computational models, using real campaign rhetoric rather than debate or social-media data.
  3. SteerForce at SemEval-2026 Task 11: Reducing Content Effects Using Layered Activation Steering (2026)
    The paper addresses content effects in SemEval-2026 Task 11 (Subtask 1). In this task, systems judge whether English syllogisms are logically valid, but models tend to be swayed by whether the conclusion sounds believable. The authors treat this bias as a geometric offset between plausibility-driven and…
  4. TTLab at SemEval-2026 Task 10: Transformer-based Approaches for Psycholinguistic Conspiracy Detection in Social Media Discourse (2026)
    Conspiracy content on social media is hard to detect from vocabulary alone. It relies on psychological framing, such as blaming powerful agents or hinting at hidden motives. The TTLab team addressed this in SemEval-2026 Task 10 (PsyCoMark). The task's dataset contains Reddit submissions with document-level conspiracy…

 Co-authors here

2 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

Updated

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