This paper presents the NIT-Agartala-NLPTeam’s submission to SemEval-2026 Task 9on polarization detection in textual data. Thetask comprises two subtasks: (i) binary classification to distinguish polarized from nonpolarized content, and (ii) multi-label classification to identify the specific type(s) of polarization. We propose a weighted soft-votingensemble framework that integrates multiplefine-tuned large language models (LLMs). Theprobabilistic outputs of the individual models are combined using weighted averagingto effectively leverage their complementarystrengths and enhance overall performance.Our system achieved a test macro F1-score of78.6 (26th out of 44 teams) in Subtask 1 and46.0 (18th out of 29 teams) in Subtask 2.

Topic modeling is a widely used technique to analyze large document corpora. With the ever-growing emergence of scientific contributions in the field, non-technical users may often use the simplest available software module, independent of whether there are potentially better models available. We present a Simplified Topic Retrieval, Exploration, and Analysis Module (STREAM) for user-friendly topic modelling and especially subsequent interactive topic visualization and analysis. For better topic analysis, we implement multiple intruder-word based topic evaluation metrics. Additionally, we publicize multiple new datasets that can extend the so far very limited number of publicly available benchmark datasets in topic modeling. We integrate downstream interpretable analysis modules to enable users to easily analyse the created topics in downstream tasks together with additional tabular information.The code is available at the following link: https://github.com/AnFreTh/STREAM