Expressivity of Tweets on Social Issues Using Aspect Based Text Classification

Social discussions about crime on Twitter and open forums aims to understand the barriers that hinder people from expressing their concerns or aligning with popular opinions. A curated dataset spanning three months in 2023 was collected, covering categories like crimes Gender Equality Violence Against Women.
 The study employs aspect-based sentiment analysis classify polarity tweets, utilising a comprehensive framework involving text feature classification stages. initial stage analyses individual words, phrases, tweet patterns features based specific linguistic elements. In subsequent step, semantic relations explore better understanding of core infer relationships between different keywords. This enhances by considering meaning contextual nuances language used tweets. final incorporates transformer-based models for effective multilabel view diversity present dataset. study's quantitative reveals Ensemble learning approach demonstrates an impressive precision measure 93%. By integrating stages classification, accuracy comprehensiveness social Twitter.