  Toxic sentiment analysis on Twitter (X) often focuses on specific topics and
events such as politics and elections. Datasets of toxic users in such research
are typically gathered through lexicon-based techniques, providing only a
cross-sectional view. his approach has a tight confine for studying toxic user
behavior and effective platform moderation. To identify users consistently
spreading toxicity, a longitudinal analysis of their tweets is essential.
However, such datasets currently do not exist.
  This study addresses this gap by collecting a longitudinal dataset from 143K
Twitter users, covering the period from 2007 to 2021, amounting to a total of
293 million tweets. Using topic modeling, we extract all topics discussed by
each user and categorize users into eight groups based on the predominant topic
in their timelines. We then analyze the sentiments of each group using 16 toxic
scores. Our research demonstrates that examining users longitudinally reveals a
distinct perspective on their comprehensive personality traits and their
overall impact on the platform. Our comprehensive dataset is accessible to
researchers for additional analysis.
