  This paper introduces a large collection of time series data derived from
Twitter, postprocessed using word embedding techniques, as well as specialized
fine-tuned language models. This data comprises the past five years and
captures changes in n-gram frequency, similarity, sentiment and topic
distribution. The interface built on top of this data enables temporal analysis
for detecting and characterizing shifts in meaning, including complementary
information to trending metrics, such as sentiment and topic association over
time. We release an online demo for easy experimentation, and we share code and
the underlying aggregated data for future work. In this paper, we also discuss
three case studies unlocked thanks to our platform, showcasing its potential
for temporal linguistic analysis.
