  In this thesis, we propose an approach to identity resolution across social
media platforms using the topics, sentiments, and timings of the posts on the
platforms. After collecting the public posts of around 5000 profiles from
Disqus and Twitter, we analyze their posts to match their profiles across the
two platforms. We pursue both temporal and non-temporal methods in our
analysis. While neither approach proves definitively superior, the temporal
approach generally performs better. We found that the temporal window size
influences results more than the shifting amount. On the other hand, our
sentiment analysis shows that the inclusion of sentiment makes little
difference, probably due to flawed data extraction methods. We also
experimented with a distance-based reward-and-punishment-focused scoring model,
which achieved an accuracy of 24.198% and an average rank of 158.217 out of
2525 in our collected corpus. Future work includes refining sentiment analysis
by evaluating sentiments per topic, extending temporal analysis with additional
phases, and improving the scoring model through weight adjustments and modified
rewards.
