# Using meaning instead of words to track topics

arXiv: https://arxiv.org/abs/2301.00565 · HTML: https://mlsanity.com/paper/2301.00565/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 13 references that are in the map.
- First posted on arXiv 2 Jan 2023.

## Builds on (most cited first, 2 of 2)

- [What is Wrong with Topic Modeling? (and How to Fix it Using Search-based Software Engineering)](https://mlsanity.com/paper/1608.08176/) (2016)
- [HTMOT : Hierarchical Topic Modelling Over Time](https://mlsanity.com/paper/2112.03104/) (2021)

## Abstract (arXiv)

The ability to monitor the evolution of topics over time is extremely valuable for businesses. Currently, all existing topic tracking methods use lexical information by matching word usage. However, no studies has ever experimented with the use of semantic information for tracking topics. Hence, we explore a novel semantic-based method using word embeddings. Our results show that a semantic-based approach to topic tracking is on par with the lexical approach but makes different mistakes. This suggest that both methods may complement each other.