# Persian topic detection based on Human Word association and graph embedding

arXiv: https://arxiv.org/abs/2302.09775 · HTML: https://mlsanity.com/paper/2302.09775/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 31 references that are in the map.
- First posted on arXiv 20 Feb 2023.

## Builds on (most cited first, 7 of 7)

- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Graph Embedding Techniques, Applications, and Performance: A Survey](https://mlsanity.com/paper/1705.02801/) (2017)
- [Covid-Transformer: Detecting COVID-19 Trending Topics on Twitter Using Universal Sentence Encoder](https://mlsanity.com/paper/2009.03947/) (2020)
- [Event Detection in Twitter Stream using Weighted Dynamic Heartbeat Graph Approach](https://mlsanity.com/paper/1902.08522/) (2019)
- [A Human Word Association based model for topic detection in social networks](https://mlsanity.com/paper/2301.13066/) (2023)

## Built on by (most cited first, 1 of 1)

- [A comprehensive study on Frequent Pattern Mining and Clustering categories for topic detection in Persian text stream](https://mlsanity.com/paper/2403.10237/) (2024)

## Abstract (arXiv)

In this paper, we propose a framework to detect topics in social media based on Human Word Association. Identifying topics discussed in these media has become a critical and significant challenge. Most of the work done in this area is in English, but much has been done in the Persian language, especially microblogs written in Persian. Also, the existing works focused more on exploring frequent patterns or semantic relationships and ignored the structural methods of language. In this paper, a topic detection framework using HWA, a method for Human Word Association, is proposed. This method uses the concept of imitation of mental ability for word association. This method also calculates the Associative Gravity Force that shows how words are related. Using this parameter, a graph can be generated. The topics can be extracted by embedding this graph and using clustering methods. This approach has been applied to a Persian language dataset collected from Telegram. Several experimental studies have been performed to evaluate the proposed framework's performance. Experimental results show that this approach works better than other topic detection methods.