Characterization of citizens using word2vec and latent topic analysis in a large set of tweets
arXiv:1904.08926 · doi:10.1016/j.cities.2019.03.019
Abstract
With the increasing use of the Internet and mobile devices, social networks are becoming the most used media to communicate citizens' ideas and thoughts. This information is very useful to identify communities with common ideas based on what they publish in the network. This paper presents a method to automatically detect city communities based on machine learning techniques applied to a set of tweets from Bogotá's citizens. An analysis was performed in a collection of 2,634,176 tweets gathered from Twitter in a period of six months. Results show that the proposed method is an interesting tool to characterize a city population based on a machine learning methods and text analytics.
References in corpus (7)
- Scikit-learn: Machine Learning in Python
- Distributed Representations of Words and Phrases and their Compositionality
- Community detection algorithms: a comparative analysis
- word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method
- A Comparative Analysis of Community Detection Algorithms on Artificial Networks
- Notes on Noise Contrastive Estimation and Negative Sampling
- Swivel: Improving Embeddings by Noticing What's Missing