Temporal Analysis on Topics Using Word2Vec

TL;DR

This paper introduces a new method for analyzing how topics evolve over time by modeling their movement using Word2Vec, clustering, and similarity measures, providing insights into trend direction and topic convergence or divergence.

Contribution

It presents a novel approach combining Word2Vec, clustering, and similarity analysis to visualize and detect topic trends and their directional changes over time.

Findings

Effective visualization of topic trends over time

Detection of convergent and divergent topic behaviors

Application demonstrated on 20 Newsgroups dataset

Abstract

The present study proposes a novel method of trend detection and visualization - more specifically, modeling the change in a topic over time. Where current models used for the identification and visualization of trends only convey the popularity of a singular word based on stochastic counting of usage, the approach in the present study illustrates the popularity and direction that a topic is moving in. The direction in this case is a distinct subtopic within the selected corpus. Such trends are generated by modeling the movement of a topic by using k-means clustering and cosine similarity to group the distances between clusters over time. In a convergent scenario, it can be inferred that the topics as a whole are meshing (tokens between topics, becoming interchangeable). On the contrary, a divergent scenario would imply that each topics' respective tokens would not be found in the same context (the words are increasingly different to each other). The methodology was tested on a group of articles from various media houses present in the 20 Newsgroups dataset.