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arXiv · 2209.11717

Temporal Analysis on Topics Using Word2Vec

Angad Sandhu·Aneesh Edara·Vishesh Narayan·Faizan Wajid·Ashok Agrawala
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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.

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Angad Sandhu, Aneesh Edara, Vishesh Narayan, Faizan Wajid, Ashok Agrawala. 2022-09-23. Temporal Analysis on Topics Using Word2Vec. https://arxiv.org/abs/2209.11717

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