An NLP approach to quantify dynamic salience of predefined topics in a text corpus

• URL: http://arxiv.org/abs/2108.07345v1
• Date: Mon, 16 Aug 2021 21:00:06 GMT
• Title: An NLP approach to quantify dynamic salience of predefined topics in a text corpus
• Authors: A. Bock, A. Palladino, S. Smith-Heisters, I. Boardman, E. Pellegrini, E.J. Bienenstock, A. Valenti
• Abstract summary: We use natural language processing techniques to quantify how a set of pre-defined topics of interest change over time across a large corpus of text. We find that given a predefined topic, we can identify and rank sets of terms, or n-grams, that map to those topics and have usage patterns that deviate from a normal baseline.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The proliferation of news media available online simultaneously presents a valuable resource and significant challenge to analysts aiming to profile and understand social and cultural trends in a geographic location of interest. While an abundance of news reports documenting significant events, trends, and responses provides a more democratized picture of the social characteristics of a location, making sense of an entire corpus to extract significant trends is a steep challenge for any one analyst or team. Here, we present an approach using natural language processing techniques that seeks to quantify how a set of pre-defined topics of interest change over time across a large corpus of text. We found that, given a predefined topic, we can identify and rank sets of terms, or n-grams, that map to those topics and have usage patterns that deviate from a normal baseline. Emergence, disappearance, or significant variations in n-gram usage present a ground-up picture of a topic's dynamic salience within a corpus of interest.

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