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A deep semantic matching approach for identifying relevant messages for social media analysis

There is a growing interest in using social media content for Natural Language Processing applications. However, it is not easy to computationally identify the most relevant set of tweets related to any specific event. Challenging semantics coupled with different ways for using natural language in s...

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Autores principales: Biggers, Frederick Brown, Mohanty, Somya D., Manda, Prashanti
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10368660/
https://www.ncbi.nlm.nih.gov/pubmed/37491443
http://dx.doi.org/10.1038/s41598-023-38761-y
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author Biggers, Frederick Brown
Mohanty, Somya D.
Manda, Prashanti
author_facet Biggers, Frederick Brown
Mohanty, Somya D.
Manda, Prashanti
author_sort Biggers, Frederick Brown
collection PubMed
description There is a growing interest in using social media content for Natural Language Processing applications. However, it is not easy to computationally identify the most relevant set of tweets related to any specific event. Challenging semantics coupled with different ways for using natural language in social media make it difficult for retrieving the most relevant set of data from any social media outlet. This paper seeks to demonstrate a way to present the changing semantics of Twitter within the context of a crisis event, specifically tweets during Hurricane Irma. These methods can be used to identify the most relevant corpus of text for analysis in relevance to a specific incident such as a hurricane. Using an implementation of the Word2Vec method of Neural Network training mechanisms to create Word Embeddings, this paper will: discuss how the relative meaning of words changes as events unfold; present a mechanism for scoring tweets based upon dynamic, relative context relatedness; and show that similarity between words is not necessarily static. We present different methods for training the vector model in Word2Vec for identification of the most relevant tweets for any search query. The impact of tuning parameters such as Word Window Size, Minimum Word Frequency, Hidden Layer Dimensionality, and Negative Sampling on model performance was explored. The window containing the local maximum for AU_ROC for each parameter serves as a guide for other studies using the methods presented here for social media data analysis.
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spelling pubmed-103686602023-07-27 A deep semantic matching approach for identifying relevant messages for social media analysis Biggers, Frederick Brown Mohanty, Somya D. Manda, Prashanti Sci Rep Article There is a growing interest in using social media content for Natural Language Processing applications. However, it is not easy to computationally identify the most relevant set of tweets related to any specific event. Challenging semantics coupled with different ways for using natural language in social media make it difficult for retrieving the most relevant set of data from any social media outlet. This paper seeks to demonstrate a way to present the changing semantics of Twitter within the context of a crisis event, specifically tweets during Hurricane Irma. These methods can be used to identify the most relevant corpus of text for analysis in relevance to a specific incident such as a hurricane. Using an implementation of the Word2Vec method of Neural Network training mechanisms to create Word Embeddings, this paper will: discuss how the relative meaning of words changes as events unfold; present a mechanism for scoring tweets based upon dynamic, relative context relatedness; and show that similarity between words is not necessarily static. We present different methods for training the vector model in Word2Vec for identification of the most relevant tweets for any search query. The impact of tuning parameters such as Word Window Size, Minimum Word Frequency, Hidden Layer Dimensionality, and Negative Sampling on model performance was explored. The window containing the local maximum for AU_ROC for each parameter serves as a guide for other studies using the methods presented here for social media data analysis. Nature Publishing Group UK 2023-07-25 /pmc/articles/PMC10368660/ /pubmed/37491443 http://dx.doi.org/10.1038/s41598-023-38761-y Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Biggers, Frederick Brown
Mohanty, Somya D.
Manda, Prashanti
A deep semantic matching approach for identifying relevant messages for social media analysis
title A deep semantic matching approach for identifying relevant messages for social media analysis
title_full A deep semantic matching approach for identifying relevant messages for social media analysis
title_fullStr A deep semantic matching approach for identifying relevant messages for social media analysis
title_full_unstemmed A deep semantic matching approach for identifying relevant messages for social media analysis
title_short A deep semantic matching approach for identifying relevant messages for social media analysis
title_sort deep semantic matching approach for identifying relevant messages for social media analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10368660/
https://www.ncbi.nlm.nih.gov/pubmed/37491443
http://dx.doi.org/10.1038/s41598-023-38761-y
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