Short Text Classification Using Contextual Analysis

Micro blogging tools provide a real time service for the public to express opinions, to broadcast news and information and offer an opportunity to comment and respond to such output. Word usage in social media is continually evolving. Micro bloggers may use different sets of words to describe a spec...

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Autores principales:	Sami Al Sulaimani, Andrew Starkey
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Text analysis event classification contextual analysis supervised machine learning Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/027ee14735574f97b30170336adaa229
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id	oai:doaj.org-article:027ee14735574f97b30170336adaa229
record_format	dspace
spelling	oai:doaj.org-article:027ee14735574f97b30170336adaa2292021-11-18T00:02:29ZShort Text Classification Using Contextual Analysis2169-353610.1109/ACCESS.2021.3125768https://doaj.org/article/027ee14735574f97b30170336adaa2292021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9605590/https://doaj.org/toc/2169-3536Micro blogging tools provide a real time service for the public to express opinions, to broadcast news and information and offer an opportunity to comment and respond to such output. Word usage in social media is continually evolving. Micro bloggers may use different sets of words to describe a specific event and they may use new words (i.e. neither exist in the training dataset nor in informal or formal dictionaries) or use words in new contexts. Dynamically capturing new words and their potential meaning from their context can help to reflect the words relationship in social media, which then can be useful for solving various problems, like the event classification task. Different approaches have been proposed in this regard, one of them is Contextual Analysis. This paper focuses on examining the potential of this approach for grouping short texts (tweets) talking about the same event into the same category. A new transparent method for text multi-class categorization is presented. It uses the Contextual Analysis approach to capture the most important words in the context of an event and to detect the usage of similar words in different contexts. In order to test the efficacy in these areas, this study evaluates the performance of the proposed method and other well known methods, such as Naïve Bayes, Support Vector Machines, K-Nearest Neighbors and Convolutional Neural Networks. On average, the experiments’ results show that the proposed multi-class classification method can effectively categorize tweets into various event groups, with a high f1-measure score f1>97.09% and f1>95.27%, in the imbalanced classes and high number of classes experiments, respectively. However, similar to the baseline methods, the performance is negatively influenced by the imbalanced dataset. The Convolutional Neural Networks method produces the best performance among the other algorithms with f1>97.74% in all experiments, which is 1.73% and 2.72% higher than the lowest performance of Naive Bayes and K-Nearest Neighbors, respectively, but does not meet the requirements of transparency of results.Sami Al SulaimaniAndrew StarkeyIEEEarticleText analysisevent classificationcontextual analysissupervised machine learningElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 149619-149629 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Text analysis event classification contextual analysis supervised machine learning Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Text analysis event classification contextual analysis supervised machine learning Electrical engineering. Electronics. Nuclear engineering TK1-9971 Sami Al Sulaimani Andrew Starkey Short Text Classification Using Contextual Analysis
description	Micro blogging tools provide a real time service for the public to express opinions, to broadcast news and information and offer an opportunity to comment and respond to such output. Word usage in social media is continually evolving. Micro bloggers may use different sets of words to describe a specific event and they may use new words (i.e. neither exist in the training dataset nor in informal or formal dictionaries) or use words in new contexts. Dynamically capturing new words and their potential meaning from their context can help to reflect the words relationship in social media, which then can be useful for solving various problems, like the event classification task. Different approaches have been proposed in this regard, one of them is Contextual Analysis. This paper focuses on examining the potential of this approach for grouping short texts (tweets) talking about the same event into the same category. A new transparent method for text multi-class categorization is presented. It uses the Contextual Analysis approach to capture the most important words in the context of an event and to detect the usage of similar words in different contexts. In order to test the efficacy in these areas, this study evaluates the performance of the proposed method and other well known methods, such as Naïve Bayes, Support Vector Machines, K-Nearest Neighbors and Convolutional Neural Networks. On average, the experiments’ results show that the proposed multi-class classification method can effectively categorize tweets into various event groups, with a high f1-measure score f1>97.09% and f1>95.27%, in the imbalanced classes and high number of classes experiments, respectively. However, similar to the baseline methods, the performance is negatively influenced by the imbalanced dataset. The Convolutional Neural Networks method produces the best performance among the other algorithms with f1>97.74% in all experiments, which is 1.73% and 2.72% higher than the lowest performance of Naive Bayes and K-Nearest Neighbors, respectively, but does not meet the requirements of transparency of results.
format	article
author	Sami Al Sulaimani Andrew Starkey
author_facet	Sami Al Sulaimani Andrew Starkey
author_sort	Sami Al Sulaimani
title	Short Text Classification Using Contextual Analysis
title_short	Short Text Classification Using Contextual Analysis
title_full	Short Text Classification Using Contextual Analysis
title_fullStr	Short Text Classification Using Contextual Analysis
title_full_unstemmed	Short Text Classification Using Contextual Analysis
title_sort	short text classification using contextual analysis
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/027ee14735574f97b30170336adaa229
work_keys_str_mv	AT samialsulaimani shorttextclassificationusingcontextualanalysis AT andrewstarkey shorttextclassificationusingcontextualanalysis
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Short Text Classification Using Contextual Analysis

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