The availability of unstructured big data, such as the ones produced by social media, highlights the increasing methodological interest on text analysis and on the linked pre-processing phases. Several works have recently studied the impact of different pre-processing treatments on text classification. This aspect has been rarely studied when the target of the research is the definition of a topic-oriented dictionary that could be used to select messages regarding a certain topic among a wide group of unlabelled texts. The latter is a crucial phase: carefully filtering messages is a key aspect to start and to properly develop any type of textual analysis. In this paper, we aim at setting up a dictionary regarding environment. Starting from a verified list of Twitter Official Social Accounts, we evaluate if and how different pre-processing treatments (and their combination) can affect the final dictionary.

(2021). Impact of Tweets Pre-processing Techniques on a Dictionary for Environment . Retrieved from http://hdl.handle.net/10446/206062

Impact of Tweets Pre-processing Techniques on a Dictionary for Environment

Toninelli, Daniele;Cameletti, Michela;
2021-01-01

Abstract

The availability of unstructured big data, such as the ones produced by social media, highlights the increasing methodological interest on text analysis and on the linked pre-processing phases. Several works have recently studied the impact of different pre-processing treatments on text classification. This aspect has been rarely studied when the target of the research is the definition of a topic-oriented dictionary that could be used to select messages regarding a certain topic among a wide group of unlabelled texts. The latter is a crucial phase: carefully filtering messages is a key aspect to start and to properly develop any type of textual analysis. In this paper, we aim at setting up a dictionary regarding environment. Starting from a verified list of Twitter Official Social Accounts, we evaluate if and how different pre-processing treatments (and their combination) can affect the final dictionary.
2021
Salvatore, Camilla; Toninelli, Daniele; Cameletti, Michela; Schlosser, Stephan
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10446/206062
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