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EnglishIn the era of social media, the huge availability of digital data allows to develop several types of research in a wide range of fields. Such data is characterized by several advantages: reduced collection costs, short retrieval times and production of almost real-time outputs. At the same time, this data is unstructured and unclassified in terms of con-tent. This study aims to develop an efficient way to filter and analyze tweets by means of sentiment related to a specific topic.
(2020). Semiautomatic dictionary-based classification of environment tweets by topic . Retrieved from http://hdl.handle.net/10446/168948
| Titolo: | Semiautomatic dictionary-based classification of environment tweets by topic | |
| Tutti gli autori: | Cameletti, Michela; Schlosser, Stephan; Toninelli, Daniele; Fabris, Silvia | |
| Data di pubblicazione: | 2020 | |
| Abstract: | In the era of social media, the huge availability of digital data allows to develop several types of research in a wide range of fields. Such data is characterized by several advantages: reduced collection costs, short retrieval times and production of almost real-time outputs. At the same time, this data is unstructured and unclassified in terms of con-tent. This study aims to develop an efficient way to filter and analyze tweets by means of sentiment related to a specific topic. | |
| Nelle collezioni: | 1.4.03 Testi di poster in atti di convegno - Conference posters |
File allegato/i alla scheda:
| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| Schlosser_276.pdf | Poster (final presented version) | publisher's version - versione editoriale | Licenza default Aisberg | Open AccessVisualizza/Apri |
| GOR20_ConferenceProceedings_Abstract.pdf | Abstract (final published version) | publisher's version - versione editoriale | Licenza default Aisberg | Open AccessVisualizza/Apri |
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