Unveiling New Insights From Textual Unstructured Big Data in Politics Through Deep Learning
[EN] Over the past decade, social media platforms have undergone significant and rapid expansion. One of the key challenges has been effectively analysing the vast amount of unstructured user-generated data they produce. This research delves into the analysis of Italian Twitter data through the appl...
| Autores: | , , , , |
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| Tipo de recurso: | capítulo de libro |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:riunet.upv.es:10251/208677 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/208677 |
| Access Level: | acceso abierto |
| Palabra clave: | Politics Deep learning Artificial intelligence Big data Statistics Sentiment |
| Sumario: | [EN] Over the past decade, social media platforms have undergone significant and rapid expansion. One of the key challenges has been effectively analysing the vast amount of unstructured user-generated data they produce. This research delves into the analysis of Italian Twitter data through the application of advanced deep learning models across three primary objectives: text classification, sentiment analysis, and hate analysis. Five cutting-edge models are evaluated, each utilizing distinct word embeddings.Furthermore, this study investigates the effects of processing emojis and emoticons in Italian tweets on sentiment and hate analysis. We compare model performances and suggest optimized approaches for each task. Finally, we apply these methodologies to real-world Twitter data and present our findings through multiple graphs and statistical analyses. This study demonstrates the possibility of extracting new insights and novel information from unstructured textual Big Data in Politics. |
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