Técnicas para el análisis del sentimiento en Twitter: Aprendizaje Automático Supervisado y SentiStrength
[EN] Sentiment analysis on Twitter offers possibilities of great interest to evaluate the currents of opinion disseminated through this medium. The huge volumes of texts require tools able to automatically process these messages without losing reliability. This paper describes two different types of...
| Autor: | |
|---|---|
| Formato: | artículo |
| Fecha de publicación: | 2017 |
| País: | España |
| Recursos: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | español |
| OAI Identifier: | oai:riunet.upv.es:10251/153230 |
| Acesso em linha: | https://riunet.upv.es/handle/10251/153230 |
| Access Level: | acceso abierto |
| Palavra-chave: | Comunicación política Análisis de sentimiento Aprendizaje automático supervisado SentiStrength Political communication Sentiment analysis Supervised learning COMERCIALIZACION E INVESTIGACION DE MERCADOS |
| Resumo: | [EN] Sentiment analysis on Twitter offers possibilities of great interest to evaluate the currents of opinion disseminated through this medium. The huge volumes of texts require tools able to automatically process these messages without losing reliability. This paper describes two different types of approaching this problem. The first strategy is based on Supervised Learning processes, developed in the field of artificial intelligence. Its application requires some tools from natural language processing along with a classifed corpus as a starting point. The second approach is based on polarity dictionaries. SentiStrength tool is located in this line. It is increasingly applied to studies of Twitter in English. The paper assesses the most advanced studies using each of these approaches for analyzing tweets in Spanish. Finally, the advantages and limitations of each of these approaches for researching political communication are assessed. While supervised learning allows taking into account the context thanks to its ability to detect patterns of words, the researcher who uses this approach requires having data analyst skills to better refine the process. Instead, SentiStrength is more oriented to the semantic content of the terms of the message. It requires more of a competence in linguistics by the researcher. The main conclusion of this study is that both automated methods of analysis can not do without a demanding manual coding if they are to be used reliably in research. |
|---|