| Revista: | Computación y sistemas |
| Base de datos: | PERIÓDICA |
| Número de sistema: | 000379429 |
| ISSN: | 1405-5546 |
| Autores: | Ben-Ami, Zvi1 Feldman, Ronen2 Rosenfeld, Binyamin2 |
| Instituciones: | 1Hebrew University of Jerusalem, School of Business Administration, Jerusalén. Israel 2Digital Trowel, Nueva York. Estados Unidos de América |
| Año: | 2014 |
| Periodo: | Jul-Sep |
| Volumen: | 18 |
| Número: | 3 |
| Paginación: | 477-490 |
| País: | México |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Enfoque: | Analítico, descriptivo |
| Resumen en inglés | Stock-related messages on social media have several interesting properties regarding the sentiment analysis (SA) task. On the one hand, the analysis is particularly challenging, because of frequent typos, bad grammar, and idiosyncratic expressions specific to the domain and media. On the other hand, stock-related messages primarily refer to the state of specific entities – companies and their stocks, at specific times (of sending). This state is an objective property and even has a measurable numeric characteristic, namely, the stock price. Given a large dataset of twitter messages, we can create two separate "views" on the dataset by analyzing text of messages and external properties separately. With this, we can expand the coverage of generic SA tools and learn new sentiment expressions. In this paper, we experiment with this learning method, comparing several types of general SA tools and sets of external properties. The method is shown to produce significant improvement in accuracy |
| Disciplinas: | Ciencias de la computación, Literatura y lingüística |
| Palabras clave: | Inteligencia artificial, Lingüística computacional, Redes sociales, Minería de texto, Procesamiento de lenguaje natural, Análisis de sentimiento, Aprendizaje no supervisado |
| Keyword: | Computer science, Literature and linguistics, Artificial intelligence, Computing linguistics, Social networks, Text mining, Natural language processing, Sentiment analysis, Unsupervised learning |
| Texto completo: | Texto completo (Ver HTML) |
