| Revista: | Computación y sistemas |
| Base de datos: | |
| Número de sistema: | 000560789 |
| ISSN: | 1405-5546 |
| Autores: | Bhagat, Pradnya1 Korkankar, Pratik D2 Pawar, Jyoti D1 |
| Instituciones: | 1Goa University, Goa Business School, Taleigao Plateau, Goa. India 2Dnyanprassarak Mandal’s College and Research Centre, Goa. India |
| Año: | 2023 |
| Periodo: | Abr-Jun |
| Volumen: | 27 |
| Número: | 2 |
| Paginación: | 389-399 |
| País: | México |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Resumen en inglés | Most of the user-preferred products on e-commerce websites are accompanied by a massive number of product reviews and manually analyzing each review to understand the features and the user opinions associated with the products is an inconceivable task. A single domain of products can contain thousands of different products and an equally significant number of associated product features/aspects, thereby making the polarity of the sentiment words in the product reviews vary widely according to the aspect with which they are associated. The paper uses the Chi-square Test statistical measure to automatically calculate the aspect-based polarity of sentiment words in a given domain. The results of the method are tested on two different domains. The experimental results show that the method delivers an accuracy of more than 75% in both domains. The method also helps in discovering strong domain-specific polar adjectives that might be missing in universal sentiment lexicons. |
| Disciplinas: | Ciencias de la computación |
| Palabras clave: | Procesamiento de datos |
| Keyword: | Data processing |
| Texto completo: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
