Disambiguating company names in microblog text using clustering for online reputation management
Twitter is used by millions of users to publish brief messages (tweets) with the purpose of sharing experiences and/or opinions about a product or service. There is a clear need for systems that can mine these messages in order to derive information about the collective thinking of twitterers (e.g....
Guardado en:
| Autores principales: | , , , |
|---|---|
| Lenguaje: | English |
| Publicado: |
Pontificia Universidad Católica de Valparaíso. Instituto de Literatura y Ciencias del Lenguaje
2015
|
| Materias: | |
| Acceso en línea: | http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-09342015000100003 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| Sumario: | Twitter is used by millions of users to publish brief messages (tweets) with the purpose of sharing experiences and/or opinions about a product or service. There is a clear need for systems that can mine these messages in order to derive information about the collective thinking of twitterers (e.g. for opinion or sentiment analysis). Tweet analysis is a very important task because comments, opinions, suggestions, complaints etc. can be used for marketing strategies or for determining information on a company’s reputation. For this purpose, it is necessary to automatically establish whether a tweet refers to a company or not, when the company name is ambiguous. This task is not a straightforward keyword search process as there may be multiple contexts in which a name can be used. The aim of this study is to present and compare four different approaches which improve the representation of short texts for better performance of the clustering task that determine whether a given tweet refers to a particular company or not. For this purpose, we have used a variety of enriching methodologies based on term expansion via the semantic similarity hidden behind the lexical structure, in order to improve the representation of tweets and as a consequence the performance of the task. We have used two different tweet datasets of company names which contain different levels of ambiguity. The results are promising although they highlight the difficulty of this task. |
|---|