Affiliations: [a] DICIS, Universidad de Guanajuato, GTO, Mexico
| [b] Department of Systems, Universidad Autónoma Metropolitana, Unidad Azcapotzalco, CDMX, Mexico
| [c] DCI, Universidad de Guanajuato Campus León, GTO, Mexico
Correspondence:
[*]
Corresponding author. Rafael Guzmán-Cabrera, DICIS, Universidad de
Guanajuato Campus, GTO, Mexico. E-mail: guzmanc@ugto.mx.
Abstract: It is increasingly common for internet users to have access to blogs and social networks, and common for them to express opinions on such sites. This research work is framed within the scope of opinion mining. Opinions allow us to measure people’s perception of a specific topic or product. Knowing the opinion that a person has towards a product or service is of great help for decision making, since it allows, between other things, that potential consumers to verify the quality of the product or service before using it. This research work is framed within the scope of opinion mining. When the number of opinions is very large the analysis gets more complicated and generally resort to tools that allow this task to be performed automatically are sought out. The present work performs an automatic categorization of textual opinions corresponding to four products: books, DVDs, kitchens, and electronics. Both negative and positive opinions are considered for the experiment. Further categorization experiments are performed using different domains of learning. The basic idea is to investigate if we can undertake classification of opinions, positive and negative, of any given domain using instances of training from a different domain. Results obtained from different methods of learning are presented. The results obtained allow us to examine the feasibility of the proposed methodology.
