Subjectivity analysis and authorship attribution are very popular areas of research. However, work in these two areas has been done separately. We believe that by combining information about subjectivity in texts and authorship, the performance of both tasks can be improved. In the paper a personalized approach to opinion mining is presented, in which the notions of personal sense and idiolect are introduced; the approach is applied to the polarity classification task. It is assumed that different authors express their private states in text individually, and opinion mining results could be improved by analyzing texts by different authors separately. The hypothesis is tested on a corpus of movie reviews by ten authors. The results of applying the personalized approach to opinion mining are presented, confirming that the approach increases the performance of the opinion mining task. Automatic authorship attribution is further applied to model the personalized approach, classifying documents by their assumed authorship. Although the automatic authorship classification imposes a number of limitations on the dataset for further experiments, after overcoming these issues the authorship attribution technique modeling the personalized approach confirms the increase over the baseline with no authorship information used.
Язык оригиналаанглийский
Название основной публикацииLREC 2010 Proceedings
Страницы1134-1137
СостояниеОпубликовано - 2010
СобытиеSeventh International Conference on Language Resources and Evaluation - Valletta, Мальта
Продолжительность: 17 мая 201023 мая 2010

конференция

конференцияSeventh International Conference on Language Resources and Evaluation
Сокращенное названиеLREC 2010
Страна/TерриторияМальта
ГородValletta
Период17/05/1023/05/10

ID: 4687592