著作: [松本 和幸]/[任 福継]/[吉田 稔]/[北 研二]/Review Score Estimation Based on Transfer Learning of Different Media Review Data/Proceedings of The 12th International Conference on Natural Language Processing and Knowledge Engineering(NLP-KE'17)
(英) Review Score Estimation Based on Transfer Learning of Different Media Review Data
(英) In this paper, we proposed a model to classify reviews based on review data of different media. Recently, researches have been actively made on transfer learning between different domains with various kinds of big data as a target. Evaluation expressions are usually different in different domains, and this becomes a barrier for reputation analysis. Users usually use different linguistic expressions to refer to the creative works of each media. For example, media such as "anime", "comics", "games" and "movies" have different terms or expressions to be described their creative works. These differences can be considered showing features of each medium and such differences should be found in other expressions as well as in evaluation expressions. We analyzed what effect such differences would cause to classication accuracy by conducting transfer learning between review data ofdifferent media. In this paper, we proved compatibility between original (pre-transferring) and target (post-transferring) media for each medium of the creative works by constructing a review classication model. As a result of evaluation experiments, we could more accurately estimate review score without using SO-Score for training of review fragment based on Long Short Term Memory (LSTM) than the SO-Score based method.
(英) Proceedings of The 12th International Conference on Natural Language Processing and Knowledge Engineering(NLP-KE'17)
|年月日||必須||2017年 12月 7日|