عنوان انگلیسی مقاله:
ReviewModus: Text classification and sentiment prediction of unstructured reviews using a hybrid combination of machine learning and evaluation models
ترجمه فارسی عنوان مقاله:
ReviewModus: طبقه بندی متن و پیش بینی احساسات بررسی های بدون ساختار با استفاده از ترکیب هیبریدی از مدل های یادگیری و ارزیابی ماشین
Sciencedirect - Elsevier - Applied Mathematical Modelling, 71 (2019) 569-583: doi:10:1016/j:apm:2019:02:032
Fouad Zablith ∗, Ibrahim H. Osman
While research interest on product and service evaluation from unstructured text reviews is increasing, investigating the effectiveness of predictive analytical models in this context is still under-explored. With the advancement in machine learning research, an opportu- nity exists to bridge this gap using a model-based product and service evaluation. We pro- pose in this article ReviewModus , a text mining and processing framework that (1) relies on the model structure and its corresponding assessment questions to train a machine learning algorithm to predict the classification of reviews around the model dimensions; (2) predicts the sentiments within the reviews based on external review training datasets; and (3) transforms the extracted measures from the reviews for further analysis. Our ap- proach is evaluated in the context of 11 e-government services where the performance of the framework is compared to the manual processing of unstructured reviews cross- checked by three independent evaluators. Our study shows promising classification results with a micro-average F-score reaching 85.16%, and a high sentiment prediction correlation (71.44%) with the manually performed sentiment assessment.
Keywords: Machine learning | Text mining | Neural network | Logistic regression | e-government