On Validity of Sentiment Analysis Scores and Development of Classification Model for Student-Lecturer Comments Using Weight-based Approach and Deep Learning
{"title":"On Validity of Sentiment Analysis Scores and Development of Classification Model for Student-Lecturer Comments Using Weight-based Approach and Deep Learning","authors":"Ochilbek Rakhmanov","doi":"10.1145/3368308.3415361","DOIUrl":null,"url":null,"abstract":"In this paper, a novel state-of-art classification method was presented for student-lecturer comment classification. Tf-Idf was used to assign weights for each word and several different ANN structures were tested. A large dataset, 52571 comments, was used during training. The results show that developed models clearly overperformed existing classification models in this field. 97% of prediction accuracy was achieved on 3-class dataset, while the prediction accuracy for 5-class dataset was 92%.","PeriodicalId":374890,"journal":{"name":"Proceedings of the 21st Annual Conference on Information Technology Education","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 21st Annual Conference on Information Technology Education","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3368308.3415361","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
In this paper, a novel state-of-art classification method was presented for student-lecturer comment classification. Tf-Idf was used to assign weights for each word and several different ANN structures were tested. A large dataset, 52571 comments, was used during training. The results show that developed models clearly overperformed existing classification models in this field. 97% of prediction accuracy was achieved on 3-class dataset, while the prediction accuracy for 5-class dataset was 92%.