Zabit Hameed, S. Shapoval, B. Garcia-Zapirain, Amaia Méndez Zorilla
{"title":"基于BiGRU模型的集成方法的情感分析——以AMIS推文为例","authors":"Zabit Hameed, S. Shapoval, B. Garcia-Zapirain, Amaia Méndez Zorilla","doi":"10.1109/ISSPIT51521.2020.9408866","DOIUrl":null,"url":null,"abstract":"This paper presents a comparably simpler yet effective deep learning approach for sentiment analysis of Twitter topics. We automatically collected positive and negative tweets and labeled them manually, and thus created a new dataset. We then leveraged BiGRU model with an ensemble approach for the binary classification of tweets. Our finalized BiGRU model offered an accuracy of 84.8% as well as an averaged F1-measure of 84.8%(±0.3). Moreover, the ensemble approach, using an averaged prediction of 5-fold strategy, provided the accuracy of 86.3% along with the averaged F1-measure of 86.3%(±0.05). Consequently, the ensemble approach offered better performance even on a smaller dataset used in this study.","PeriodicalId":111385,"journal":{"name":"2020 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Sentiment analysis using an ensemble approach of BiGRU model: A case study of AMIS tweets\",\"authors\":\"Zabit Hameed, S. Shapoval, B. Garcia-Zapirain, Amaia Méndez Zorilla\",\"doi\":\"10.1109/ISSPIT51521.2020.9408866\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a comparably simpler yet effective deep learning approach for sentiment analysis of Twitter topics. We automatically collected positive and negative tweets and labeled them manually, and thus created a new dataset. We then leveraged BiGRU model with an ensemble approach for the binary classification of tweets. Our finalized BiGRU model offered an accuracy of 84.8% as well as an averaged F1-measure of 84.8%(±0.3). Moreover, the ensemble approach, using an averaged prediction of 5-fold strategy, provided the accuracy of 86.3% along with the averaged F1-measure of 86.3%(±0.05). Consequently, the ensemble approach offered better performance even on a smaller dataset used in this study.\",\"PeriodicalId\":111385,\"journal\":{\"name\":\"2020 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)\",\"volume\":\"108 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-12-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISSPIT51521.2020.9408866\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISSPIT51521.2020.9408866","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Sentiment analysis using an ensemble approach of BiGRU model: A case study of AMIS tweets
This paper presents a comparably simpler yet effective deep learning approach for sentiment analysis of Twitter topics. We automatically collected positive and negative tweets and labeled them manually, and thus created a new dataset. We then leveraged BiGRU model with an ensemble approach for the binary classification of tweets. Our finalized BiGRU model offered an accuracy of 84.8% as well as an averaged F1-measure of 84.8%(±0.3). Moreover, the ensemble approach, using an averaged prediction of 5-fold strategy, provided the accuracy of 86.3% along with the averaged F1-measure of 86.3%(±0.05). Consequently, the ensemble approach offered better performance even on a smaller dataset used in this study.