{"title":"CNN-LSTM在印地语混合语言情感分析中的比较","authors":"Manish Rao Ghatge, S. Barde","doi":"10.1109/ICTACS56270.2022.9988531","DOIUrl":null,"url":null,"abstract":"Despite the fact that Hindi is spoken by over 490 million people globally and social media is producing a massive quantity of Hindi data on a daily basis, few research studies and initiatives to develop Hindi language resources and assess user sentiments have been accomplished. The study's major objectives are to (1) develop Hindi-English-Chhattisgarhi dataset for agriculturist's sentiment analysis and (2) assess multiple approaches of sentiment analysis through deep putting the deep learning classifiers into action (1D-CNN and LSTM).","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Comparison of CNN-LSTM in Sentiment Analysis for Hindi Mix Language\",\"authors\":\"Manish Rao Ghatge, S. Barde\",\"doi\":\"10.1109/ICTACS56270.2022.9988531\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Despite the fact that Hindi is spoken by over 490 million people globally and social media is producing a massive quantity of Hindi data on a daily basis, few research studies and initiatives to develop Hindi language resources and assess user sentiments have been accomplished. The study's major objectives are to (1) develop Hindi-English-Chhattisgarhi dataset for agriculturist's sentiment analysis and (2) assess multiple approaches of sentiment analysis through deep putting the deep learning classifiers into action (1D-CNN and LSTM).\",\"PeriodicalId\":385163,\"journal\":{\"name\":\"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-10-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICTACS56270.2022.9988531\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICTACS56270.2022.9988531","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Comparison of CNN-LSTM in Sentiment Analysis for Hindi Mix Language
Despite the fact that Hindi is spoken by over 490 million people globally and social media is producing a massive quantity of Hindi data on a daily basis, few research studies and initiatives to develop Hindi language resources and assess user sentiments have been accomplished. The study's major objectives are to (1) develop Hindi-English-Chhattisgarhi dataset for agriculturist's sentiment analysis and (2) assess multiple approaches of sentiment analysis through deep putting the deep learning classifiers into action (1D-CNN and LSTM).