{"title":"情感数据分析Ulasan微博登记本长短期记忆","authors":"Sharfina Febbi Handayani, Riszki Wijayatun Pratiwi, Dairoh Dairoh, Dwi Intan Af’idah","doi":"10.31544/jtera.v7.i1.2022.39-46","DOIUrl":null,"url":null,"abstract":"The development of social media has made it easier for people to disseminate information. One form of information in question is the freedom to express opinions on social media. The development of research related to sentiment analysis on text review data aims to determine the polarity of opinion on social media which has increased. One of the methods applied in the sentiment analysis of the review text is the use of the Long Short-Term Memory (LSTM) method. The purpose of this study was to determine the performance of the LSTM model on various sentiments of reviews of Indonesian-language Twitter texts. The testing process is carried out based on the calculation of the hyperparameter tuning accuracy value. Testing the accuracy of this study using Word2Vec parameters, activation function, number of epochs, and number of neurons. The optimal LSTM performance test results from this study were obtained based on tuning the Word2Vec Continuous Bag of Words (CBOW) architecture with an accuracy of 57.15%, tuning the number of neurons as much as 150 producing an accuracy value of 57.35%, tuning the number of epochs at 30 producing an accuracy value of 57. .40%, and tuning the softmax activation function produces an accuracy value of 57.35%.","PeriodicalId":17680,"journal":{"name":"JTERA (Jurnal Teknologi Rekayasa)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Analisis Sentimen pada Data Ulasan Twitter dengan Long-Short Term Memory\",\"authors\":\"Sharfina Febbi Handayani, Riszki Wijayatun Pratiwi, Dairoh Dairoh, Dwi Intan Af’idah\",\"doi\":\"10.31544/jtera.v7.i1.2022.39-46\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The development of social media has made it easier for people to disseminate information. One form of information in question is the freedom to express opinions on social media. The development of research related to sentiment analysis on text review data aims to determine the polarity of opinion on social media which has increased. One of the methods applied in the sentiment analysis of the review text is the use of the Long Short-Term Memory (LSTM) method. The purpose of this study was to determine the performance of the LSTM model on various sentiments of reviews of Indonesian-language Twitter texts. The testing process is carried out based on the calculation of the hyperparameter tuning accuracy value. Testing the accuracy of this study using Word2Vec parameters, activation function, number of epochs, and number of neurons. The optimal LSTM performance test results from this study were obtained based on tuning the Word2Vec Continuous Bag of Words (CBOW) architecture with an accuracy of 57.15%, tuning the number of neurons as much as 150 producing an accuracy value of 57.35%, tuning the number of epochs at 30 producing an accuracy value of 57. .40%, and tuning the softmax activation function produces an accuracy value of 57.35%.\",\"PeriodicalId\":17680,\"journal\":{\"name\":\"JTERA (Jurnal Teknologi Rekayasa)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-06-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"JTERA (Jurnal Teknologi Rekayasa)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.31544/jtera.v7.i1.2022.39-46\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"JTERA (Jurnal Teknologi Rekayasa)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.31544/jtera.v7.i1.2022.39-46","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
摘要
社交媒体的发展使人们更容易传播信息。受到质疑的一种信息形式是在社交媒体上表达意见的自由。对文本评论数据进行情感分析相关研究的发展旨在确定社交媒体上日益增加的意见极性。在评论文本情感分析中应用的方法之一是长短期记忆(LSTM)方法。本研究的目的是确定LSTM模型在印尼语Twitter文本评论的各种情绪上的表现。测试过程基于超参数整定精度值的计算。使用Word2Vec参数、激活函数、epoch数、神经元数来检验本研究的准确性。优化Word2Vec Continuous Bag of Words (CBOW)架构,优化准确率为57.15%,优化神经元数最多为150,优化准确率为57.35%,优化epoch数为30,优化softmax激活函数,优化准确率为57.40%,得到了最优的LSTM性能测试结果。
Analisis Sentimen pada Data Ulasan Twitter dengan Long-Short Term Memory
The development of social media has made it easier for people to disseminate information. One form of information in question is the freedom to express opinions on social media. The development of research related to sentiment analysis on text review data aims to determine the polarity of opinion on social media which has increased. One of the methods applied in the sentiment analysis of the review text is the use of the Long Short-Term Memory (LSTM) method. The purpose of this study was to determine the performance of the LSTM model on various sentiments of reviews of Indonesian-language Twitter texts. The testing process is carried out based on the calculation of the hyperparameter tuning accuracy value. Testing the accuracy of this study using Word2Vec parameters, activation function, number of epochs, and number of neurons. The optimal LSTM performance test results from this study were obtained based on tuning the Word2Vec Continuous Bag of Words (CBOW) architecture with an accuracy of 57.15%, tuning the number of neurons as much as 150 producing an accuracy value of 57.35%, tuning the number of epochs at 30 producing an accuracy value of 57. .40%, and tuning the softmax activation function produces an accuracy value of 57.35%.