A Machine Learning-Based Technique with Intelligent WordNet Lemmatize for Twitter Sentiment Analysis

作     者：S.Saranya G.Usha 

作者机构：Computing TechnologySRMISTChennaiTamil NaduIndia 

出 版 物：《Intelligent Automation & Soft Computing》 (智能自动化与软计算（英文）)

年 卷 期：2023年第36卷第4期

页      面：339-352页

核心收录：

学科分类：08[工学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Random Forest sentiment analysis social media term frequency and inverse document frequency twitter wordnet lemmatize 

摘      要：Laterally with the birth of the Internet,the fast growth of mobile stra-tegies has democratised content production owing to the widespread usage of social media,resulting in a detonation of short informal *** is micro-blogging short text and social networking services,with posted millions of quick *** analysis addresses the topic of interpreting users’tweets in terms of ideas,interests,and views in a range of settings andfi*** type of study can be useful for a variation of academics and applications that need knowing people’s perspectives on a given topic or *** sentiment examination of these texts is useful for a variety of reasons,it is typically seen as a difficult undertaking due to the fact that these messages are frequently short,informal,loud,and rich in linguistic ambiguities such as ***,most contemporary sentiment analysis algorithms are based on clean *** this paper,we offers a machine-learning-based sentiment analysis method that extracts features from Term Frequency and Inverse Document Frequency(TF-IDF)and needs to apply deep intelligent wordnet lemmatize to improve the excellence of tweets by removing *** also utilise the Random Forest network to detect the emotion of a *** authenticate the proposed approach performance,we conduct extensive tests on publically accessible datasets,and thefindings reveal that the suggested technique significantly outperforms sentiment classification in multi-class emotion text data.