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 writings.Twi...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 writings.Twitter is micro-blogging short text and social networking services,with posted millions of quick messages.Twitter analysis addresses the topic of interpreting users’tweets in terms of ideas,interests,and views in a range of settings andfields.This type of study can be useful for a variation of academics and applications that need knowing people’s perspectives on a given topic or event.Although 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 polysemy.Furthermore,most contemporary sentiment analysis algorithms are based on clean data.In 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 noise.We also utilise the Random Forest network to detect the emotion of a tweet.To 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.展开更多
文摘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 writings.Twitter is micro-blogging short text and social networking services,with posted millions of quick messages.Twitter analysis addresses the topic of interpreting users’tweets in terms of ideas,interests,and views in a range of settings andfields.This type of study can be useful for a variation of academics and applications that need knowing people’s perspectives on a given topic or event.Although 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 polysemy.Furthermore,most contemporary sentiment analysis algorithms are based on clean data.In 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 noise.We also utilise the Random Forest network to detect the emotion of a tweet.To 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.
