A Survey and Comparative Study of Tweet Sentiment Analysis via Semi-Supervised Learning

2016 ◽  
Vol 49 (1) ◽  
pp. 1-26 ◽  
Author(s):  
Nadia Felix F. Da Silva ◽  
Luiz F. S. Coletta ◽  
Eduardo R. Hruschka
Keyword(s):  
Comparative Study ◽  
Sentiment Analysis ◽  
Supervised Learning

scholarly journals A review: preprocessing techniques and data augmentation for sentiment analysis

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Huu-Thanh Duong ◽  
Tram-Anh Nguyen-Thi
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Supervised Learning ◽  
Data Augmentation ◽  
Original Data ◽  
Training Data ◽  
Unseen Data ◽  
Augmentation Techniques ◽  
User Intervention

AbstractIn literature, the machine learning-based studies of sentiment analysis are usually supervised learning which must have pre-labeled datasets to be large enough in certain domains. Obviously, this task is tedious, expensive and time-consuming to build, and hard to handle unseen data. This paper has approached semi-supervised learning for Vietnamese sentiment analysis which has limited datasets. We have summarized many preprocessing techniques which were performed to clean and normalize data, negation handling, intensification handling to improve the performances. Moreover, data augmentation techniques, which generate new data from the original data to enrich training data without user intervention, have also been presented. In experiments, we have performed various aspects and obtained competitive results which may motivate the next propositions.

Sentiment analysis using semi-supervised learning with few labeled data

2020 ◽  
Author(s):  
Yuhao Pan ◽  
Zhiqun Chen ◽  
Yoshimi Suzuki ◽  
Fumiyo Fukumoto ◽  
Hiromitsu Nishizaki
Keyword(s):  
Sentiment Analysis ◽  
Supervised Learning

A Comparative Study of Sentiment Analysis Tools

2021 ◽  
Author(s):  
Nabanita Das ◽  
Saloni Gupta ◽  
Srinjoy Das ◽  
Shuvam Yadav ◽  
Trishika Subramanian ◽  
...  
Keyword(s):  
Comparative Study ◽  
Sentiment Analysis ◽  
Analysis Tools
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