Sentiment analysis via semi-supervised learning: a model based on dynamic threshold and multi-classifiers

2019 ◽  
Vol 32 (9) ◽  
pp. 5117-5129
Author(s):  
Yue Han ◽  
Yuhong Liu ◽  
Zhigang Jin
Keyword(s):  
Sentiment Analysis ◽  
Supervised Learning ◽  
Dynamic Threshold ◽  
Model Based

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 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

A cost-sensitive semi-supervised learning model based on uncertainty

2017 ◽  
Vol 251 ◽  
pp. 106-114 ◽  
Author(s):  
Hongyu Zhu ◽  
Xizhao Wang
Keyword(s):  
Supervised Learning ◽  
Learning Model ◽  
Model Based

scholarly journals Sentiment Analysis on Twitter Airline Data

2021 ◽  
Vol 9 (VI) ◽  
pp. 3767-3770
Author(s):  
Kirti Jain
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Learning Task ◽  
Model Based ◽  
Sentiment Mining ◽  
General Opinion

Sentiment analysis, also known as sentiment mining, is a submachine learning task where we want to determine the overall sentiment of a particular document. With machine learning and natural language processing (NLP), we can extract the information of a text and try to classify it as positive, neutral, or negative according to its polarity. In this project, We are trying to classify Twitter tweets into positive, negative, and neutral sentiments by building a model based on probabilities. Twitter is a blogging website where people can quickly and spontaneously share their feelings by sending tweets limited to 140 characters. Because of its use of Twitter, it is a perfect source of data to get the latest general opinion on anything.

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