Ensemble Learning Sentiment Classification for Un-labeled Arabic Text

2019 ◽  
pp. 203-210
Author(s):  
Amal Alkabkabi ◽  
Mounira Taileb
Keyword(s):  
Ensemble Learning ◽  
Sentiment Classification ◽  
Arabic Text

Imbalanced Sentiment Classification with Multi-strategy Ensemble Learning

2011 ◽  
Author(s):  
Zhongqing Wang ◽  
Shoushan Li ◽  
Guodong Zhou ◽  
Peifeng Li ◽  
Qiaoming Zhu
Keyword(s):  
Ensemble Learning ◽  
Sentiment Classification

Multi-view ensemble learning method for microblog sentiment classification

2021 ◽  
Vol 166 ◽  
pp. 113987
Author(s):  
Xin Ye ◽  
Hongxia Dai ◽  
Lu-an Dong ◽  
Xinyue Wang
Keyword(s):  
Ensemble Learning ◽  
Sentiment Classification ◽  
Learning Method

Ensemble Learning for Sentiment Classification

2013 ◽  
pp. 84-93 ◽  
Author(s):  
Ying Su ◽  
Yong Zhang ◽  
Donghong Ji ◽  
Yibing Wang ◽  
Hongmiao Wu
Keyword(s):  
Ensemble Learning ◽  
Sentiment Classification

scholarly journals Graph Ensemble Learning over Multiple Dependency Trees for Aspect-level Sentiment Classification

2021 ◽  
Author(s):  
Xiaochen Hou ◽  
Peng Qi ◽  
Guangtao Wang ◽  
Rex Ying ◽  
Jing Huang ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Sentiment Classification ◽  
Dependency Trees

Hybrid Ensemble Learning With Feature Selection for Sentiment Classification in Social Media

2020 ◽  
Vol 10 (2) ◽  
pp. 40-58 ◽  
Author(s):  
Sanur Sharma ◽  
Anurag Jain
Keyword(s):  
Social Media ◽  
Feature Selection ◽  
Ensemble Learning ◽  
Information Gain ◽  
Empirical Evaluation ◽  
Ensemble Classifier ◽  
Sentiment Classification ◽  
Ensemble Classifiers ◽  
Chi Squared

This article presents a study on ensemble learning and an empirical evaluation of various ensemble classifiers and ensemble features for sentiment classification of social media data. The data was collected from Twitter in real-time using Twitter API and text pre-processing and ranking-based feature selection is applied to textual data. A framework for a hybrid ensemble learning model is presented where a combination of ensemble features (Information Gain and CHI-Squared) and ensemble classifier that includes Ada Boost with SMO-SVM and Logistic Regression has been implemented. The classification of Twitter data is performed where sentiment analysis is used as a feature. The proposed model has shown improvements as compared to the state-of-the-art methods with an accuracy of 88.2% with a low error rate.

scholarly journals Deep Learning Application to Ensemble Learning—The Simple, but Effective, Approach to Sentiment Classifying

2019 ◽  
Vol 9 (13) ◽  
pp. 2760 ◽  
Author(s):  
Khai Tran ◽  
Thi Phan
Keyword(s):  
Deep Learning ◽  
Sentiment Analysis ◽  
Ensemble Learning ◽  
Language Processing ◽  
Short Term Memory ◽  
Learning Model ◽  
Sentiment Classification ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Deep Learning Model

Sentiment analysis is an active research area in natural language processing. The task aims at identifying, extracting, and classifying sentiments from user texts in post blogs, product reviews, or social networks. In this paper, the ensemble learning model of sentiment classification is presented, also known as CEM (classifier ensemble model). The model contains various data feature types, including language features, sentiment shifting, and statistical techniques. A deep learning model is adopted with word embedding representation to address explicit, implicit, and abstract sentiment factors in textual data. The experiments conducted based on different real datasets found that our sentiment classification system is better than traditional machine learning techniques, such as Support Vector Machines and other ensemble learning systems, as well as the deep learning model, Long Short-Term Memory network, which has shown state-of-the-art results for sentiment analysis in almost corpuses. Our model’s distinguishing point consists in its effective application to different languages and different domains.

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