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

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

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.

scholarly journals Bullet Subtitle Sentiment Classification Based on Affective Computing and Ensemble Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Lei Yu ◽  
Yu Wu ◽  
Jie Yang ◽  
Yunkai Zhang
Keyword(s):  
Ensemble Learning ◽  
Affective Computing ◽  
Classification Problem ◽  
Computing Methods ◽  
Sentiment Classification ◽  
Classification Algorithms ◽  
Practical Application ◽  
Sentiment Lexicon ◽  
Text Information ◽  
Computing Method

The bullet subtitle reflects a kind of instant feedback from the user to the current video. It is generally short but contains rich sentiment. However, the bullet subtitle has its own unique characteristics, and the effect of applying existing sentiment classification methods to the bullet subtitle sentiment classification problem is not ideal. First, since bullet subtitles usually contain a large number of buzzwords, existing sentiment lexicons are not applicable, we propose Chinese Bullet Subtitle Sentiment Lexicon on the basis of existing sentiment lexicons. Second, considering that some traditional affective computing methods only consider the text information and ignore the information of other dimensions, we construct a bullet subtitle affective computing method by combining the information of other dimensions of the bullet subtitle. Finally, aiming at the problem that existing classification algorithms ignore the importance of sentiment words in short texts, we propose a sentiment classification method based on affective computing and ensemble learning. Our experiment results show that the proposed method has higher accuracy and better practical application effect.

Sentiment classification: The contribution of ensemble learning

2014 ◽  
Vol 57 ◽  
pp. 77-93 ◽  
Author(s):  
Gang Wang ◽  
Jianshan Sun ◽  
Jian Ma ◽  
Kaiquan Xu ◽  
Jibao Gu
Keyword(s):  
Ensemble Learning ◽  
Sentiment Classification
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