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Cooperative Hybrid Semi-Supervised Learning for Text Sentiment Classification

Yang Li, Ying Lv, Suge Wang, Jiye Liang, Juanzi Li, Xiaoli Li
2019 Symmetry  
A large-scale and high-quality training dataset is an important guarantee to learn an ideal classifier for text sentiment classification.  ...  the training text set, to the co-training strategy of the classifier is proposed in this paper for text sentiment classification.  ...  In this paper, we proposed a cooperative semi-supervised learning method based on the hybrid mechanism of active learning and self-learning for text sentiment classification.  ... 
doi:10.3390/sym11020133 fatcat:dh35arvfsrhhjcy2vqbloye7qa

Active Semi-Supervised Learning Method with Hybrid Deep Belief Networks

Shusen Zhou, Qingcai Chen, Xiaolong Wang, Catalin Buiu
2014 PLoS ONE  
In this paper, we develop a novel semi-supervised learning algorithm called active hybrid deep belief networks (AHD), to address the semi-supervised sentiment classification problem with deep learning.  ...  We did several experiments on five sentiment classification datasets, and show that AHD is competitive with previous semi-supervised learning algorithm.  ...  In this paper, we propose a novel semi-supervised learning algorithm called active hybrid deep belief networks (AHD), to address the semi-supervised sentiment classification problem with deep learning.  ... 
doi:10.1371/journal.pone.0107122 pmid:25208128 pmcid:PMC4160211 fatcat:a426utznr5bajbj4il7tam42jy

A Review on Sentiment Analysis in Arabic Using Document Level

Ibrahim Awajan, Mumtazimah Mohamad
2018 International Journal of Engineering & Technology  
This review includes research and studies published during the period of 2011-2017 for different approaches of document-level sentiment analysis.  ...  ., the focus on the sentiment analysis (SA) is deeply being studied. The research on Arabic sentiment analysis is progressing very slow in compared to English sentiment analysis.  ...  This approach creates cooperation between unsupervised and supervised learning by adjusting for the shortfall of labeled cases with unlabeled ones, So Semi-Supervised Learning is good to build sentiment  ... 
doi:10.14419/ijet.v7i3.13.16338 fatcat:jlwjed6cizdtrohcjqow5uafk4

LeSSA: A Unified Framework based on Lexicons and Semi-Supervised Learning Approaches for Textual Sentiment Classification

Jawad Khan, Young-Koo Lee
2019 Applied Sciences  
In the absence of enough labeled data, the alternative usage of sentiment lexicons and semi-supervised learning approaches for sentiment classification have substantially attracted the attention of the  ...  However, state-of-the-art techniques for semi-supervised sentiment classification present research challenges expressed in questions like the following.  ...  [29] proposed a cooperative semi-supervised learning approach based on the hybrid mechanism of active learning and self-learning for textual sentiment classification.  ... 
doi:10.3390/app9245562 fatcat:adzlvshbmbfklew457auwrh7ue

Evaluation of Named Entity Recognition Algorithms in Short Texts

Edgar Casasola Murillo, Raquel Fonseca
2017 CLEI Electronic Journal  
The text that is generated in social networks constitutes a new type of content, that is short, informal, lacking grammar in some cases, and noise prone.  ...  This paper presents the results of applying AlchemyAPI y Dandelion API algorithms in a corpus provided by The SemEval-2015 Aspect Based Sentiment Analysis Conference.  ...  Acknowledgment The authors would like to thank professor Rodrigo Bartels, researcher from the Centro de Investigaciones en Tecnologías de la Información y Comunicación (CITIC), for his expert feedback  ... 
doi:10.19153/cleiej.20.1.4 fatcat:6mdzxfl3s5apdk5nufqnvxjv6m

A Systematic Literature Review of Personality Trait Classification from Textual Content

Hussain Ahmad, Muhammad Zubair Asghar, Alam Sher Khan, Anam Habib
2020 Open Computer Science  
In addition, personality trait identification and techniques were classified into different types, including deep learning, machine learning (ML) and semi-supervised/hybrid.Implications This paper's outcomes  ...  When using social networks, people share personal data that is broadcast between users, which provides useful information for organizations.  ...  Table 6 : 6 Selected Studies for Personality Trait Classification Using Semi-Supervised and Hybrid Machine Learning techniques Std.  ... 
doi:10.1515/comp-2020-0188 fatcat:iwnypf73pfel5n2tmvau4sbgri

Lexicon‐pointed hybrid N‐gram Features Extraction Model ( LeNFEM ) for sentence level sentiment analysis

James Mutinda, Waweru Mwangi, George Okeyo
2021 Engineering Reports  
algorithm for sentence level sentiment analysis.  ...  Feature extraction and selection is a key determinant of accuracy and computational cost of machine learning models for such analysis.  ...  such texts for sentiment classification still poses challenges to data analytics.  ... 
doi:10.1002/eng2.12374 fatcat:3m25kzgmxnambm4hnaxngcr6yy

A Cooperative Binary-Clustering Framework Based on Majority Voting for Twitter Sentiment Analysis

Maryum Bibi, Wajid Aziz, Majid Almarashi, Imtiaz Hussain Khan, Malik Sajjad Ahmed Nadeem, Nazneen Habib
2020 IEEE Access  
For this purpose, supervised learning techniques have mostly been employed, which require labeled data for training. However, it is very time consuming to label datasets of large size.  ...  INDEX TERMS Cooperative clustering, majority voting, sentiment analysis, twitter sentiment analysis.  ...  Recently, some researchers proposed semi-supervised learning techniques based on statistical-learning theory for sentiment analysis [39] .  ... 
doi:10.1109/access.2020.2983859 fatcat:yazthauwr5fzvifbwmq74jrsqu

Survey of Tools and Techniques for Sentiment Analysis of Social Networking Data

Sangeeta Rani, Nasib Singh, Preeti Gulia
2021 International Journal of Advanced Computer Science and Applications  
The result reflects that Twitter is the most explored social networking site for opinion mining. Naïve Bayes and SVM machine learning algorithms are implemented in maximum researches.  ...  Sentiment analysis of this data has a vast scope in decision support and attracted many researchers to explore various possibilities for technique enhancement and accuracy improvement.  ...  [25] proposed a new semi-supervised approach to solve the problem of cost of getting supervised data for machine learning.  ... 
doi:10.14569/ijacsa.2021.0120430 fatcat:kccjxxwia5gmfpwixmemza2oqm

Recommendation system based on heterogeneous feature: A survey

Hui Wang, ZiChun Le, Xuan Gong
2020 IEEE Access  
These methods are introduced, as shown in Figure 2 . 1) Graph semi-supervised learning recommendation system The basic idea of graph-based semi-supervised learning is to construct graphs for all samples  ...  In this paper, graph-based recommendation systems are divided into graph semi-supervised, graph-unsupervised, graph-supervised, and graph-based reinforcement learning recommendation systems.  ... 
doi:10.1109/access.2020.3024154 fatcat:clxk77bcr5hdjd3hnxxi6wzlr4

Swarm Intelligence in Semi-supervised Classification [article]

Shahira Shaaban Azab, Hesham Ahmed Hefny
2017 arXiv   pre-print
This paper introduces a brief literature review for applying swarm intelligence algorithms in the field of semi-supervised learning  ...  This Paper represents a literature review of Swarm intelligence algorithm in the area of semi-supervised classification.  ...  The proposed classifier is compared with two supervised classifiers (multilayer perceptron and support vector machine) and three semi-supervised classifiers (semi-supervised classification by low-density  ... 
arXiv:1706.00998v1 fatcat:rpcyd4t4hzh5nig3h76veorwha

A Comprehensive Analysis of Approaches for Sentiment Analysis Using Twitter Data on COVID-19 Vaccines

Amrita Mishra, Department of Computer Science Engineering, BBD University, Lucknow, India
2021 Journal of Informatics Electrical and Electronics Engineering (JIEEE)  
Sentiment Analysis has paved routes for opinion analysis of masses over unrestricted territorial limits.  ...  While these social media data are extremely informative and well connected, the major challenge lies in incorporating efficient Text Classification strategies which not only overcomes the unstructured  ...  and a hybrid algorithmn(based on Differential Evolution and Genetic Algorithm)for weight training, thereby obtaining a maximum classification accuracy of 83.25%.  ... 
doi:10.54060/jieee/002.02.009 fatcat:msrpzwvm7ngjzmwb5zkakgwgg4

IT ticket classification: the simpler, the better

Aleksandra Revina, Krisztian Buza, Vera G. Meister
2020 IEEE Access  
Therefore, due to the limited amount of labeled tickets, we also experimented with semi-supervised approaches for ticket classification.  ...  Self-learning is a semi-supervised technique known to improve the learning process in case of a large number of unlabeled and a small number of labeled instances [84] .  ...  Her research interests include diverse methods and tools for business process analysis and automation with the goal of developing efficient decision support systems for process workers. KRISZTIAN  ... 
doi:10.1109/access.2020.3032840 fatcat:tpljivu26jhe3ivpua6v24hbgq

Sentiment analysis of customer data

Olivera Grljević, Zita Bošnjak
2018 Strategic Management  
In addition to the significance of sentiment analysis for business, this paper deals with sentiment analysis process and underlying data mining techniques.  ...  Within these contents -texts that users voluntarily post on the Internet and make it publicly available -users freely express their views, opinions, describe their consumer experience, the problems they  ...  In the sentiment classification, a lexicon-based approach was applied together with supervised learning techniques.  ... 
doi:10.5937/straman1803038g fatcat:fd3yhzwrvze5baqxip3zp3kv6q

Editorial of the evolving and hybrid systems' modelling special issue

Lazaros Iliadis, Ilias Maglogiannis
2020 Evolving Systems  
A variety of well-known anomaly detection algorithms are employed, which address intrusion detection as a semi-supervised problem.  ...  Its title is "Evaluation of feature learning for anomaly detection in network traffic".  ... 
doi:10.1007/s12530-020-09353-2 fatcat:4r4qcsx7obejpjlmli5m3xd5ae
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