Active learning enhanced semi-automatic annotation tool for aspect-based sentiment analysis

Smatana, Miroslav; Koncz, P.; Smatana, Peter; Paralič, Ján

doi:10.1109/sisy.2013.6662568

Cited by 6 publications

(2 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It only takes 190 sentences using active learning to get the same quality of 270 sentences without using active learning. Sentiment accuracy for both annotation tools was also measured, where the accuracy of the active learning annotation tool increased by more than 60% and the accuracy of the annotation tool without active learning was about 57% [14].…”

Section: Figure 2 Accuracy Results Of Libnear and Mnbmentioning

confidence: 99%

A Literature Research on Machine Learning Techniques used for Training Annotated Corpus

2019

IJRTE

View full text Add to dashboard Cite

The development of research in the annotation area is growing. Researchers perform annotation task using various forms of datasets such as text, sound, images, and videos. Various algorithms are used to perform tasks. The purpose of this survey is to find out algorithms that are often used by researchers to perform annotation tasks, especially on text data. The literature surveys thirteen research papers on text annotation from the last 5 years. The results of this review indicate that SVM is the algorithm used for all three annotation methods: manual, automatic and semi-automatic annotation, with a significant accuracy above 80%. The result of this survey will be referred by the authors as the basis for subsequent research that will be conducted, especially in the semi-automatic annotation method.

show abstract

Section: Figure 2 Accuracy Results Of Libnear and Mnbmentioning

confidence: 99%

A Literature Research on Machine Learning Techniques used for Training Annotated Corpus

2019

IJRTE

View full text Add to dashboard Cite

show abstract

“…In machine learning based approach, one can use 'Supervised classification techniques', like Naïve Bayes (NB) (Smatana et al, 2013;Ghorpade and Ragha, 2012), Support Vector Machine (SVM) (Abdi et al, 2018;Okada et al, 2014;Dehkharghani et al, 2012;Shi and Li, 2011;Zheng andYe, 2009, Kianmehr et al, 2007) and Decision Tree (Raut and Londhe, 2014;Kabir et al, 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

Decoding Customer Opinion for Products or Brands Using Social Media Analytics

Yadav

Dugar²,

Baishya

2022

International Journal of Intelligent Information Technologies

View full text Add to dashboard Cite

This study uses aspect level sentiment analysis using lexicon-based approach to analyse online reviews of an Indian brand called Patanjali, which sells many FMCG products under its name. These reviews have been collected from the microblogging site twitter from where a total of 4961 tweets about ten Patanjali branded products have been extracted and analysed. Along with the aspect level sentiment analysis, an opinion tagged corpora has also been developed. Machine learning approaches - Support Vector Machine (SVM), Decision Tree, and Naïve Bayes have also been used to perform the sentiment analysis and to figure out the appropriate classifiers suitable for such product reviews analysis. Authors first identify customer preferences and / or opinions about a product or brand by analyisng online customer reviews as they express them on social media platform, twitter by using aspect level sentiment analysis. Authors also address the limitations of scarcity of opinion tagged data, required to train supervised classifiers to perform sentiment analysis by developing tagged corpora.

show abstract

Machine Learning-Based Sentiment Analysis for Analyzing the Travelers Reviews on Egyptian Hotels

Mostafa

2020

Advances in Intelligent Systems and Computing

View full text Add to dashboard Cite

Active learning enhanced semi-automatic annotation tool for aspect-based sentiment analysis

Cited by 6 publications

References 13 publications

A Literature Research on Machine Learning Techniques used for Training Annotated Corpus

A Literature Research on Machine Learning Techniques used for Training Annotated Corpus

Decoding Customer Opinion for Products or Brands Using Social Media Analytics

Machine Learning-Based Sentiment Analysis for Analyzing the Travelers Reviews on Egyptian Hotels

Contact Info

Product

Resources

About