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A Study of Synthetic Oversampling for Twitter Imbalanced Sentiment Analysis
2016
European Conference on Principles of Data Mining and Knowledge Discovery
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We argue that Twitter opinion mining using learning methods should be addressed in the framework of imbalanced learning. ...
The majority of Twitter sentiment analysis systems implicitly assume that the class distribution is balanced while in practice it is usually skewed. ...
Related Works
Twitter Sentiment Analysis Twitter sentiment analysis has received a growing interest starting from 2009 [5, 19] . ...
dblp:conf/pkdd/Ah-PineS16
fatcat:ki52hpxuinfllefzhkqncnsc5u
Framework for Improved Sentiment Analysis via Random Minority Oversampling for User Tweet Review Classification
2022
Electronics
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Sentiment analysis based on machine learning has been successful in discovering the opinion of the people using redundantly available data. ...
In this paper, we propose a framework for improved sentiment analysis through various ordered preprocessing steps with the combination of resampling of minority classes to produce greater performance. ...
imbalanced dataset for multiclass classification problem through the use of random oversampling; • Selection of best features for sentiment analysis. ...
doi:10.3390/electronics11193058
fatcat:kg64axoxdnd5joeu3e5daoesue
Adaptation of domain-specific transformer models with text oversampling for sentiment analysis of social media posts on Covid-19 vaccines
[article]
2023
arXiv
pre-print
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where there is an imbalanced class distribution among the positive, negative and neutral sentiment classes. ...
Our results summarize our findings on the suitability of text oversampling for imbalanced small sample datasets that are used to fine-tune state-of-the-art pre-trained transformer models, and the utility ...
Other unsupervised techniques used for Twitter sentiment analysis include AFINN [28] and TextBlob [27] . ...
arXiv:2209.10966v2
fatcat:3pplkc5k5nhafdjrzoulweb7oa
Sentiment Analysis of Hate Speech on Twitter Public Figures with AdaBoost and XGBoost Methods
2022
JURNAL MEDIA INFORMATIKA BUDIDARMA
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This study aims to determine public sentiment towards public figure Anya Geraldine conveyed on Twitter in Indonesian. ...
From the label results using oversampling to avoid excessive overfitting problems. The feature used is TF-IDF weighting. ...
Number of Sentences for Each Label Before Oversampling Figure 2 shows imbalanced data. When imbalanced data, the classification tends to ignore the minority class. ...
doi:10.30865/mib.v6i3.4394
fatcat:gt7vhz7pvnctzmhbwufk3txeoq
Review of Factors Affecting Efficiency of Twitter Data Sentiment Analysis
2020
Journal of clean energy technologies
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The survey also focuses on various metrics used for representation of sentiment analysis result and their relevance. ...
The nature of data collected by Twitter imposes several challenges for sentiment analysis. ...
Data may be downloaded statically from the past repository or live streaming data can be used for analysis. In Twitter data sentiment analysis tweets are re-tweeted many numbers of times. ...
doi:10.7763/ijcte.2020.v12.1263
fatcat:s5rrbykzlfel7fvspzfhvfbeoe
SENTIMENT ANALYSIS OF THE LARGE PRIEST OF FPI'S RETURN USING SUPPORT VECTOR MACHINE WITH OVERSAMPLING METHOD
2021
Jurnal Riset Informatika
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The researcher wants to classify the opinion text data of Habib Rizieq's return from Twitter into positive and negative sentiments using the Support Vector Machine method. ...
While the SVM classification with the oversampling method gets 100% accuracy, precision, and recall. ...
Data analysis technique In this study, it is known that the data is obtained through the user's Twitter account through the Twitter API. ...
doi:10.34288/jri.v4i1.262
fatcat:alqcniyq2rdhxenllzvuhv64lm
UBC-NLP at SemEval-2019 Task 6: Ensemble Learning of Offensive Content With Enhanced Training Data
2019
Proceedings of the 13th International Workshop on Semantic Evaluation
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We examine learning offensive content on Twitter with limited, imbalanced data. ...
For the purpose, we investigate the utility of using various data enhancement methods with a host of classical ensemble classifiers. ...
Da Silva et al. (2014) exploit ensembles to boost the accuracy on twitter sentiment analysis. ...
doi:10.18653/v1/s19-2136
dblp:conf/semeval/RajendranZA19
fatcat:6mdn2q3ljvgg3clfbsdyy32zty
Twitter Sentiment Analysis, 3-Way Classification: Positive, Negative or Neutral?
2018
2018 IEEE International Conference on Big Data (Big Data)
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Effect of oversampling, unigram features and other features on overall and class-based accuracy ratios is worked on the datasets. ...
The study's main focus is to classify negative, positive and neutral approaches of three (3) annotated twitter datasets. ...
It is observed that critical stage of twitter sentiment analysis is feature extraction, analysis. ...
doi:10.1109/bigdata.2018.8621970
dblp:conf/bigdataconf/Celiktug18
fatcat:z36sif2jgzfbpmsaz2itkes6pe
Spiteful, One-Off, and Kind: Predicting Customer Feedback Behavior on Twitter
[chapter]
2016
Lecture Notes in Computer Science
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We use profile and content features extracted from Twitter. We experiment with different algorithms to create a prediction model. ...
First, we identify a few categories of customers based on their feedback frequency and the sentiment of the feedback. We identify three main categories: spiteful, one-off, and kind. ...
This result gives evidence that handling imbalanced data by using minority-class oversampling improves the accuracy of the constructed prediction model. ...
doi:10.1007/978-3-319-47874-6_26
fatcat:jtlz7xvrivfv7hzotl3wshwkwm
A novel k-nearest neighbor distance based under sampling for improved opinion mining on skewed data using random forest
2018
International Journal of Engineering & Technology
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In recent years, consumers are performing a pilot investigation using online resources before making any decision of purchase. One of the most popular social blogging online medium is twitter. ...
The opinions collected from twitter at any point of frame in real world scenario are tending towards class imbalance in nature. ...
Julien Ah-Pine et al., [25] have proposed a novel method for twitter opinion mining using machine learning method such as synthetic oversampling techniques for imbalanced learning using tweet-polarity ...
doi:10.14419/ijet.v7i1.8.9970
fatcat:lchsyekmwjguxcmsqbwsrpg5zq
Multi-Class Sentiment Analysis of Social Media Data with Machine Learning Algorithms
2021
Computers Materials & Continua
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Three resampling techniques (undersampling, oversampling, and synthetic minority oversampling (SMOTE)) are used to resample the datasets to deal with this issue. ...
As relatively few works have paid attention to sentiment analysis in the Russian and Kazakh languages, this article thoroughly analyzes news posts in the Kazakhstan media space. ...
Acknowledgement: We would like to thank the Center for data analysis and processing of Al-Farabi Kazakh National University for providing the datasets obtained with the OMSystem. ...
doi:10.32604/cmc.2021.017827
fatcat:2drn7jk3knhhjgdllxam44gpee
Performance Evaluation of Sentiment Analysis on Balanced and Imbalanced Dataset Using Ensemble Approach
2022
Indian Journal of Science and Technology
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Class imbalance is often discussed as a strenuous task in the realm of sentiment analysis. ...
Methods: At primary level this study uses a novel Synthetic Minority Oversampling Technique (SMOTE) for balancing the dataset and then proposes an ensemble model, named Ensemble Bagging Support Vector ...
The impact of imbalanced and balanced dataset is analysed using random under-sampling and SMOTE oversampling techniques. ...
doi:10.17485/ijst/v15i17.2339
fatcat:5i6qh3kwknglderyehl4ylsk6e
Word2Vec on Sentiment Analysis with Synthetic Minority Oversampling Technique and Boosting Algorithm
2022
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
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In this research, an aspect-based sentiment analysis was conducted on Telkomsel users on Twitter. ...
Aspect-based sentiment analysis can be used by companies to analyze more specifically and find out what aspects need to be improved. ...
As a solution to the problem of imbalanced data, this research used SMOTE as an oversampling technique and Gradient Boosting is also used to optimize the classification process. ...
doi:10.29207/resti.v6i4.4186
fatcat:w366ifkbobgqjbrltriouvobbi
Sentimental Analysis on Health-Related Information with Improving Model Performance using Machine Learning
2021
Journal of Computer Science
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The model has achieved a higher accuracy that reached 95% when using Synthetic Minority Oversampling TEchnique (SMTOE) techniques to balanced dataset than imbalanced dataset. ...
Therefore, this study proposes a model to detect and discover emotions/opinions of YouTube users on herbal treatment videos is proposed through an analysis of user comments by using machine learning classifiers ...
The model has achieved a higher accuracy level that reached 95% when using Synthetic Minority Oversampling TEchnique (SMTOE). ...
doi:10.3844/jcssp.2021.112.122
fatcat:eqyokxnyyzbcnmq3k5pzygfkti
Ensemble Classifiers for Arabic Sentiment Analysis of Social Network (Twitter Data) towards COVID-19-Related Conspiracy Theories
2022
Applied Computational Intelligence and Soft Computing
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Several single-based and ensemble-based machine learning classifiers have been used with and without SMOTE (synthetic minority oversampling technique). ...
Just a few studies utilized sentiment analysis of social media using a machine learning approach. ...
We integrated SMOTENC (synthetic minority oversampling technique for nominal and continuous) on the training data to balance an imbalanced dataset. ...
doi:10.1155/2022/6614730
fatcat:hcb4vibtwjg7tmzew5o7ny3pku
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