Enhancing Sentiment Analysis via Random Majority Under-Sampling with Reduced Time Complexity for Classifying Tweet Reviews

Almuayqil, Saleh Naif; Humayun, Mamoona; Jhanjhi, N. Z.; Almufareh, Maram Fahaad; Khan, Navid Ali

doi:10.3390/electronics11213624

Cited by 8 publications

(4 citation statements)

References 39 publications

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“…Almuayqil et al [34] took an innovative approach by designing a model specifically for imbalanced Twitter datasets. By utilizing an array of text sequencing preprocessing methods combined with random under-sampling of the majority class, they managed to considerably cut down the computational time required for the task.…”

Section: Imbalanced Sentiment Analysismentioning

confidence: 99%

Mitigating Class Imbalance in Sentiment Analysis through GPT-3-Generated Synthetic Sentences

Suhaeni,

Yong

2023

Applied Sciences

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In this paper, we explore the effectiveness of the GPT-3 model in tackling imbalanced sentiment analysis, focusing on the Coursera online course review dataset that exhibits high imbalance. Training on such skewed datasets often results in a bias towards the majority class, undermining the classification performance for minority sentiments, thereby accentuating the necessity for a balanced dataset. Two primary initiatives were undertaken: (1) synthetic review generation via fine-tuning of the Davinci base model from GPT-3 and (2) sentiment classification utilizing nine models on both imbalanced and balanced datasets. The results indicate that good-quality synthetic reviews substantially enhance sentiment classification performance. Every model demonstrated an improvement in accuracy, with an average increase of approximately 12.76% on the balanced dataset. Among all the models, the Multinomial Naïve Bayes achieved the highest accuracy, registering 75.12% on the balanced dataset. This study underscores the potential of the GPT-3 model as a feasible solution for addressing data imbalance in sentiment analysis and offers significant insights for future research.

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Section: Imbalanced Sentiment Analysismentioning

confidence: 99%

Mitigating Class Imbalance in Sentiment Analysis through GPT-3-Generated Synthetic Sentences

Suhaeni,

Yong

2023

Applied Sciences

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“…The likelihood of winning is affected by the runs scored by the side batting first throughout the first inning [14]. In our survey on predicting outcomes in the Pakistan Super League (PSL) using machine learning techniques, our research builds upon foundational insights presented in [15][16][17][18][19][20][21][22][23][24][25][26].…”

Section: Different Attribute To Influence the Matchmentioning

confidence: 99%

Prediction for Pakistan Super League Using Machine Learning Techniques: A Survey

ul Ain,

Ashraf,

Jhanjhi

2023

Preprint

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Cricket, the most popular sports in the world to chase by the fans. Many cricket fans are interested in analyzing the elements and activities that affect the outcomes of matches. Machine learning in sports analytics is a relatively new topic in computer science. The purpose of this survey is to anticipate the outcome of a PSL champion team and build a winning strategy. Machine learning (ML) has exhibited favorable outcomes in a variety of sectors for diverse prediction using classification, regression, and so on, and is proven to be accurate. The innovative frameworks based on ML have the ability to learn from previous experiences. The cricket pitch is the most important aspect, alongside home game an edge, coin toss, innings of play, day/night match, physical conditioning, and dynamic plans, among other things. The dataset from previously ESPN Cricifno was used for this purpose. Recent classification approaches including, Random Forest, Gradient Boost, Deep Neural Networks used to conduct a comparative analysis based on their results and performances, as well as team strategies.

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“…On the other hand, techniques at the data processing level concentrate on adjusting the distribution of training samples to reduce the imbalance within datasets, which is a straightforward approach to achieving balance. A typical data-oriented approach is the resampling method that mainly encompasses the undersampling [8] and the oversampling techniques [9]. Oversampling is frequently preferred over other data-level methods due to its capacity to improve classification accuracy by augmenting the minority class instances.…”

Section: Introductionmentioning

confidence: 99%

Learning Vector Quantization-Based Fuzzy Rules Oversampling Method

Chen,

Han,

Zhang

et al. 2024

CMC

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Imbalanced datasets are common in practical applications, and oversampling methods using fuzzy rules have been shown to enhance the classification performance of imbalanced data by taking into account the relationship between data attributes. However, the creation of fuzzy rules typically depends on expert knowledge, which may not fully leverage the label information in training data and may be subjective. To address this issue, a novel fuzzy rule oversampling approach is developed based on the learning vector quantization (LVQ) algorithm. In this method, the label information of the training data is utilized to determine the antecedent part of If-Then fuzzy rules by dynamically dividing attribute intervals using LVQ. Subsequently, fuzzy rules are generated and adjusted to calculate rule weights. The number of new samples to be synthesized for each rule is then computed, and samples from the minority class are synthesized based on the newly generated fuzzy rules. This results in the establishment of a fuzzy rule oversampling method based on LVQ. To evaluate the effectiveness of this method, comparative experiments are conducted on 12 publicly available imbalance datasets with five other sampling techniques in combination with the support function machine. The experimental results demonstrate that the proposed method can significantly enhance the classification algorithm across seven performance indicators, including a boost of 2.15% to 12.34% in Accuracy, 6.11% to 27.06% in G-mean, and 4.69% to 18.78% in AUC. These show that the proposed method is capable of more efficiently improving the classification performance of imbalanced data.

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Enhancing Sentiment Analysis via Random Majority Under-Sampling with Reduced Time Complexity for Classifying Tweet Reviews

Cited by 8 publications

References 39 publications

Mitigating Class Imbalance in Sentiment Analysis through GPT-3-Generated Synthetic Sentences

Mitigating Class Imbalance in Sentiment Analysis through GPT-3-Generated Synthetic Sentences

Prediction for Pakistan Super League Using Machine Learning Techniques: A Survey

Learning Vector Quantization-Based Fuzzy Rules Oversampling Method

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