A two-stage unsupervised sentiment analysis method

Wang, Yingqi; He, Xin; Zhai, Rui

doi:10.1007/s11042-023-14864-6

Cited by 4 publications

(2 citation statements)

References 21 publications

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“…Surveyed reviews from users in social networks can be categorized into several distinct types based on the properties that [37], [38] reviews exhibit. The class categorization problem can be solved by considering two categories: the two-class problem and the multi-class problem.…”

Section: Proposed Modelmentioning

confidence: 99%

Machine learning-based approach for sentiment analysis from recorded social media movie reviews

Jassim,

Abd,

Omri

2023

Preprint

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The goal of Sentiment Analysis (SA), especially in social media, is to identify useful information in large amounts of unstructured data from many different sources. Text mining involves extracting revision texts and categorizing them by positive and negative attitudes. However, the value of the extracted features also lies in those that contribute most to the classification process. That's where dimensional reduction comes into play to remove noise and reduce the high area of the feature while maintaining the required accuracy. As part of this work, we propose a new approach for extracting words from a specific text and then classifying them. Thanks to a phase of pre-processing and extraction of words depending on the frequency. For this purpose, we considered a test group database of 50,000 film reviews, of which 25,000 were rated positive and 25,000 negatives. We have selected 4,000 words that have an impact on the feelings of the documents, To predict the rating which is based on a textual customer review. We evaluated the algorithms we proposed, as part of this approach, and compared the effectiveness of the following machine learning techniques as classifiers for SA: extreme gradient boosting (XGBoost), artificial neurons (ANN), and stochastic gradient descent (SGD). with a vectorial construction by applying the techniques of ”Term Frequency” (TF) and that of the ”Term frequency-Inverse Document Frequency” (TF-IDF). We noticed that our approach based on the SGD algorithm recorded the best performance. Indeed, we recorded an accuracy of 88.093\%, a precision of 89\%, a recall of 86\%, and an F1-score of 88\%. \textbf{Keywords :} Sentiment analysis; Opinion mining; movie review; classification; Machine learning; XGBoost; Artificial neural network; stochastic gradient descent.

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Section: Proposed Modelmentioning

confidence: 99%

Machine learning-based approach for sentiment analysis from recorded social media movie reviews

Jassim,

Abd,

Omri

2023

Preprint

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“…Addressing the issue of false negatives in mini-batches, some methods propose cancellation and attraction strategies [35]. TMCT introduces a third network with momentum for target learning [36].…”

Section: Introductionmentioning

confidence: 99%

Patch-Wise Self-Supervised Visual Representation Learning: A Fine-Grained Approach

Javidani,

Sadeghi,

Araabi

2024

Preprint

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Self-supervised visual representation learning traditionally focuses on image-level instance discrimination. Our study introduces an innovative, fine-grained dimension by integrating patch-level discrimination into these methodologies. This integration allows for the simultaneous analysis of local and global visual features, thereby enriching the quality of the learned representations. Initially, the original images undergo spatial augmentation. Subsequently, we employ a distinctive photometric patch-level augmentation, where each patch is individually augmented, independent from other patches within the same view. This approach generates a diverse training dataset with distinct color variations in each segment. The augmented images are then processed through a self-distillation learning framework, utilizing the Vision Transformer (ViT) as its backbone. The proposed method minimizes the representation distances across both image and patch levels to capture details from macro to micro perspectives. To this end, we present a simple yet effective patch-matching algorithm to find the corresponding patches across the augmented views. Thanks to the efficient structure of the patch-matching algorithm, our method reduces computational complexity compared to similar approaches. Consequently, we achieve an advanced understanding of the model without adding significant computational requirements. We have extensively pretrained our method on datasets of varied scales, such as Cifar10, ImageNet-100, and ImageNet-1K. It demonstrates superior performance over state-of-the-art self-supervised representation learning methods in image classification and downstream tasks, such as copy detection and image retrieval.

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Comprehensive review and comparative analysis of transformer models in sentiment analysis

Bashiri,

Naderi

2024

Knowl Inf Syst

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A two-stage unsupervised sentiment analysis method

Cited by 4 publications

References 21 publications

Machine learning-based approach for sentiment analysis from recorded social media movie reviews

Machine learning-based approach for sentiment analysis from recorded social media movie reviews

Patch-Wise Self-Supervised Visual Representation Learning: A Fine-Grained Approach

Comprehensive review and comparative analysis of transformer models in sentiment analysis

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