Multi-layers Convolutional Neural Network for Twitter Sentiment Ordinal Scale Classification

Alali, Muath; Sharef, Nurfadhlina Mohd; Hamdan, Hazlina; Murad, Masrah Azrifah Azmi; Husin, Nor Azura

doi:10.1007/978-3-319-72550-5_43

Cited by 12 publications

(8 citation statements)

References 15 publications

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“…Existing ML and DL approaches in the literature attempt to model ordinal constraints in different ways [15]. The most naive methods range from performing a simple regression using the class labels and rounding the values on the prediction phase to using a costsensitive (e.g., cost matrix) approach to evaluate multiclass classification models [16]. However, regression learners may depend on the values used for representing the labels, while the cost matrix can lead to different label representations, thus leading to different ambiguous solutions.…”

Section: A Ordinal Classificationmentioning

confidence: 99%

Learning Ordinal–Hierarchical Constraints for Deep Learning Classifiers

Rosati,

Romeo,

Vargas

et al. 2024

IEEE Trans. Neural Netw. Learning Syst.

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Real-world classification problems may disclose different hierarchical levels where the categories are displayed in an ordinal structure. However, no specific deep learning (DL) models simultaneously learn hierarchical and ordinal constraints while improving generalization performance. To fill this gap, we propose the introduction of two novel ordinal-hierarchical DL methodologies, namely, the hierarchical cumulative link model (HCLM) and hierarchical-ordinal binary decomposition (HOBD), which are able to model the ordinal structure within different hierarchical levels of the labels. In particular, we decompose the hierarchical-ordinal problem into local and global graph paths that may encode an ordinal constraint for each hierarchical level. Thus, we frame this problem as simultaneously minimizing global and local losses. Furthermore, the ordinal constraints are set by two approaches [ordinal binary decomposition (OBD) and cumulative link model (CLM)] within each global and local function. The effectiveness of the proposed approach is measured on four real-use case datasets concerning industrial, biomedical, computer vision, and financial domains. The extracted results demonstrate a statistically significant improvement to state-ofthe-art nominal, ordinal, and hierarchical approaches.

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Section: A Ordinal Classificationmentioning

confidence: 99%

Learning Ordinal–Hierarchical Constraints for Deep Learning Classifiers

Rosati,

Romeo,

Vargas

et al. 2024

IEEE Trans. Neural Netw. Learning Syst.

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show abstract

“…• NCNN: narrow convolutional neural network structure trained on top of word embedding. The model consists of three convolutional layers, each one followed by max-pooling proposed by [18].…”

Section: Benchmark Approachesmentioning

confidence: 99%

“…In contrast, deep learning (DL) approaches for SA, such as recurrent neural network (RNN) [17], convolutional neural network (CNN) [18][19][20][21], and recursive auto encoder (RAE) [22], have been identified as having the ability to provide superior adaptability and robustness in the past few years by extracting features automatically. However, deep neural network (DNN) approaches in Arabic dialect SA achievement are still limited in number compared with its applications in other areas, including chatbot [23], recommendation systems [24,25], remote sensing [26], and load monitoring [27].…”

Section: Introductionmentioning

confidence: 99%

Multitasking Learning Model Based on Hierarchical Attention Network for Arabic Sentiment Analysis Classification

et al. 2022

Self Cite

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Limited approaches have been applied to Arabic sentiment analysis for a five-point classification problem. These approaches are based on single task learning with a handcrafted feature, which does not provide robust sentence representation. Recently, hierarchical attention networks have performed outstandingly well. However, when training such models as single-task learning, these models do not exhibit superior performance and robust latent feature representation in the case of a small amount of data, specifically on the Arabic language, which is considered a low-resource language. Moreover, these models are based on single task learning and do not consider the related tasks, such as ternary and binary tasks (cross-task transfer). Centered on these shortcomings, we regard five ternary tasks as relative. We propose a multitask learning model based on hierarchical attention network (MTLHAN) to learn the best sentence representation and model generalization, with shared word encoder and attention network across both tasks, by training three-polarity and five-polarity Arabic sentiment analysis tasks alternately and jointly. Experimental results showed outstanding performance of the proposed model, with high accuracy of 83.98%, 87.68%, and 84.59 on LABR, HARD, and BRAD datasets, respectively, and a minimum macro mean absolute error of 0.632% on the Arabic tweets dataset for five-point Arabic sentiment classification problem.

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“…Accordingly, tweets of length 30 are used in training deep proposed model, the reason -frequency of word vectors is preferred over pre-trained word embeddings. Alali et al [68] proposed a Multi-Layered CNN (MLCNN) to classify tweets into five scales -highly positive, positive, neutral, negative, and highly negative. Nevertheless, after empirical evaluation of the proposed model, the authors found 3-layered CNN, the best among all other combinations of layers.…”

Section: ) Classification Using DLmentioning

confidence: 99%

Towards Deep Learning Prospects: Insights for Social Media Analytics

et al. 2019

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Deep learning (DL) has attracted increasing attention on account of its significant processing power in tasks, such as speech, image, or text processing. In order to the exponential development and widespread availability of digital social media (SM), analyzing these data using traditional tools and technologies is tough or even intractable. DL is found as an appropriate solution to this problem. In this paper, we keenly discuss the practiced DL architectures by presenting a taxonomy-oriented summary, following the major efforts made toward the SM analytics (SMA). Nevertheless, instead of the technical description, this paper emphasis on describing the SMA-oriented problems with the DL-based solutions. To this end, we also highlight the DL research challenges (such as scalability, heterogeneity, and multimodality) and future trends. INDEX TERMS Social media data, dynamic network, deep learning, feature learning. I. INTRODUCTION

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Multi-layers Convolutional Neural Network for Twitter Sentiment Ordinal Scale Classification

Cited by 12 publications

References 15 publications

Learning Ordinal–Hierarchical Constraints for Deep Learning Classifiers

Learning Ordinal–Hierarchical Constraints for Deep Learning Classifiers

Multitasking Learning Model Based on Hierarchical Attention Network for Arabic Sentiment Analysis Classification

Towards Deep Learning Prospects: Insights for Social Media Analytics

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