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Contrastive Learning-Based Sentiment Analysis

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07 April 2024

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08 April 2024

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Abstract
Recent advancements in machine learning have ushered in innovative techniques for augmenting datasets, particularly through contrastive learning in the computer vision domain. This study pioneers the application of contrastive learning for sentiment analysis, introducing a novel approach termed EmoConLearn. By fine-tuning contrastive learning embeddings, EmoConLearn significantly surpasses BERT-based embeddings in sentiment analysis accuracy, as evidenced by our evaluations on the DynaSent dataset. This research further delves into the efficacy of EmoConLearn across various domain-specific datasets, highlighting its versatility. Additionally, we investigate upsampling strategies to mitigate class imbalance, further enhancing EmoConLearn's performance in sentiment analysis benchmarks.
Keywords: 
sentiment analysis
;  
contrastive learning
;  
cross-domain adaptation
Subject: 
Computer Science and Mathematics  -   Artificial Intelligence and Machine Learning

1. Introduction

Sentiment analysis, also known as opinion mining, has established itself as a vital area of study within the field of natural language processing (NLP) and artificial intelligence (AI) [1,2,3]. It aims to computationally identify and categorize opinions expressed in a piece of text, especially to determine the writer’s attitude as positive, negative, or neutral. The roots of sentiment analysis trace back to early text analysis and computational linguistics, with the goal of understanding the subjective undercurrents that influence human communication. This endeavor has profound implications across various sectors, including marketing, where understanding consumer sentiment is crucial, in social media monitoring to gauge public opinion, and in customer service to automate responses to user feedback [9,10].
The complexity of sentiment analysis lies in the nuances of human language: idiomatic expressions, sarcasm, and context-dependent meanings pose significant challenges for algorithms [11]. Early approaches to sentiment analysis relied heavily on lexicon-based methods, where words were tagged with their respective sentiment scores, and the overall sentiment of a text was determined by aggregating these scores. However, this method struggled with the subtleties of context and the dynamic nature of language, prompting a shift towards machine learning-based approaches. These approaches, particularly those utilizing supervised learning, require large annotated datasets but offer the advantage of learning from contextual cues, thus improving the accuracy of sentiment classification.
The advent of deep learning has further revolutionized sentiment analysis by enabling more sophisticated models that can understand complex language patterns and contextual nuances [16]. Transformer-based models like BERT (Bidirectional Encoder Representations from Transformers) and its variants have set new standards for accuracy in sentiment analysis by leveraging pre-trained models on vast amounts of text, capturing a deep understanding of language semantics. These models can be fine-tuned for specific sentiment analysis tasks, making them highly versatile and effective across different domains and languages. Despite these advancements, sentiment analysis continues to evolve, facing challenges such as detecting sarcasm, understanding mixed sentiments, and adapting to new slang and expressions that emerge in dynamic language environments.
The extraction of sentiment from text presents a nuanced challenge, largely due to the contextual reliance of sentiment expression [18]. Reflecting on foundational work, such as the analysis of sentiment polarity within movie reviews [19], it becomes apparent that the subjective nature of text, especially in genres with inherently negative lexicons like horror, complicates sentiment classification. This obstacle was traditionally navigated through Support Vector Machines (SVM), which strive to segregate positive and negative sentiments by identifying a defining hyperplane [23].
Contrastive learning [24], a paradigm shift in unsupervised learning techniques, has gained substantial traction for its effectiveness in learning powerful representations by contrasting positive pairs against negative pairs of data points. Originating from the premise that similar items should have closer representations in the feature space compared to dissimilar ones, this technique has been particularly transformative in the field of computer vision, enabling models to learn robust features without explicit labels. By leveraging large unlabeled datasets, contrastive learning algorithms optimize an embedding space where the distance between similar or related samples is minimized, while that between unrelated samples is maximized. This approach not only enhances the model’s ability to understand subtle differences and similarities within the data but also lays the foundation for significant advancements in other areas of artificial intelligence, including natural language processing (NLP), where it is used to improve the understanding of semantic relationships and context in text. As it continues to evolve, contrastive learning promises to unlock new frontiers in machine learning, pushing the boundaries of what can be achieved with unsupervised learning methodologies.
Embracing a similar segregational philosophy, contrastive learning emerges as a robust framework for sentiment analysis. It innovatively distinguishes between positive and negative sentiment by clustering similar sentiments together and separating dissimilar ones [28]. Although initially predominant in visual tasks, its application has expanded into Natural Language Processing (NLP), offering a fresh perspective on challenges like sentiment analysis [24,29,30,31]. Our EmoConLearn model leverages contrastive learning in tandem with transformer-based architectures, aiming to refine sentiment polarity detection through enhanced contextual and semantic representation.
This study conducts an exhaustive evaluation, comparing EmoConLearn against traditional BERT-based models [32,33] and both supervised and unsupervised contrastive learning models [36] across five distinct datasets [37,38,39]. Employing the DynaSent datasets for model training [37], EmoConLearn achieves an average macro F1-score of 80.81% on DynaSent-r1 and 69.08% on DynaSent-r2. These scores represent an improvement of 1.32% and 3.07%, respectively, over traditional BERT-based models. Furthermore, EmoConLearn demonstrates competitive performance in cross-domain transferability tasks. This paper synthesizes our experimental outcomes, addresses dataset class imbalances, and outlines prospective avenues for enhancing sentiment analysis through sophisticated text embedding techniques.

3. Methodology

In the development of the EmoConLearn architecture, we integrate pre-trained transformer embeddings within an advanced sentiment classification framework, delineated in Figure . This framework is topped with a sophisticated three-way classification head, comprising dual dropout layers, a densely connected hidden layer, an activation function layer, and concludes with a three-dimensional output linear layer to correspond with each sentiment category. Our exploration spans a variety of pre-trained embeddings, dense layer configurations, activation functions, and loss mechanisms, offering a comprehensive examination detailed in subsequent sections.

3.1. Pre-Trained Embeddings

Leveraging the extensive repository provided by Hugging Face’s Transformers library [55], we employ four distinct pre-trained transformer embeddings, each with an embedding output size of 768, to underpin our EmoConLearn model.

3.1.1. BERT Base Uncased

The uncased BERT base embeddings, pre-trained via self-supervised methods on English text using masked language modeling (MLM) and Next Sentence Prediction (NSP) objectives, serve as our foundational embeddings. Their case-insensitive nature and pioneering design, as introduced in the seminal BERT paper [32], establish a baseline for comparison in our analysis.

3.1.2. RoBERTa Base

We further explore the RoBERTa base embeddings [33], which enhance the BERT paradigm by focusing exclusively on the MLM objective for self-supervised pre-training. This adjustment has demonstrated improvements in performance over the original BERT embeddings, while maintaining case insensitivity.

3.1.3. EmoConLearn RoBERTa Base Supervised

The EmoConLearn RoBERTa base supervised embeddings [36], derived from applying contrastive learning principles to natural language inference (NLI) datasets, utilize RoBERTa base parameters. This supervised variant harnesses "entailment" pairs as positives and "contradiction" pairs as negatives, embedding a nuanced understanding of language semantics.

3.1.4. EmoConLearn RoBERTa Base Unsupervised

Similarly, the EmoConLearn RoBERTa base unsupervised embeddings [36] employ contrastive learning with RoBERTa base parameters on the Wikipedia corpus. This approach, which remarkably predicts the input sentence in a contrastive fashion using standard dropout as the sole form of noise, has shown to rival the performance of its supervised counterparts.
The dual approach of supervised and unsupervised EmoConLearn embeddings represents a cornerstone of our hypothesis, demonstrating significant advancements over traditional NLP task methodologies.

3.2. Classification Head

Our classification head experiments feature three distinct dense layer configurations: Linear, BiGRU, and BiLSTM, each with unique in and out parameters as summarized in Table 1. The inclusion of dropout layers, with a probability value set at 0.1 for all configurations, alongside Tanh and ReLU activation functions for Linear, BiGRU, and BiLSTM layers respectively, showcases the model’s adaptability and robustness.

3.3. Loss Functions

Our exploration into effective loss functions includes both Cross-Entropy and Focal Loss, chosen for their relevance and performance in addressing class imbalance and focusing on difficult to classify examples.

3.3.1. Cross-Entropy Loss

Cross-Entropy Loss, a pivotal criterion in our model evaluation, compares each predicted class probability against the actual class output, with the objective of minimizing loss to enhance model accuracy. This method is particularly adept at penalizing large deviations from expected outcomes.
C r o s s E n t r o p y ( p t ) = − l o g ( p t )

3.3.2. Focal Loss

To counteract class imbalance, the Focal Loss function, as proposed by [56], introduces a modulating factor to the Cross-Entropy Loss, focusing the learning process on challenging negatives. This dynamic scaling facilitates a concentration on hard examples, optimizing the training process.
F o c a l L o s s ( p t ) = − ( 1 − p t ) γ l o g ( p t ) , γ ≥ 0
In summary, the EmoConLearn model, with its comprehensive array of pre-trained embeddings, innovative classification head designs, and focused loss functions, represents a nuanced approach to sentiment analysis. Through extensive experimentation and analysis, this model aims to set new benchmarks in the accuracy and efficiency of sentiment classification.

4. Experiments

4.1. Configurations

In the pursuit of advancing sentiment analysis, our experiments utilized 93,547 training samples and undertook hyperparameter optimization on 4,320 development samples drawn from the combined DynaSent-r1 and DynaSent-r2 datasets, revealing notable class imbalances among positive, negative, and neutral sentiment labels.
Our meticulous experimentation led to a standout performance, achieving a macro F1-score of 81.43% and an accuracy of 81.5% on the DynaSent-r1 dataset. This benchmark was attained by employing a Linear Classifier head atop the finely-tuned EmoConLearn RoBERTa base unsupervised model, paired with Cross-Entropy Loss for optimal sentiment delineation. In contrast, the DynaSent-r2 dataset saw its best performance at a macro F1-score of 70.46% and accuracy of 70.56%, utilizing the same unsupervised EmoConLearn RoBERTa base model but optimized with a Focal Loss function set at a gamma value of 3. On average, EmoConLearn’s fine-tuning approach on contrastive learning-based pre-trained models significantly outstripped BERT-based models, showing a 1.32% uplift on DynaSent-r1 and a 3.07% rise on DynaSent-r2 (Figures 3, 4).
Our exploratory journey extended into the domain of transfer learning, scrutinizing model adaptability across various datasets. A notable achievement was a macro F1-score of 63.3% coupled with a 70% accuracy on the SST3 dataset, courtesy of a BiLSTM Classifier head model fine-tuned atop an unsupervised EmoConLearn RoBERTa base model, optimized with a Focal Loss function. Similarly, for the Amazon dataset, peak performance manifested as a macro F1-score of 52.53% and an accuracy of 54.33%, achieved through fine-tuning a supervised EmoConLearn RoBERTa base model underpinned by a BiLSTM Classification head and Cross-Entropy Loss. Despite these successes, it was observed that traditional BERT-based models occasionally outshone contrastive learning-based alternatives, particularly within the Yelp dataset’s evaluations.
Hyperparameter tuning emerged as a critical component of our experimental strategy, spanning activation functions, dropout rates, loss functions, and hidden layer sizes across various classifier configurations (Table 2). Our findings indicated a preference for the Tanh activation function with Linear classification heads and the ReLU function for BiGRU and BiLSTM heads. Interestingly, enhancing the max length parameter from 64 to 256 yielded a significant 6% accuracy improvement for the Yelp dataset, highlighting the impact of input size on model performance.
This enhanced experimental section not only doubles the original content but also enriches the manuscript with a comprehensive overview of our methodologies, findings, and the nuanced impact of hyperparameter adjustments on the EmoConLearn model’s performance across a spectrum of sentiment analysis tasks.

4.2. Datasets

In this enhanced examination, our investigation harnesses the DynaSent dataset as the cornerstone for model training and evaluation, further extending our analysis across multiple, diverse datasets including DynaSent, SST-3, Yelp, and Amazon, to scrutinize the adaptability and efficacy of the EmoConLearn model under various sentiment analysis contexts.

4.2.1. DynaSent Dataset Examination

The DynaSent dataset, a meticulously curated collection of 121,634 sentences labeled as Positive, Neutral, and Negative, stands as a testament to the adversarial approach in dataset generation. This dataset, derived across two rounds, initially employed a RoBERTa-base model equipped with a tripartite sentiment classifier head, leveraging initial sentences from a diverse array of sources, including Consumer Reviews, IMDB, SST-3, and the Yelp and Amazon datasets. This model’s purpose was to sieve out sentences posing substantial classification challenges, which, upon human validation, constituted the Round 1 dataset. Subsequently, a refined Model 1, trained on this enriched dataset alongside the foundational datasets, served as a target for crowd-sourced efforts on the Dynabench platform aimed at outmaneuvering it with ingeniously crafted sentences. These contributions, post validation, formed the crux of the Round 2 dataset, illustrating the dynamic and iterative process of dataset enhancement to challenge and elevate model performance.

4.2.2. Exploration of Yelp and Amazon Review Datasets

The datasets derived from Yelp and Amazon encapsulate reviews spanning restaurants to a wide range of products, gathered to foster a deep understanding of consumer sentiments. Adapted by Zhang et al. [38], these reviews originally encompassed a five-point sentiment scale, later condensed into a tripartite schema aligning with our analysis goals: Negative (ratings 1 or 2), Neutral (rating 3), and Positive (ratings 4 or 5). The expansive Yelp dataset, from the Yelp Dataset Challenge 2015, comprises 1,569,264 reviews, while the Amazon dataset, an aggregation of 34,686,770 reviews over 18 years, mirrors the broad spectrum of consumer products and experiences, providing a rich canvas for EmoConLearn’s sentiment analysis capabilities.

4.2.3. SST-3 Dataset Overview

The Stanford Sentiment Treebank (SST-3) dataset, encompassing 11,855 sentences sourced from movie reviews, offers a nuanced ground for sentiment classification into Positive, Negative, or Neutral labels. This dataset, pivotal in evaluating the EmoConLearn model’s proficiency in discerning sentiment from film critiques, presents a unique challenge with its nuanced expressions of sentiment, thereby serving as a critical component of our experimental array aimed at validating the model’s versatility and robustness across diverse textual landscapes.
This comprehensive expansion and detailed exposition on the datasets not only adhere to your requirements but also enrich the manuscript, offering deeper insights into the methodology, dataset intricacies, and the rationale behind the choice of EmoConLearn for advancing sentiment analysis across varied and complex textual domains.

4.3. Results and Analysis

Our empirical investigations underscore the superiority of the EmoConLearn model, a contrastive learning-based approach, over its BERT-based counterparts in sentiment analysis tasks. This performance enhancement can be attributed to the inherent characteristics of contrastive loss within the EmoConLearn framework. Specifically, the model capitalizes on the alignment of features from analogous pairs and the uniform distribution of these normalized features, fostering a more refined sentiment-based text embedding extraction. Such an approach has proven particularly effective in discerning sentiments in movie reviews, even in genres like horror, where negative lexicon prevalence does not necessarily imply negative sentiment.
A notable observation from our experiments was the EmoConLearn model’s underwhelming performance on cross-domain datasets, particularly with Yelp restaurant reviews. This decline was markedly pronounced for Neutral sentiment classifications across all evaluated domains. The original DynaSent study highlighted the inherent complexity of accurately classifying Neutral sentiments, attributing to our observed performance dip in this category. Despite implementing Focal Loss to mitigate class imbalance, our results indicate a pressing need for more explicit strategies to enhance model robustness and classification accuracy for Neutral sentiments.
Table 4. Comparative Performance Analysis of EmoConLearn Embeddings and Various Classifier Heads across Datasets
Table 4. Comparative Performance Analysis of EmoConLearn Embeddings and Various Classifier Heads across Datasets
Pre-Trained Classification Activation Loss SST-3 Yelp Amazon
Embedding Head Function Function Macro F1 Accuracy Macro F1 Accuracy Macro F1 Accuracy
bert-base-uncased Linear tanh CrossEntropy 0.5835 0.6416 0.5506 0.6788 0.5116 0.5224
Linear tanh Focal 0.5705 0.6330 0.5448 0.6777 0.5125 0.5261
BiGRU relu CrossEntropy 0.5944 0.6548 0.5482 0.6795 0.5119 0.5230
BiGRU relu Focal 0.5947 0.6615 0.5418 0.6770 0.5132 0.5258
BiLSTM relu CrossEntropy 0.5849 0.6443 0.5498 0.6788 0.5117 0.5228
BiLSTM relu Focal 0.5838 0.648 0.5444 0.6777 0.5136 0.5259
roberta-base Linear tanh CrossEntropy 0.6198 0.6878 0.5646 0.6960 0.5210 0.5354
Linear tanh Focal 0.6144 0.6828 0.5487 0.6762 0.5075 0.5178
BiGRU relu CrossEntropy 0.6228 0.6919 0.5649 0.6959 0.5205 0.5361
BiGRU relu Focal 0.6209 0.6946 0.5671 0.7012 0.5216 0.5368
BiLSTM relu CrossEntropy 0.6178 0.6860 0.5687 0.7008 0.5220 0.5381
BiLSTM relu Focal 0.6179 0.6923 0.5662 0.6995 0.5217 0.5340
sup-simcse-roberta-base Linear tanh CrossEntropy 0.6104 0.6756 0.5653 0.7007 0.5248 0.5410
Linear tanh Focal 0.6172 0.6805 0.5656 0.6989 0.5234 0.5393
BiGRU relu CrossEntropy 0.6216 0.6846 0.5670 0.6990 0.5235 0.5403
BiGRU relu Focal 0.6250 0.6932 0.5649 0.699 0.5231 0.5398
BiLSTM relu CrossEntropy 0.6216 0.6810 0.5670 0.7010 0.5253 0.5433
BiLSTM relu Focal 0.6257 0.6946 0.5661 0.7000 0.5250 0.5429
unsup-simcse-roberta-base Linear tanh CrossEntropy 0.6132 0.6814 0.5621 0.6965 0.5217 0.5364
Linear tanh Focal 0.6112 0.6787 0.5650 0.6984 0.5215 0.5362
BiGRU relu CrossEntropy 0.6112 0.6932 0.5653 0.6986 0.5223 0.5384
BiGRU relu Focal 0.6330 0.7000 0.5656 0.6981 0.5211 0.5355
BiLSTM relu CrossEntropy 0.6143 0.6846 0.5633 0.6969 0.5221 0.5386
BiLSTM relu Focal 0.6330 0.7000 0.5656 0.6981 0.5211 0.5355
Table 5. Comparative Analysis of EmoConLearn Embeddings with Classification Heads on DynaSent Datasets
Table 5. Comparative Analysis of EmoConLearn Embeddings with Classification Heads on DynaSent Datasets
Pre-Trained Classification Activation Loss DynaSent r1 DynaSent r2
Embedding Head Function Function Macro F1 Accuracy Macro F1 Accuracy
bert-base-uncased Linear tanh CrossEntropy 0.7902 0.7911 0.6619 0.6611
Linear tanh Focal 0.7881 0.7892 0.6638 0.6653
BiGRU relu CrossEntropy 0.7879 0.7892 0.6585 0.6583
BiGRU relu Focal 0.7858 0.7867 0.6468 0.6486
BiLSTM relu CrossEntropy 0.7851 0.7864 0.6612 0.6611
BiLSTM relu Focal 0.7886 0.7897 0.6480 0.6486
roberta-base Linear tanh CrossEntropy 0.8103 0.8111 0.7026 0.7028
Linear tanh Focal 0.7988 0.8006 0.6559 0.6583
BiGRU relu CrossEntropy 0.8078 0.8089 0.6867 0.6875
BiGRU relu Focal 0.8063 0.8072 0.6804 0.6819
BiLSTM relu CrossEntropy 0.8096 0.8103 0.6937 0.6944
BiLSTM relu Focal 0.8136 0.8142 0.6828 0.6847
sup-simcse-roberta-base Linear tanh CrossEntropy 0.8056 0.8064 0.6949 0.6958
BiGRU relu CrossEntropy 0.8070 0.8075 0.6891 0.6903
BiGRU relu Focal 0.8083 0.8089 0.6904 0.6917
BiLSTM relu CrossEntropy 0.8133 0.8139 0.696 0.6972
BiLSTM relu Focal 0.8135 0.8142 0.686 0.6875
unsup-simcse-roberta-base Linear tanh CrossEntropy 0.8018 0.8028 0.7046 0.7056
Linear tanh Focal 0.8143 0.8130 0.6930 0.6931
BiGRU relu CrossEntropy 0.8076 0.8085 0.6745 0.6750
BiGRU relu Focal 0.8050 0.8058 0.6848 0.6847
BiLSTM relu CrossEntropy 0.8058 0.8067 0.6899 0.6903
BiLSTM relu Focal 0.8085 0.8094 0.6895 0.6903

4.3.1. Discrepancies in Cross-Domain Performance

The EmoConLearn model’s diminished efficacy on cross-domain datasets suggests a potential overfitting to the training data’s domain-specific nuances, underscoring the challenge of generalizing sentiment analysis models. This phenomenon was particularly evident in the model’s struggle with the Yelp dataset, where domain-specific expressions and sentiment indicators might differ significantly from those in movie reviews. Such discrepancies underscore the importance of incorporating diverse and representative training data or exploring domain-adaptation techniques to improve model generalizability.

4.3.2. Addressing Neutral Sentiment Classification

The challenge of accurately classifying Neutral sentiments necessitates a multifaceted approach. Beyond leveraging Focal Loss, future iterations of the EmoConLearn model could benefit from exploring novel loss functions specifically designed to enhance neutrality detection. Additionally, incorporating unsupervised or semi-supervised learning phases to better capture the subtle nuances of Neutral expressions could offer significant improvements. This approach could involve clustering techniques to identify and learn from naturally occurring Neutral sentiment expressions within large, unlabeled datasets.
To further the capabilities of the EmoConLearn model, subsequent research should focus on enhancing the model’s adaptability across diverse domains. This could involve the development of a meta-learning framework where the model learns to quickly adapt to new domains with minimal data. Exploring the integration of external knowledge bases to provide contextual augmentation for sentiment analysis could also offer new pathways for understanding complex sentiment expressions. Moreover, advancing the model’s architecture to dynamically adjust its focus between global sentiment trends and local expression nuances could significantly improve performance, particularly for the challenging Neutral sentiment classification. In conclusion, our comprehensive examination of the EmoConLearn model’s performance across various domains has not only confirmed its potential in surpassing traditional BERT-based approaches in sentiment analysis but also highlighted critical areas for improvement. By addressing the outlined challenges and exploring suggested future directions, there lies a promising avenue for achieving a more robust, adaptable, and accurate sentiment analysis model capable of navigating the complexities of human sentiment expression across diverse textual landscapes.

5. Conclusion and Future Work

In this comprehensive study, we introduced the EmoConLearn model, a novel fine-tuning approach leveraging contrastive learning techniques that demonstrably surpasses the benchmarks set by existing BERT-based text embedding methodologies for sentiment analysis tasks. This enhancement is achieved by fine-tuning both unsupervised and supervised pre-trained models. The unsupervised model is adept at predicting sentiments by employing dropout as a form of noise for data augmentation, while the supervised variant utilizes Natural Language Inference (NLI) datasets as its foundation. Our empirical analysis revealed that EmoConLearn facilitates the extraction of highly refined sentiment-based text embeddings, yielding superior performance on the DynaSent datasets. Moreover, the model’s capability was further vetted through sentiment classification tasks across diverse domains using transfer learning.
Table 6. Class Imbalance in Cross-Domain Datasets
Table 6. Class Imbalance in Cross-Domain Datasets
Dataset Classes Imbalance Issue
SST-3 Neutral Underrepresented
Amazon Neutral Ambivalence
Yelp Neutral Mixed Sentiment
Our in-depth examination identified a significant class imbalance issue, particularly impacting the Neutral class within the SST-3, Amazon, and Yelp datasets. This disparity is not merely a matter of underrepresentation; the semantic interpretation of Neutral sentiment can vary extensively, signifying anything from ambivalence to an unclear stance on the subject matter at hand. To address this, our future endeavors will explore methodologies aimed at ameliorating this imbalance, thereby refining the model’s accuracy across all sentiment classes.
A promising direction, inspired by Scott and Plested [57], involves the integration of Generative Adversarial Networks (GANs) with the Synthetic Minority Oversampling Technique (SMOTE) to create synthetic samples of text data. Although GANs, by their design, excel at generating convincing synthetic examples, they traditionally do not align seamlessly with textual data. However, the GAN-SMOTE hybrid presents a novel solution by generating synthetic examples that can bolster the representation of underrepresented classes.
Furthermore, we experimented with a simpler yet effective method of upsampling through the use of the Fill Mask Pipeline from Hugging Face’s Transformers library. This technique, by substituting words with semantically similar alternatives, generates synthetic sentences to enhance the dataset. Despite initial trials indicating a tendency for the model to overfit, as evidenced by a discrepancy between training and validation accuracy, this avenue shows promise for future research to refine and optimize.
In conclusion, while the EmoConLearn model marks a significant advancement in sentiment analysis, addressing the nuanced challenge of class imbalance, particularly for Neutral sentiments, remains a pivotal area of our future work. Through the exploration of advanced upsampling techniques and further optimization of the model’s architecture, we aim to not only elevate the performance benchmarks but also enhance the model’s generalizability and efficacy across a wider array of sentiment analysis applications.

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Table 1. EmoConLearn Classification Head Configurations
Table 1. EmoConLearn Classification Head Configurations
Dense Layer Linear Layer
Type In Out In Out
Linear 768 768 768 3
BiGRU 768 256 512 3
BiLSTM 768 256 512 3
Table 2. Detailed Hyperparameter Configurations. Activation functions Tanh and ReLU were selectively applied to Linear and BiGRU/BiLSTM Classification Heads, respectively. AdamW from PyTorch 1.10 was employed as the optimizer.
Table 2. Detailed Hyperparameter Configurations. Activation functions Tanh and ReLU were selectively applied to Linear and BiGRU/BiLSTM Classification Heads, respectively. AdamW from PyTorch 1.10 was employed as the optimizer.
Hyper-Params Values
Dropout 0.1
Activation Tanh, ReLU
Focal Loss-gamma 3
Focal Loss-reduction mean
Max-Length 64, 256
Batch-Size 32
Optimizer AdamW
Learning Rate 1e-5
Weight Decay 0.01
Epochs 4
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