Benchmarking and Design of Hybrid Transformer-Quantum Classifiers for.pptx

Benchmarking and Design of Hybrid Transformer-Quantum Classifiers

1. Benchmarking and Design of Hybrid Transformer-Quantum Classifiers for Enhanced Sentiment Analysis on Limited Datasets

2. 1. Introduction and Background Sentiment Analysis (SA) is a core task in NLP, vital for understanding public opinion from sources like social media. Current State-of-the-Art (SOTA) performance is dominated by Transformer Models (BERT,  RoBERTa ), which excel due to massive pre-training. However, these models are computationally expensive and can struggle with generalization on highly limited or domain-specific datasets (like the CVTD mentioned in the flow chart) due to the risk of overfitting or high fine-tuning costs.

3. Problem Traditional machine learning and neuro-fuzzy approaches for Sentiment Analysis (SA) face several challenges due to the rapid growth and complexity of textual data, especially in noisy environments. These limitations include:

4. Problem: Failure to efficiently handle noise or outliers. Difficulty in scaling effectively to high-dimensional data. Inability to interpret results accurately or determine the optimal number of clusters. A tendency to be insensitive to input variations. Struggling to maintain both accuracy and efficiency when processing large-scale datasets.

5. 2. Problem Statement Dataset Size & Variety : This section addresses the limitation of having small or specific datasets for training QML models, leading to a need for leveraging classical techniques. Only CVTD and Generalized Sentimental Tweets Dataset (Limited Data) Pretrained Transformer Models Solution: Hybrid Q-Quantum-Classical Models : Hybrid quantum-classical algorithms are computational methods designed to combine quantum and classical computing resources to solve problems more effectively than either could alone.

6. Hybrid Q-Quantum-Classical Models

7. Comparison of Transeformer Model with Fuzzy neural Network Superior contextual understanding Uses a self-attention mechanism to weigh the importance of all words in a sentence relative to each other. This allows it to grasp complex linguistic nuances like negation, sarcasm, and long-range dependencies, which are vital for accurate sentiment analysis. For example, it can understand that "The food wasn't bad" is a positive statement. Processes input based on pre-defined fuzzy rules and membership functions. FNNs can struggle with the fluidity of language and have difficulty capturing context, especially with vague, ambiguous, or conversational text.

8. After analyzing the provided research paper and conducting comprehensive research, I can identify several superior alternatives to fuzzy neural networks for sentiment analysis, along with future enhancement recommendations. Transformer-Based Models

9. BERT and RoBERTa  represent the most significant advancement in sentiment analysis. These transformer models have revolutionized the field through their self-attention mechanisms, which enable better capture of syntactic and semantic dependencies compared to traditional neural networks. RoBERTa  consistently outperforms BERT by removing the Next Sentence Prediction (NSP) task and using dynamic masking, achieving superior performance with similar computational requirements DeBERTa  offers similar performance to RoBERTa but requires approximately twice the computational power These models demonstrate substantial improvements in accuracy, precision, and recall across various sentiment analysis datasets

10. Proposed Solution (Quantum Fuzzy Neural Network - QFNN) In the context of the provided paper on SentiQNF (a quantum fuzzy neural network for sentiment analysis), quantum transformers offer a promising alternative or enhancement to the fuzzy layers, enabling more efficient modeling of linguistic uncertainties (e.g., sarcasm, negation) through quantum superposition and entanglement, potentially boosting the 90-100% accuracy reported on Twitter datasets while reducing parameter counts.