QUANTUM SENTIMENT ANALYSIS: POTENTIAL, BENCHMARKING AND LIMITATIONS

Abstract

Sentiment analysis is one of the most frequent activities of natural language processing (NLP) that has extensive use cases in social media monitoring, business intelligence, and analysis of public opinion. With their ability to scale effectively to large sentiments datasets, although deep-learning methods like recurrent neural networks have reached high-performance, their increasing computational (energy) requirements have stimulated a search toward quantum computations and quantum-inspired computations. Nevertheless, the current literature tends to test quantum NLP algorithms using non-congruent experimental conditions, which precludes determining their feasibility on practice in comparison to classical base- lines. In this thesis, a coherent and repeatable comparison of the classical, deep learning and quantum-enhanced sentiment analysis algorithms are compared within a unified evaluation procedure. Two popular and widely-used benchmark datasets with markedly distinct linguistic features, namely Twitter entity sentiment data, and IMDB movie reviews are experimented with. The compared models are traditional machine learning pipelines, using TF -IDF features, LSTM-based deep learning models, and hybrid quantum -classical sequence models (QLSTM) and diagrammatic quantum NLP (QNLP) models in the form of lambeq toolkit. Training and evaluation of all models are performed with the help of similar preprocessing, data splits and performance metrics. The findings indicate that powerful classical baselines retain highly competitive especially in small-scale text sentiment analysis of Twitter, and LSTM models are efficient in long IMDB reviews. The quantum-inspired methods that have been shown to perform well with an equal or better result include quantum-inspired hybrid pipelines that show good results and in a few of the configurations they may even do better than the classifiers which are completely classical in nature. Conversely, QLSTM set-ups tested demonstrate poor learning and cannot compete with the near-term quantum limitations. Generally, the results are that in the short-term quantum advantage in sentiment analysis is more probable to be obtained via well-crafted hybrid quantum-classical pipelines than entirely worked-out quantum sequence frameworks. This thesis offers a controlled benchmark and real-life observations that can add to the future evaluation of quantum-enhanced NLP in practical scenarios.

Date of Award	2026
Original language	American English
Awarding Institution	HBKU College of Science and Engineering