Team Error Point at BLP-2023 Task 2: A Comparative Exploration of Hybrid Deep Learning and Machine Learning Approach for Advanced Sentiment Analysis Techniques.

AbstractThis paper presents a thorough and extensive investigation into the diverse models and techniques utilized for sentiment analysis. What sets this research apart is the deliberate and purposeful incorporation of data augmentation techniques with the goal of improving the efficacy of sentiment analysis in the Bengali language. We systematically explore various approaches, including preprocessing techniques, advancedmodels like Long Short-Term Memory (LSTM) and LSTM-CNN (Convolutional Neural Network) Combine, and traditional machine learning models such as Logistic Regression, Decision Tree, Random Forest, Multi-Naive Bayes, Support Vector Machine, and Stochastic Gradient Descent. Our study highlights the substantial impact of data augmentation on enhancing model accuracy and understanding Bangla sentiment nuances. Additionally, we emphasize the LSTM model’s ability to capture long-range correlations in Bangla text. Our system scored 0.4129 and ranked 27th among the participants.