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

Abstract: This 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.

## introduction sentiment analysis, the process of extracting emotional information from textual data, has witnessed significant advancements in recent years. our participation in the sentiment analysis shared task-2 at the blp workshop during emnlp 2023 underscores our progress in bangla language processing (blp) and sentiment analysis @xcite . this study arises from the critical need to address sentiment expression issues specific to bangla, a language with distinct linguistic nuances. additionally, with the proliferation of bangla content online, effective sentiment analysis tools are invaluable for applications ranging from social media monitoring to customer feedback analysis. @xcite pronoun replacement-based special tagging system (prs-ts) highlights context-specific language, improving bangla sentiment analysis. the use of a broad multitask transformer network (bmt-net) showed that multitask learning works in sentiment analysis @xcite . @xcite convolution over hierarchical syntactic and lexical graphs revealed ways to use syntactic and lexical information for aspect-level sentiment analysis. @xcite convolutional multi-head self-attention on memory improved aspect sentiment categorization. the fusion strategy by @xcite for hate speech detection and the augmentation of bert representations with contextaware embedding demonstrate contextual embeddings potential in sentiment analysis @xcite . @xcite bangla sentiment analysis uses word embeddings to adapt to different languages. long short-term memory (lstm) networks in hardware-accelerated sentiment analysis have also expanded this field @xcite . twitter is a popular social media tool for sentiment research. @xcite use of heterogeneous multi-layer network representation and embedding shows new ways to look at unstructured textual data. our comprehensive study uses conventional preprocessing methods, advanced models like long short-term memory (lstm) and lstm-cnn combine, and traditional machine learning models like logistic regression, decision tree, random forest, multi-naive bayes, support vector machine (svm), and stochastic gradient descent (sgd). deliberate data augmentation is a hallmark of our study. strategic augmentation has improved our dataset and sentiment analysis approaches, demonstrating data augmentation's ability to improve model accuracy and illuminate bangla sentiment expression. we analyse lstm and lstm-cnn models with and without data augmentation as our main focus. we use dataset partition, performance evaluation criteria, and extensive per-class analysis in our experiments. the following discussion emphasises data augmentation's importance for model efficacy. comparing lstm models to combined lstm-cnn models shows that the former captures long-range correlations in bangla text better, advancing bangla sentiment analysis research. 1 final implementation with an anonymous github link 2 . ## literature review recent studies in sentiment analysis, particularly in bangla language processing (blp), have catalysed the field @xcite . a key aspect of this progress lies in the development of specialised techniques for bangla sentiment analysis. @xcite showed how word embeddings can be used in different linguistic settings. another study by @xcite ) looked into more complex models, specifically how to group opinions in bangla sentences. considering structural aspects in sentiment analysis, @xcite engineered an automated system for sentiment analysis from bangla text using supervised learning techniques. @xcite underscored the flexibility of deep learning techniques through a comprehensive sentiment analysis spanning various domains. innovative methodologies are exemplified by @xcite bi-directional lstm-cnn model, placing emphasis on fine-grained sentiment information extraction. @xcite introduced an emotion-semantic-enhanced bidirectional lstm with a multi-head attention mechanism for microblog sentiment analysis, showcasing the potential of attention mechanisms. @xcite demonstrated the effectiveness of convolutional and recurrent neural network models for sentiment analysis tasks. @xcite comparative study on modeling approaches for bangla sentiment analysis yielded valuable insights. moreover, @xcite introduced sentnob, a valuable resource for scrutinizing sentiment in informal and noisy textual data. finally, @xcite integrated bidirectional lstm with two-dimensional max pooling, showcasing the potential of amalgamating techniques for sentiment analysis tasks. ## data and methodology within the section, we provide a comprehensive overview of the data sources utilized and the rigorous research methodologies employed, ensuring transparency and credibility in our approach. ## results and discussion in this section, we present the outcomes of our experiments and engage in a comprehensive analysis of the findings.