Decoding Sentiments: Virtue or Vice through Multilingual Paragraph Analysis

Abstract

This work provides a comprehensive overview of recent developments in sentiment analysis methodologies. It explores innovative approaches, including the integration of rule-based sentiment dictionaries, machine learning techniques, and deep learning solutions for financial sentiment analysis. Emphasis is placed on key preprocessing steps such as tokenization, lowercasing, stop words removal, and punctuation elimination. Feature extraction techniques like Bag-of-Words, Word2Vec, and TF-IDF are discussed, highlighting their roles in representing textual information. The abstract delves into model selection, covering traditional machine learning models like Naive Bayes, Support Vector Machines, and Random Forests, as well as deep learning models such as Recurrent Neural Networks, Long Short-Term Memory networks, and BERT. The abstract explains these algorithms in detail, emphasizing their application in sentiment analysis. Training the model through supervised learning and evaluating its performance using metrics like accuracy, precision, recall, and F1 score are outlined. Additionally, a structured approach to paraphrasing is introduced, underlining its significance in creating meaningful representations of text.