A neural network framework for enhancing sentiment analysis

Abstract

This research presents a thorough design and assessment of various machine learning models newlineapplied to sentiment analysis of Twitter data. The primary focus was to assess and compare the newlineeffectiveness of different classifiers, namely Logistic Regression, Linear SVM, Random Forest, newlineDecision Tree, XG Boost, and Naive Bayes, in categorizing tweets into positive and negative newlinesentiments. The evaluation criteria included metrics such as accuracy, precision, recall, F1-score, newlineand support, with a particular emphasis on both training and testing data accuracies. Key findings newlinerevealed that Logistic Regression and Linear SVM models exhibited high accuracy and balanced newlineperformance across different metrics, making them particularly suitable for sentiment analysis newlinetasks. In contrast, Random Forest and Decision Tree models, despite their high training newlineaccuracies, showed lower testing accuracies, indicating potential overfitting issues. XG Boost and newlineNaive Bayes models demonstrated moderate accuracy levels with consistent performance, newlineoffering a viable alternative for sentiment classification tasks. An essential aspect of the study newlineinvolved data analysis and visualization, focusing on the distribution of sentiments within the newlinetweets. This preliminary step informed the subsequent processes of feature extraction and newlineclassification. The use of word clouds for feature extraction and support vector machine-based newlineclassifiers for sentiment classification underscored the effectiveness of these methods in deriving newlineinsights from the data. The research utilized the sentiment140 dataset, consisting of 1.6 million newlinetweets, which were labeled for sentiment based on the presence of emoticons. This approach newlinehighlighted the innovative use of automated sentiment labeling in large datasets. The study also newlineunderscored the importance of balanced sentiment label distribution in training data for optimal newlinemodel performance. The findings lay a foundation for future research, emphasizing the adaptation newlineof these models to specific analytical conte