Abstract:
In recent times social media platforms serve as the main source for communication, especially on public relations or on any economical crisis. During such situations, man...Show MoreMetadata
Abstract:
In recent times social media platforms serve as the main source for communication, especially on public relations or on any economical crisis. During such situations, many organizations depend on tweet conversations on X platform (earlier Twitter), to know the public sentiment and reactions, and provide responsive strategies. This research mainly focuses on using machine learning and deep learning techniques for analyzing the tweet conversations on PR. The novelty in this research is to use the power of natural language processing (NLP) techniques, to analyze every word and its occurrence to know the semantic meaning and understand the sentiment of that conversation. The proposed methodology begins with preprocessing the conversation data, building deep learning models namely LSTM, BiLSTM and machine learning models like logistic regression, Naive Bayes, SVM and XGBoost, which is followed by evaluation through certain metrics. This study helps in providing automated tools for improving the organizations to know the public sentiment during crisis and respond as fast as possible with effective strategies to address the public needs.
Published in: 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT)
Date of Conference: 24-28 June 2024
Date Added to IEEE Xplore: 04 November 2024
ISBN Information: