Text Sentiment Analysis Using the Bald Eagle-Based Bidirectional Long Short-Term Memory

• Published in: Advances in Computing and Data Sciences Vol. 1613; pp. 26 - 36
• Main Authors: Kumar, Garadasu Anil, Reddy, S. Sai Satyanarayana, Sripallavi, Punna, Parashuramulu, Bollam, Banu, B. Suresh
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2022; Springer International Publishing
• Series: Communications in Computer and Information Science
• Subjects: Bald eagle; BiLSTM; Sentiments; Text sentiment analysis; Tokenization
• ISBN: 3031126378; 9783031126376
• ISSN: 1865-0929; 1865-0937
• DOI: 10.1007/978-3-031-12638-3_3

Extraction of emotions that are conveyed through text is termed as sentimental analysis, which is also called as opinion mining. The sentimental analysis is enabled to extract the information about particular objects or an event or various subjects, which helps to analyze and detect the problems associated with them. The detection of this opinion helps in resolving the problems and it is embedded with the growth of the organization. In this research, a bald eagle based BiLSTM classifier is proposed to extricate the sentiment in the text. Initially, the data are collected and preprocessing is performed using the techniques tokenization, stopword removal, stemming and POS tagging. Features are extracted from the preprocessed data and classification is performed using the bald eagle optimization enabled BiLSTM classifier. The experiment shows that the proposed method works more efficient with a precision of 92.65%, and recall of 93.82%.