Automated Sentiment Analysis Using Pigeon Inspired Optimization Algorithm with Attention Bidirectional Gated Recurrent on Social Media
Automated Sentiment Analysis Using Pigeon Inspired Optimization Algorithm with Attention Bidirectional Gated Recurrent on Social Media |
||
![]() |
![]() |
|
© 2024 by IJETT Journal | ||
Volume-72 Issue-8 |
||
Year of Publication : 2024 | ||
Author : K.Manikandan, V.Ganesh |
||
DOI : 10.14445/22315381/IJETT-V72I8P119 |
How to Cite?
K.Manikandan, V.Ganesh,"Automated Sentiment Analysis Using Pigeon Inspired Optimization Algorithm with Attention Bidirectional Gated Recurrent on Social Media," International Journal of Engineering Trends and Technology, vol. 72, no. 8, pp. 186-196, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I8P119
Abstract
Sentiment Analysis (SA) is a model employed in Natural Language Processing (NLP), which describes the feeling or sentiment conveyed in textual content. It uses automated tools to identify psychological data like thoughts, attitudes, and mental states shown in writing and indirectly over social network platforms. SA model is regularly executed on text datasets to help understand user desires, responses, and businesses observing products. Therefore, the Deep Learning (DL) technique has been developed as a promising model that has been commonly used and reached significant results. As DL approaches mechanically eliminate features from the database, there exists a possibility that an intermediate symbol, which has been disregarded may serve as a related factor. This study introduces an automated SA using the Pigeon-Inspired Optimization Algorithm with Attention Bidirectional Gated Recurrent Unit (PIOA-ABGRU) technique for social networking. The presented PIOA-ABGRU technique mainly focused on detecting multiple classes of feelings that occur in social media. The PIOA-ABGRU approach undergoes various sub-processes to alter the input data into a beneficial layout. Also, the Word Embedding (WE) procedure is performed using the BERT approach. For sentiment detection, the PIOA-ABGRU technique applies the ABGRU model, which detects sentiments in distinct classes. Finally, the PIOA-based hyperparameter tuning procedure is executed to select ABGRU's hyperparameters. The simulation results of the PIOA-ABGRU model take place on a social media dataset. The experimental analysis specified that the PIOA-ABGRU model reaches effectual achievement over other models using distinct measures.
Keywords
Sentiment analysis, Word embedding, Social media, Natural Language Processing, Pigeon Inspired Optimization.
References
[1] Rezaul Haque et al., “Multi-Class Sentiment Classification on Bengali Social Media Comments Using Machine Learning,” International Journal of Cognitive Computing in Engineering, vol. 4, pp. 21-35, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Nirmal Varghese Babu, and E. Grace Mary Kanaga, “Sentiment Analysis in Social Media Data for Depression Detection Using Artificial Intelligence: A Review,” SN Computer Science, vol. 3, pp. 1-20, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[3] S.V. Praveen, and Vajratiya Vajrobol, “Understanding the Perceptions of Healthcare Researchers Regarding ChatGPT: A Study Based on Bidirectional Encoder Representation from Transformers (BERT) Sentiment Analysis and Topic Modeling,” Annals of Biomedical Engineering, vol. 51, pp. 1654-1656, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Miftahul Qorib et al., “Covid-19 Vaccine Hesitancy: Text Mining, Sentiment Analysis and Machine Learning on COVID-19 Vaccination Twitter Dataset,” Expert Systems with Applications, vol. 212, pp. 1-14, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Sayyida Tabinda Kokab, Sohail Asghar, and Shehneela Naz, “Transformer-Based Deep Learning Models for the Sentiment Analysis of Social Media Data,” Array, vol. 14, pp. 1-12, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Arwa Alqarni, and Atta Rahman, “Arabic Tweets-Based Sentiment Analysis to Investigate the Impact of COVID-19 in KSA: A Deep Learning Approach,” Big Data and Cognitive Computing, vol. 7, no. 1, pp. 1-29, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Ferdaous Benrouba, and Rachid Boudour, “Emotional Sentiment Analysis of Social Media Content for Mental Health Safety,” Social Network Analysis and Mining, vol. 13, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Amjad Iqbal et al., “Sentiment Analysis of Consumer Reviews Using Deep Learning,” Sustainability, vol. 14, no. 17, pp. 1-19, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Jose Ramon Saura, Daniel Palacios-Marqués, and Domingo Ribeiro-Soriano, “Privacy Concerns in Social Media UGC Communities: Understanding User Behavior Sentiments in Complex Networks,” Information Systems and e-Business Management, pp. 1-21, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Bador Al Sari et al., “Sentiment Analysis for Cruises in Saudi Arabia on Social Media Platforms Using Machine Learning Algorithms,” Journal of Big Data, vol. 9, pp. 1-28, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Hameedur Rahman et al., “Multi-Tier Sentiment Analysis of Social Media Text Using Supervised Machine Learning,” Computers, Materials & Continua, vol. 74, no. 3, pp. 5527-5543, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Xianyong Li et al., “A Novel Deep Learning-based Sentiment Analysis Method Enhanced with Emojis in Microblog Social Networks,” Enterprise Information Systems, vol. 17, no. 5, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Ahmed Alsayat, “Improving Sentiment Analysis for Social Media Applications Using an Ensemble Deep Learning Language Model,” Arabian Journal for Science and Engineering, vol. 47, pp. 2499-2511, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Harleen Kaur et al., “A Proposed Sentiment Analysis Deep Learning Algorithm for Analyzing COVID-19 Tweets,” Information Systems Frontiers, vol. 23, pp. 1417-1429, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Mohamed Arbane et al., “Social Media-Based COVID-19 Sentiment Classification Model Using Bi-LSTM,” Expert Systems with Applications, vol. 212, pp. 1-9, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Lal Khan et al., “Deep Sentiment Analysis Using CNN-LSTM Architecture of English and Roman Urdu Text Shared in Social Media,” Applied Sciences, vol. 12, no. 5, pp. 1-18, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Ali Shariq Imran et al., “Cross-Cultural Polarity and Emotion Detection Using Sentiment Analysis and Deep Learning on COVID-19 Related Tweets,” IEEE Access, vol. 8, pp. 181074-181090, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Sida Yuan, and Qiong Yang, “Network Rumor Detection Using Attention Mechanism and BiGRU Neural Network in Big Data Environment,” Journal of Circuits, Systems and Computers, vol. 33, no. 1, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Nikhat Parveen et al., “Twitter Sentiment Analysis Using Hybrid Gated Attention Recurrent Network,” Journal of Big Data, vol. 10, pp. 1-29, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Sasirekha Natarajan et al., “An Automated Learning Model for Twitter Sentiment Analysis Using Ranger AdaBelief Optimizer Based Bidirectional Long Short Term Memory,” Expert Systems, vol. 41, no. 9, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Jianfeng Deng, Lianglun Cheng, and Zhuowei Wang, “Self-Attention-Based BiGRU and Capsule Network for Named Entity Recognition,” Arxiv, pp. 1-9, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Yan Cheng et al., “Text Sentiment Orientation Analysis Based on Multi-Channel CNN and Bidirectional GRU with Attention Mechanism,” IEEE Access, vol. 8, pp. 134964-134975, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[23] M. Geetha et al., “A Sequential Hybrid Optimization Algorithm (SHOA) to Solve the Hybrid Flow Shop Scheduling Problems to Minimize Carbon Footprint,” Processes, vol. 12, no. 1, pp. 1-17, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Marios Michaelidis KazAnova, Sentiment140 Dataset with 1.6 Million Tweets, Kaggle, 2017. [Online]. Available: https://www.kaggle.com/datasets/kazanova/sentiment140
[25] Hanan T. Halawani et al., “Automated Sentiment Analysis in Social Media Using Harris Hawks Optimisation and Deep Learning Techniques,” Alexandria Engineering Journal, vol. 80, pp. 433-443, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Twitter US Airline Sentiment, Kaggle, 2019. [Online]. Available: https://www.kaggle.com/datasets/crowdflower/twitter-airline-sentiment