Open Access
Sentiment Analysis of Social Network Comments for Identifying Opinion Leaders Using Machine Learning
4
Research Scholar Jabalpur, India
Abstract
The rate of social media growth is the greatest determinant in the spread of information. In order to understand and change opinion of the majority, it is necessary to find Key Opinion Leaders. In this research, a technique is provided for detecting opinion leaders in social media communications by analyzing their sentiment using Sentiment140. Following a long pre-processing sequence that includes text cleaning, tokenization, stemming, normalization, and Bag-of-Words feature extraction, the class imbalance problem is solved by using the SMOTE approach. The suggested method find geographical patterns and causal linkages in the text by using deep learning models like Bidirectional Long Short-Term Memory (BiLSTM) networks and Convolutional Neural Networks (CNNs). Compared to other machine learning models, such as Naive Bayes, Adaboost, and Logistic Regression, the suggested models performed better in the empirical data. The F1-score (F1), accuracy (ACC), and recall (REC) of these models are all above average, reaching at 98%. The results indicated that the deep learning algorithms can perform very well in the field of identifying opinion leaders based on sentiment analysis.
Keywords
Text Classification,
Social Network,
Machine Learning,
Opinion Leader
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