Affiliations: Department of Computer Science & Engineering, Sanjeev Agrawal Global Educational University, Bhopal, Madhya Pradesh, India
Correspondence:
[*]
Corresponding author: Shruti Mathur, Research Scholar, Department of Computer Science & Engineering, Sanjeev Agrawal Global Educational University, Bhopal, Madhya Pradesh 462022, India. E-mail: shrutimathur19@gmail.com.
Abstract: Sentiment analysis, which involves determining the emotional polarity positivity, negativity, or neutrality in the source texts, is a crucial task. Multilingual sentiment analysis techniques were developed to analyze data in several languages; a notable deficiency of resources in multilingual sentiment analysis is one of the primary issues. Furthermore, the developed methods for multilingual sentiment analysis have some limitations such as data dependency, reliability, robustness, and computational complexity. To tackle these shortcomings, this research proposed a multilingual improved multi-attention Deep Learning model (M2PSC-DL), which leverages the advantages of the Bi-directional Long Short Term Memory (BiLSTM) classifier with improved attention mechanisms. The Multi-metric graph embedding technique encodes the data to provide more contextual information representation. Additionally, the combination of improved Positional Spatial Channel (SPC) attention increases the capability of the model to extract relevant features in the training process which leads to getting accurate results in sentiment analysis tasks. Furthermore, the research proposed an improved sigmoid activation for solving the vanishing gradient issues that help the model avoid gradient saturations. The validation results demonstrate that the M2PSC-DL model attains 96.26% accuracy, 96.06% precision, and 96.18% recall for the XED dataset which is far better than the traditional methods.
Keywords: Sentiment analysis, multilingual improved multi-attention based Deep Learning model, TF-IDF based dependency features, Multi metric graph embedding, Bi-directional Long Short Term Memory
