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2(1), (2023):8-15 DOI: https://doi.org/10.46632/jdaai/2/1/2
C. Kumaresan, P. Thangaraju.
Sentiment analysis, the process of automatically identifying and extracting subjective information from text, has gained increasing attention in recent years due to its potential applications in a variety of fields. However, the task of sentiment analysis can be challenging when applied to texts in multiple languages, as it requires not only language-specific preprocessing and feature extraction techniques, but also the development and adaptation of machine learning models that are able to handle the complexities of different languages. This research paper provides an overview of the current approaches and challenges in sentiment analysis for multiple languages. This study begins by discussing the general principles and techniques of sentiment analysis, including the use of deep learning and machine learning methods, as well as the importance of feature selection and ethical considerations. It examines the specific challenges and approaches for sentiment analysis in various languages, including Arabic, Chinese, Russian, and English. The use of multimodal sentiment analysis and the potential applications of sentiment analysis in various domains, such as healthcare, social media, and customer service. At the end, this review highlights the potential of sentiment analysis in multiple languages and the need for further research to improve the accuracy and reliability of sentiment analysis models for a variety of languages and domains. Future work should also address the ethical concerns involved in the collection and use of sentiment analysis data, as well as the challenges of adapting models to new languages and domains.
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C. Kumaresan, P. Thangaraju. “Sentiment Analysis in Multiple Languages: A Review of Current Approaches and Challenges.” REST Journal on Data Analytics and Artificial Intelligence 2(1), (2023):8-15.