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2000
Volume 18, Issue 6
  • ISSN: 2666-2558
  • E-ISSN: 2666-2566

Abstract

Background

Data is rapidly expanding in today's digital age. The reason for the expansion of data is due to social media sites. The internet produces an enormous quantity of unstructured data every second. Numerous users have many opinions and reviews to impart on everything from items and services to common pastimes. Opinions, feelings, attitudes, impressions, ., concerning subjects, products, and services are collected and analyzed through a method called sentiment analysis. Web-based networking mediums that rely on textual communication can be overwhelming. Understanding human psychology requires the real-time processing of data using techniques like sentiment analysis.

Aims

This study provides a thorough examination of the differences between methods of sentiment analysis, as well as its obstacles and emerging trends. The paper exemplifies the analysis's practical uses, examines its challenges, and outlines common methods of conducting it.

Objective

The objective of the current overview is to better understand the market, gauge public opinion, and make strategic decisions. In addition, enterprises, governments, and scholars can all benefit from conducting a sentiment analysis.

Methods

In this study, we review and categorize the most widely applied methods of deep learning and machine learning for analyzing sentiment. From the paper, we learn that which sentiment analysis technique is the best depends on the data at hand. When confronted with large amounts of data and a lengthy procedure, traditional machine learning-based algorithms flop. The ability to train deep learning models to learn more features using larger datasets is why they currently beat machine learning methodologies. Considerations include textual and temporal context, as well as data volume.

Results

Regardless of the fact that the English language has traditionally been the focus of research in this field, other spoken languages have recently attracted a growing amount of interest. The lack of resources for these languages continues to present numerous obstacles. Consequently, it can be an intriguing line of future effort to tackle other natural languages outside English by generating beneficial resources like building databases and addressing the problems with Natural language processing that have been stated in the context of sentiment examination.

Conclusion

The difficulties of sentiment analysis are examined as well, with the goal of illuminating potential solutions.

© 2025 Bentham Science Publishers
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/content/journals/rascs/10.2174/0126662558312258240911111028
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Leading-edge Sentiment Analysis: A Survey of Application Context, Challenges and Advanced Techniques
Recent Advances in Computer Science and Communications 18, E26662558312258 (2025); https://doi.org/10.2174/0126662558312258240911111028
/content/journals/rascs/10.2174/0126662558312258240911111028
/content/journals/rascs/10.2174/0126662558312258240911111028
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  • Article Type:     Review Article
Keyword(s): deep learning; Emotion AI; machine learning; natural language processing; sentiment analysis; social media

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