Advancements in sentiment analysis – A comprehensive review of recent techniques and challenges
Downloads
Published
DOI:
https://doi.org/10.58414/SCIENTIFICTEMPER.2025.16.spl-1.09Keywords:
Sentiment analysis, Machine learning, Deep learning, Aspect analysis, Emotion Detection, Fine-grained Sentiment analysisDimensions Badge
Issue
Section
License
Copyright (c) 2025 The Scientific Temper

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
In an increasingly digital world, opinions and emotions expressed across a variety of online platforms, when analyzed,propose immense potential for businesses, governments, and organizations. Sentiment analysis includes a collection of techniques that provide a fast and efficient way to classify user comments and derive meaningful information. Though sentiment analysis has been in practice for quite some time, there is a significant advancement in terms of approaches used because of increasing amounts of available data in various forms, including text, requirement of contextual understanding, business needs, etc. This article provides a comprehensive review of the latest advancements in sentiment classification in terms of scope, techniques and challenges. This literature review presents a good insight into the classification of various approaches in sentiment analysis and comparative analysis of different techniques. It also highlights the challenges in terms of the research gap and proposes future directions.Abstract
How to Cite
Downloads
Similar Articles
- Abhishek Dwivedi, Nikhat Raza Khan, Reconfiguration of Automated Manufacturing Systems Using Gated Graph Neural Networks , The Scientific Temper: Vol. 13 No. 02 (2022): The Scientific Temper
- R. Thiagarajan, S. Prakash Kumar, Performance of public transport appraisal using machine learning , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- Amit Maru, Dhaval Vyas, Hybrid deep learning approach for pre-flood and post-flood classification of remote sensed data , The Scientific Temper: Vol. 16 No. Spl-1 (2025): The Scientific Temper
- Sharanya Unnikrishnan, Eldhose Thomas, Arunima Dey, AI-Powered NLP in Vernacular Public Relations: Opportunities, Challenges, and Ethical Implications for India’s Multilingual Landscape , The Scientific Temper: Vol. 16 No. 10 (2025): The Scientific Temper
- Radha K. Jana, Dharmpal Singh, Saikat Maity, Modified firefly algorithm and different approaches for sentiment analysis , The Scientific Temper: Vol. 15 No. 01 (2024): The Scientific Temper
- Josephine Theresa S, Graph Neural Network Ensemble with Particle Swarm Optimization for Privacy-Preserving Thermal Comfort Prediction , The Scientific Temper: Vol. 16 No. 12 (2025): The Scientific Temper
- Somalee Mahapatra, Manoranjan Dash, Subhashis Mohanty, Adoption of artificial intelligence and the internet of things in dental biomedical waste management , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Sharayu Mirasdar, Mangesh Bedekar, Knowledge graphs for NLP: A comprehensive analysis , The Scientific Temper: Vol. 16 No. Spl-1 (2025): The Scientific Temper
- Amala Deepa V., T. Lucia Agnes Beena, Enhancing data imputation in complex datasets using Lagrange polynomial interpolation and hot-deck fusion , The Scientific Temper: Vol. 16 No. 02 (2025): The Scientific Temper
- T. Kanimozhi, V. Rajeswari, R. Suguna, J. Nirmaladevi, P. Prema, B. Janani, R. Gomathi, RWHO: A hybrid of CNN architecture and optimization algorithm to predict basal cell carcinoma skin cancer in dermoscopic images , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
<< < 12 13 14 15 16 17 18 19 20 21 > >>
You may also start an advanced similarity search for this article.

