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
- P. S. Dheepika, V. Umadevi, An optimized approach for detection and mitigation of DDoS attack cloud using an ensembled deep learning approach , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Suprabha Amit Kshatriya, Jaymin K Bhalani, Fire and smoke detection with high accuracy using YOLOv5 , The Scientific Temper: Vol. 16 No. 06 (2025): The Scientific Temper
- N. Saranya, M. Kalpana Devi, A. Mythili, Summia P. H, Data science and machine learning methods for detecting credit card fraud , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- Balaji V, Purnendu Bikash Acharjee, Muniyandy Elangovan, Gauri Kalnoor, Ravi Rastogi, Vishnu Patidar, Developing a semantic framework for categorizing IoT agriculture sensor data: A machine learning and web semantics approach , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- Jhankar Moolchandani, Kulvinder Singh, English language analysis using pattern recognition and machine learning , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- Deepa S, Sripriya T, Radhika M, Jeneetha J. J, Experimental evaluation of artificial intelligence assisted heart disease prediction using deep learning principle , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- D. Padma Prabha, C. Victoria Priscilla, A combined framework based on LSTM autoencoder and XGBoost with adaptive threshold classification for credit card fraud detection , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- Temesgen A. Asfaw, Batch size impact on enset leaf disease detection , The Scientific Temper: Vol. 15 No. 01 (2024): The Scientific Temper
- Lakshminarayani A, A Shaik Abdul Khadir, A blockchain-integrated smart healthcare framework utilizing dynamic hunting leadership algorithm with deep learning-based disease detection and classification model , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
- Adedotun Adedayo F, Odusanya Oluwaseun A, Adesina Olumide S, Adeyiga J. A, Okagbue, Hilary I, Oyewole O, Prediction of automobile insurance fraud claims using machine learning , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
<< < 1 2 3 4 5 6 7 8 9 10 > >>
You may also start an advanced similarity search for this article.

