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
- 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
- Kinjal K. Patel, Kiran Amin, Predictive modeling of dropout in MOOCs using machine learning techniques , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- Roshni Kanth, R Guru, Anusuya M A, Madhu B K, A comprehensive study of AI in test case generation: Analysing industry trends and developing a predictive model , The Scientific Temper: Vol. 16 No. Spl-1 (2025): The Scientific Temper
- A. Sathya, M. S. Mythili, MOHCOA: Multi-objective hermit crab optimization algorithm for feature selection in sentiment analysis of Covid-19 Twitter datasets , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Ganga Gudi, Mallamma V Reddy, Hanumanthappa M, Enhancing Kannada text-to-speech and braille conversion with deep learning for the visually impaired , The Scientific Temper: Vol. 16 No. Spl-1 (2025): The Scientific Temper
- Muhammed Jouhar K. K., Dr. K. Aravinthan, An improved social media behavioral analysis using deep learning techniques , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- R. Gomathi, Balaji V, Sanjay R. Pawar, Ayesha Siddiqua, M. Dhanalakshmi, Ravi Rastogi, Ensuring ethical integrity and bias reduction in machine learning models , The Scientific Temper: Vol. 15 No. 01 (2024): The Scientific Temper
- Nisha Patil, Archana Bhise, Rajesh K. Tiwari, Fusion deep learning with pre-post harvest quality management of grapes within the realm of supply chain management , The Scientific Temper: Vol. 15 No. 01 (2024): The Scientific Temper
- S. Udhaya Priya, M. Parveen, ETPPDMRL: A novel approach for prescriptive analytics of customer reviews via enhanced text parsing and reinforcement learning , The Scientific Temper: Vol. 16 No. 05 (2025): The Scientific Temper
- Engida Admassu, Classifying enset based on their disease tolerance using deep 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.

