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
- V. Parimala, D. Ganeshkumar, Solar energy-driven water distillation with nanoparticle integration for enhanced efficiency, sustainability, and potable water production in arid regions , The Scientific Temper: Vol. 15 No. 01 (2024): The Scientific Temper
- Nida Syeda, Kishore Selva Babu, Exploring the role of digital humanities in the centralization of knowledge production: Clusters, networks, or echo chambers , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
- Rashmi Chandra, Afroz Alam, Phytochemical Analysis Using X-ray Diffraction Spectroscopy (XRD) and GC-MS Analysis of Bioactive Compounds in Cucumis sativus L. (Angiosperms; Cucurbitaceae) , The Scientific Temper: Vol. 13 No. 01 (2022): The Scientific Temper
- Selva Kumar D, Revisiting the challenges of disinvestment practices and central public sector enterprises (CPSEs): Indian empirical evidence , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
- Pritee Rajaram Ray, Bijal Zaveri, Inclusive education for children with learning difficulties in Mauritius: An analytical study among select stakeholders , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Karan Berry, Shiv Kumar, Exploring the mediating role of gastronomic experience in tourist satisfaction: A multigroup analysis , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Sawitri Devi, Raj Kumar, Unveiling scholarly insights: A bibliometric analysis of literature on gender bias at the workplace , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- G. Chitra, Hari Ganesh S., Cultural algorithm based principal component analysis (CA-PCA) approach for handling high dimensional data , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- Abbasova Sona Jamal, Aliyev Sabit Shakir, Mahmudov Elmir Heydar, Museyibli Emin Bakir, Nadirkhanova Dilshat Adalat, Econometric analysis of grain yields (using the example of the Republic of Azerbaijan) , The Scientific Temper: Vol. 16 No. 02 (2025): The Scientific Temper
- D. Prabakar, Santhosh Kumar D.R., R.S. Kumar, Chitra M., Somasundaram K., S.D.P. Ragavendiran, Narayan K. Vyas, Task offloading and trajectory control techniques in unmanned aerial vehicles with Internet of Things – An exhaustive review , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
<< < 14 15 16 17 18 19 20 21 22 23 > >>
You may also start an advanced similarity search for this article.

