Authors : Rohit Ranvir; Gopal Rathod; Jay Gore; Abhijit Zade; Sachin Chavhan

Volume/Issue : Volume 10 - 2025, Issue 2 - February

Google Scholar : https://tinyurl.com/2ffhhta6

Scribd : https://tinyurl.com/yc7r6syd

DOI : https://doi.org/10.5281/zenodo.14959397

Abstract : This survey paper provides an in-depth exploration of sentiment analysis on social media through the lens of deep learning-based methods. It systematically examines various components essential to sentiment analysis, including data classification, pre-processing techniques, text representations, and learning models, as well as their applications as discussed in different research papers. The authors evaluate recent advancements in deep learning architectures, highlighting their advantages and disadvantages in the context of sentiment analysis. Additionally, the paper addresses the challenges faced in this field, such as the informal nature of social media language and the necessity for large and labelled datasets, while also identifying factors that can enhance the accuracy of sentiment classification. A significant focus of the article is on emotion recognition from text, which is recognized as a critical task within Natural Language Processing (NLP) that can greatly benefit artificial intelligence and improve human interaction. To illustrate the effectiveness of deep learning techniques, the authors propose a sentiment classification method utilizing Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks, demonstrating high accuracy in emotion classification across three different datasets. Overall, the paper offers valuable insights into the current state of sentiment analysis, emphasizing the potential of deep learning to advance this important area of research. Recurrent Neural Network (RNN). Gated Recurrent Unit (GRU) was reported as the best performer in terms of accuracy on benchmark datasets.

Keywords : Sentiment Analysis, Social Media, Deep Learning, Deep Learning, Pre- Processing.

References :

1. Lei Zhang, Lei Zhang Shuai, Wang, Bing Liu, “Deep Learning for Sentiment Analysis, ”Jurnal Administrasi Publik: arXiv preprint arXiv CS , vol . N/A , pp. N/A, January 2018.
2. Shilpa C p.c, Susmi Jacob, P. Vinod Cochin, “Sentiment Analysis Using Deep Learning,”Jurnal Administrasi Publik: IEEE, vol . N/A , pp. N/A, February 2021.
3. Aadil Gani Ganie, Samad Dadvandipour, “Traditional or deep learning for sentiment analysis ,”Jurnal Administrasi Publik: Multidiszciplináris Tudományok , vol .12 , pp. 3-12, January 2022.
4. Qurat Tul Ain, Mubashir Ali, Amna Riaz, Amna Noureen, Muhammad Kamran, Babar Hayat, A. Rehman, “Sentiment Analysis Using Deep Learning Techniques: A Revie,” Jurnal Administrasi Publik: International Journal of Advanced Computer Science and Applications (IJACSA), vol . 8 , pp . 6 , 2017.
5. Atandoh, Fengli Zhang, Mugahed A. Al-antari, Daniel Addo, Yeong Hyeon Gu, “Scalable deep learning framework for sentiment analysis prediction for online movie reviews, ” Jurnal Administrasi Publik: Elsevier, Vol . 10, Issue 10, 30 May 2024.
6. Gagandeep Kaur & Amit Sharma, “A deep learning-based model using hybrid feature extraction approach for consumer sentiment analysis, ” Jurnal Administrasi Publik: Springer, vol . 10 , Article number: 5 (2023).
7. Nikhil Sanjay Suryawanshi “Sentiment analysis with machine learning and deep    learning: A survey of techniques and applications” Jurnal Administrasi Publik: N/A, vol . N/A, pp . N/A June 2024.
8. Wael Etaiwi, Dima Suleiman, Arafat Awajan, “ Deep Learning Based Techniques for Sentiment Analysis: A Survey ” Jurnal Administrasi Publik: Informatica, vol . 45, pp.89-95 , 2021.