Sentiment Analysis Framework of Social Media Text by Feature Extraction and Machine Learning Model

Kajal Mathur; Paresh Jain; Sunita Gupta; Puneet Mathur

doi:10.17485/IJST/v16i29.1537

Article

Sentiment Analysis Framework of Social Media Text by Feature Extraction and Machine Learning Model

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v16i29.1537

Year: 2023, Volume: 16, Issue: 29, Pages: 2233-2243

Original Article

Sentiment Analysis Framework of Social Media Text by Feature Extraction and Machine Learning Model

Kajal Mathur^1*, Paresh Jain², Sunita Gupta³, Puneet Mathur⁴

¹Research Scholar, Department of Computer Science and Engineering, Suresh Gyan Vihar University, Jaipur, Rajasthan, India
²Associate Professor, Department of Electronics and Communication Engineering, Suresh Gyan Vihar University, Jaipur, Rajasthan, India
³Associate Professor, Department of information technology, Swami Keshvanand Institute of Technology, Jaipur, Rajasthan, India
⁴Assistant Engineer (IT), RVUN, Jaipur, Rajasthan, India

*Corresponding Author
Email: [email protected]

Received Date:21 June 2023, Accepted Date:26 June 2023, Published Date:03 August 2023

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objectives: This research paper aims to analyze sentiment and opinions in online resources like discussion forums, review sites, and blogs. It also compares the effectiveness of three feature extraction techniques (TF-IDF, Word2Vec, and WAM) and evaluates three machine learning algorithms (Naïve Bayes, SVM, and ANN) for sentiment classification to determine the most accurate algorithm. Methods: The study utilizes sentiment-rich datasets from IMDB movie reviews, Yelp reviews, and tweets. Three feature extraction techniques are applied to extract relevant features and patterns from the text. Three machine learning algorithms are implemented to classify sentiments into positive, negative, and neutral categories. Accuracy, precision, recall, and F-measure are used to assess algorithm performance. The model is updated and refined three times to ensure reliability. Findings: The Artificial Neural Network (ANN) algorithm outperforms Naïve Bayes and Support Vector Machines, achieving an impressive accuracy rate of 99.74% for sentiment classification. Precision, recall, and F-measure exceed 98.5% after model refinement, demonstrating the approach’s robustness. The study highlights the potential of sentiment analysis in online resources and emphasizes the ANN’s superior accuracy, providing valuable insights for future sentiment analysis studies. Novelty: This research combines three popular feature extraction techniques in sentiment analysis, compares three machine learning algorithms on multiple datasets, and achieves a remarkable accuracy rate of 99.74% with the ANN. The study demonstrates the robustness of the approach through model refinement and contributes insights into sentiment analysis in online resources.

Keywords: Dataset; Feature Extraction; Machine Learning; Sentiment Analysis; Accuracy and Precision

References

Muhammet SB, Fatih K. Sentiment Analysis on Social Media Reviews Datasets with Deep Learning Approach Article. Sakarya University Journal of Computer and Information Sciences·. 2021;4(1). Available from: https://doi.org/10.35377/saucis.04.01.833026
Bordoloi M, Biswas SK. Sentiment analysis: A survey on design framework, applications and future scopes. Artificial Intelligence Review. 2023. Available from: https://doi.org/10.1007/s10462-023-10442-2
Alantari HJ, Currim IS, Deng Y, Singh S. An empirical comparison of machine learning methods for text-based sentiment analysis of online consumer reviews. International Journal of Research in Marketing. 2022;39(1):1–19. Available from: https://doi.org/10.1016/j.ijresmar.2021.10.011
Bhuvaneshwari P, Rao AN, Robinson YH, Thippeswamy MN. Sentiment analysis for user reviews using Bi-LSTM self-attention based CNN model. Multimedia Tools and Applications. 2022;81(9):12405–12419. Available from: https://doi.org/10.1007/s11042-022-12410-4
Ping W, Li J, Jingrui H. S2SAN: A sentence-to-sentence attention network for sentiment analysis of online reviews. 2021. Available from: https://doi.org/10.1016/j.dss.2021.113603
Li L, Goh TTT, Jin D. How textual quality of online reviews affect classification performance: a case of deep learning sentiment analysis. Neural Computing and Applications. 2020;32(9):4387–4415. Available from: https://doi.org/10.1007/s00521-018-3865-7
Li W, Qi F, Tang M, Yu Z. Bidirectional LSTM with self-attention mechanism and multi-channel features for sentiment classification. Neurocomputing. 2020;387:63–77. Available from: https://doi.org/10.1016/j.neucom.2020.01.006
Zhiqiang G, Guofei C, Yongming H, Lu G, Li F. Semantic relation extraction using sequential and tree-structured LSTM with attention. Information Sciences. 2020;509:183–192. Available from: https://doi.org/10.1016/j.ins.2019.09.006
Jain R, Kumar A, Nayyar A, Dewan K, Garg R, Raman S, et al. Explaining sentiment analysis results on social media texts through visualization. Multimedia Tools and Applications. 2023;82(15):22613–22629. Available from: https://doi.org/10.1007/s11042-023-14432-y
Nandwani P, Verma R. A review on sentiment analysis and emotion detection from text. Social Network Analysis and Mining. 2021;11(1). Available from: https://doi.org/10.1007/s13278-021-00776-6
Rahman H, Tariq J, Masood MA, Subahi AF, Khalaf OI, Alotaibi Y. Multi-Tier Sentiment Analysis of Social Media Text Using Supervised Machine Learning. Computers, Materials & Continua. 2023;74(3):5527–5543. Available from: https://doi.org/10.32604/cmc.2023.033190
Budhi GS, Chiong R, Pranata I, Hu Z. Using Machine Learning to Predict the Sentiment of Online Reviews: A New Framework for Comparative Analysis. Archives of Computational Methods in Engineering. 2021;28(4):2543–2566. Available from: https://doi.org/10.1007/s11831-020-09464-8
Fan FLL, Xiong J, Li M, Wang G. On Interpretability of Artificial Neural Networks: A Survey. IEEE Transactions on Radiation and Plasma Medical Sciences. 2021;5(6):741–760. Available from: https://doi.org/10.1109/TRPMS.2021.3066428
Kaur G, Sharma A. A deep learning-based model using hybrid feature extraction approach for consumer sentiment analysis. Journal of Big Data. 2023;10(1). Available from: https://doi.org/10.1186/s40537-022-00680-6
Sayyida TK, Sohail A, Shehneela N. Transformer-based deep learning models for the sentiment analysis of social media data. Array. 2022;14:100157. Available from: https://doi.org/10.1016/j.array.2022.100157
Qianwen AX, Victor C, Chrisina J. A systematic review of social media-based sentiment analysis: Emerging trends and challenges. Decision Analytics Journal. 2022;3:100073. Available from: https://doi.org/10.1016/j.dajour.2022.100073
Dimple T, Bharti N, Bhoopesh SB, Ashutosh M, Manoj K. A systematic review of social network sentiment analysis with comparative study of ensemble-based techniques. Artif Intell Rev. 2023;12:1–55. Available from: https://doi.org/10.1007/s10462-023-10472-w
Muhammet SB, Fatih K. Sentiment analysis with machine learning methods on social media. 2020. Available from: https://doi.org/10.14201/ADCAIJ202093515
Wang H, Wang X. Sentiment analysis of tweets and government translations: Assessing China’s post-COVID-19 landscape for signs of withering or booming. Global Media and China. 2023;8(2):213–233. Available from: https://doi.org/10.1177/20594364231181745
Yin Z, Shao J, Hussain MJ, Hao Y, Chen Y, Zhang X, et al. DPG-LSTM: An Enhanced LSTM Framework for Sentiment Analysis in Social Media Text Based on Dependency Parsing and GCN. Applied Sciences. 2022;13(1):354. Available from: https://doi.org/10.3390/app13010354

Copyright

© 2023 Mathur et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)