A Detailed Sentiment Analysis Survey Based on Machine Learning Techniques
Abstract Sentiment analysis is a rapidly growing topic of research as a result of the tremendous growth of digital information. In the modern era of artificial intelligence, one of the most crucial technologies for obtaining sentiment data from the vast amounts of data is sentiment analysis. It refers to a procedure of finding and categorising the opinions expressed in a source text. Reaching a consensus regarding business decisions is made much easier by conducting a sentiment analysis on consumer data. Machine learning offers an efficient and trustworthy technique for sentiment categorization and opinion mining. State-of-art machine learning techniques and methodologies have evolved and expanded. In addition to summarising research articles based on movie reviews, product reviews, and Twitter reviews, this survey article covers sentiment analysis notations, needs, levels, methodologies, sources, and machine learning approaches and tools. This research aims to determine the significance of sentiment analysis and to generate interest in the subject.
- Referencias
- Cómo citar
- Del mismo autor
- Métricas
Abirami, A. M., & Gayathri, V., 2017. A survey on sentiment analysis methods and approach. 2016 Eighth International Conference on Advanced Computing (ICoAC), 72–76. 10.1109/ICoAC.2017.7951748
Alzahrani, M. E., Aldhyani, T. H. H., Alsubari, S. N., Althobaiti, M. M., & Fahad, A., 2022. Developing an intelligent system with deep learning algorithms for sentiment analysis of e-commerce product reviews. Computational Intelligence and Neuroscience, 2022. 10.1109/ICoAC.2017.7951748
Astya, P. et al., 2017. Sentiment analysis: approaches and open issues. 2017 International Conference on Computing, Communication and Automation (ICCCA), 154–158.
Aydo, E., & Akcayol, M. A., 2016. Aydogan2016.Pdf.
Bandana, R., 2018,. Sentiment analysis of movie reviews using heterogeneous features. 2018 2nd International Conference on Electronics, Materials Engineering \& Nano-Technology (IEMENTech), 1–4. 10.1109/IEMENTECH.2018.8465346
Basarslan, M. S., & Kayaalp, F., 2020. Sentiment Analysis with Machine Learning Methods on Social Media. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 9(3), 5–15. 10.14201/adcaij202093515
Batanović, V., Nikolić, B., & Milosavljević, M., 2016. Reliable baselines for sentiment analysis in resource-limited languages: The serbian movie review dataset. Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16), 2688–2696.
Bengesi, S., Oladunni, T., Olusegun, R., & Audu, H., 2023. A Machine Learning-Sentiment Analysis on Monkeypox Outbreak: An Extensive Dataset to Show the Polarity of Public Opinion From Twitter Tweets. IEEE Access, 11, 11811–11826. 10.1109/ACCESS.2023.3242290
Brar, G. S., & Sharma, A., 2018. Sentiment analysis of movie review using supervised machine learning techniques. International Journal of Applied Engineering Research, 13(16), 12788-12791.
Chen, X., Xue, Y., Zhao, H., Lu, X., Hu, X., & Ma, Z., 2019. A novel feature extraction methodology for sentiment analysis of product reviews. Neural Computing and Applications, 31, 6625–6642. 10.1007/s00521-018-3477-2
Chouhan, K., Yadav, M., Rout, R. K., Sahoo, K. S., Jhanjhi, N., Masud, M., & Aljahdali, S., 2023. Sentiment analysis with tweets behaviour in twitter streaming api. Comput Syst Sci Eng, 45(2), 1113–1128. 10.32604/csse.2023.030842
Dey, S., Wasif, S., Tonmoy, D. S., Sultana, S., Sarkar, J., & Dey, M., 2020. A comparative study of support vector machine and Naive Bayes classifier for sentiment analysis on Amazon product reviews. 2020 International Conference on Contemporary Computing and Applications (IC3A), 217–220. 10.1109/IC3A48958.2020.233300
Dhande, L. L., & Patnaik, G. K., 2014. Analyzing sentiment of movie review data using Naive Bayes neural classifier. International Journal of Emerging Trends \& Technology in Computer Science (IJETTCS), 3(4), 313–320.
El Rahman, S. A., AlOtaibi, F. A., & AlShehri, W. A., 2019. Sentiment analysis of twitter data. 2019 International Conference on Computer and Information Sciences (ICCIS), 1–4. 10.1109/ICCISci.2019.8716464
Enduri, M. K., Sangi, A. R., Anamalamudi, S., Manikanta, R. C. B., Reddy, K. Y., Yeswanth, P. L., Reddy, S. K. S., & Karthikeya, A., 2023. Comparative study on sentimental analysis using machine learning techniques. Mehran University Research Journal Of Engineering \& Technology, 42(1), 207–215. 10.22581/muet1982.2301.19
Goularas, D., & Kamis, S., 2019. Evaluation of deep learning techniques in sentiment analysis from twitter data. 2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML), 12–17. 10.1109/Deep-ML.2019.00011
Jagdale, R. S., Shirsat, V. S., & Deshmukh, S. N., 2019. Sentiment analysis on product reviews using machine learning techniques. Advances in Intelligent Systems and Computing, 768, 639–647. 10.1007/978-981-13-0617-4_61
Jain, A. P., & Dandannavar, P., 2016. Application of machine learning techniques to sentiment analysis. 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology (ICATccT), 628–632. 10.1109/ICATCCT.2016.7912076
Kanna, P. R., & Pandiaraja, P., 2019. An efficient sentiment analysis approach for product review using turney algorithm. Procedia Computer Science, 165, 356–362. 10.1016/j.procs.2020.01.038
Kaur, H., Mangat, V., et al., 2017. A survey of sentiment analysis techniques. 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC), 921–925. 10.1109/I-SMAC.2017.8058315
Kumar, A., & Garg, G., 2019. Sentiment analysis of multimodal twitter data. Multimedia Tools and Applications, 78, 24103–24119. 10.1007/s11042-019-7390-1
Kumar, A., Sebastian, T. M., et al., 2012. Sentiment analysis: A perspective on its past, present and future. International Journal of Intelligent Systems and Applications, 4(10), 1–14. 10.5815/ijisa.2012.10.01
Mai, L., & Le, B., 2021. Joint sentence and aspect-level sentiment analysis of product comments. Annals of Operations Research, 300, 493–513. 10.1007/s10479-020-03534-7
Mishra, P., Rajnish, R., & Kumar, P., 2016. Sentiment analysis of Twitter data: Case study on digital India. 2016 International Conference on Information Technology (InCITe)-The Next Generation IT Summit on the Theme-Internet of Things: Connect Your Worlds, 148–153. 10.1109/INCITE.2016.7857607
Mitra, A., 2020. Sentiment Analysis Using Machine Learning Approaches (Lexicon based on movie review dataset). Journal of Ubiquitous Computing and Communication Technologies, 2(3), 145–152. 10.36548/jucct.2020.3.004
Moralwar, S. B., & Deshmukh, S. N., 2015. Different approaches of sentiment analysis. Int. J. Comput. Sci. Eng, 3(3), 160–165.
Mumtaz, D., & Ahuja, B., 2016. Sentiment analysis of movie review data using Senti-lexicon algorithm. 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology (ICATccT), 592–597. 10.1109/ICATCCT.2016.7912069
Neethu, M. S., & Rajasree, R., 2013. Sentiment analysis in twitter using machine learning techniques. 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT), 1–5. 10.1109/ICCCNT.2013.6726818
Parkhe, V., & Biswas, B., 2016. Sentiment analysis of movie reviews: finding most important movie aspects using driving factors. Soft Computing, 20(9), 3373–3379. 10.1007/s00500-015-1779-1
Pujari, C., Aiswarya, & Shetty, N. P., 2018. Comparison of classification techniques for feature oriented sentiment analysis of product review data. Data Engineering and Intelligent Computing: Proceedings of IC3T 2016, 149–158. 10.1007/978-981-10-3223-3_14
Raghuvanshi, N., & Patil, J. M., 2016. A brief review on sentiment analysis. 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), 2827–2831. 10.1109/ICEEOT.2016.7755213
Rahman, A., & Hossen, M. S., 2019. Sentiment analysis on movie review data using machine learning approach. 2019 International Conference on Bangla Speech and Language Processing (ICBSLP), 1–4. 10.1109/ICBSLP47725.2019.201470
Ray, P., & Chakrabarti, A., 2017. Twitter sentiment analysis for product review using lexicon method. 2017 International Conference on Data Management, Analytics and Innovation (ICDMAI), 211–216. 10.1109/ICDMAI.2017.8073512
Ruz, G. A., Henriquez, P. A., & Mascareño, A., 2020. Sentiment analysis of Twitter data during critical events through Bayesian networks classifiers. Future Generation Computer Systems, 106, 92–104. 10.1016/j.future.2020.01.005
Saini, S., Punhani, R., Bathla, R., & Shukla, V. K., 2019. Sentiment analysis on twitter data using R. 2019 International Conference on Automation, Computational and Technology Management (ICACTM), 68–72. 10.1109/ICACTM.2019.8776685
Sharma, P., & Moh, T.-S., 2016. Prediction of Indian election using sentiment analysis on Hindi Twitter. 2016 IEEE International Conference on Big Data (Big Data), 1966–1971. 10.1109/BigData.2016.7840818
Shivaprasad, T. K., & Shetty, J., 2017. Sentiment analysis of product reviews: a review. 2017 International Conference on Inventive Communication and Computational Technologies (ICICCT), 298–301. 10.1109/ICICCT.2017.7975207
Smetanin, S., & Komarov, M., 2019. Sentiment analysis of product reviews in Russian using convolutional neural networks. 2019. IEEE 21st Conference on Business Informatics (CBI), 1, 482–486. 10.1109/CBI.2019.00062
Tripathy, A., 2017. Sentiment analysis using machine learning techniques. PhD thesis.
Alzahrani, M. E., Aldhyani, T. H. H., Alsubari, S. N., Althobaiti, M. M., & Fahad, A., 2022. Developing an intelligent system with deep learning algorithms for sentiment analysis of e-commerce product reviews. Computational Intelligence and Neuroscience, 2022. 10.1109/ICoAC.2017.7951748
Astya, P. et al., 2017. Sentiment analysis: approaches and open issues. 2017 International Conference on Computing, Communication and Automation (ICCCA), 154–158.
Aydo, E., & Akcayol, M. A., 2016. Aydogan2016.Pdf.
Bandana, R., 2018,. Sentiment analysis of movie reviews using heterogeneous features. 2018 2nd International Conference on Electronics, Materials Engineering \& Nano-Technology (IEMENTech), 1–4. 10.1109/IEMENTECH.2018.8465346
Basarslan, M. S., & Kayaalp, F., 2020. Sentiment Analysis with Machine Learning Methods on Social Media. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 9(3), 5–15. 10.14201/adcaij202093515
Batanović, V., Nikolić, B., & Milosavljević, M., 2016. Reliable baselines for sentiment analysis in resource-limited languages: The serbian movie review dataset. Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16), 2688–2696.
Bengesi, S., Oladunni, T., Olusegun, R., & Audu, H., 2023. A Machine Learning-Sentiment Analysis on Monkeypox Outbreak: An Extensive Dataset to Show the Polarity of Public Opinion From Twitter Tweets. IEEE Access, 11, 11811–11826. 10.1109/ACCESS.2023.3242290
Brar, G. S., & Sharma, A., 2018. Sentiment analysis of movie review using supervised machine learning techniques. International Journal of Applied Engineering Research, 13(16), 12788-12791.
Chen, X., Xue, Y., Zhao, H., Lu, X., Hu, X., & Ma, Z., 2019. A novel feature extraction methodology for sentiment analysis of product reviews. Neural Computing and Applications, 31, 6625–6642. 10.1007/s00521-018-3477-2
Chouhan, K., Yadav, M., Rout, R. K., Sahoo, K. S., Jhanjhi, N., Masud, M., & Aljahdali, S., 2023. Sentiment analysis with tweets behaviour in twitter streaming api. Comput Syst Sci Eng, 45(2), 1113–1128. 10.32604/csse.2023.030842
Dey, S., Wasif, S., Tonmoy, D. S., Sultana, S., Sarkar, J., & Dey, M., 2020. A comparative study of support vector machine and Naive Bayes classifier for sentiment analysis on Amazon product reviews. 2020 International Conference on Contemporary Computing and Applications (IC3A), 217–220. 10.1109/IC3A48958.2020.233300
Dhande, L. L., & Patnaik, G. K., 2014. Analyzing sentiment of movie review data using Naive Bayes neural classifier. International Journal of Emerging Trends \& Technology in Computer Science (IJETTCS), 3(4), 313–320.
El Rahman, S. A., AlOtaibi, F. A., & AlShehri, W. A., 2019. Sentiment analysis of twitter data. 2019 International Conference on Computer and Information Sciences (ICCIS), 1–4. 10.1109/ICCISci.2019.8716464
Enduri, M. K., Sangi, A. R., Anamalamudi, S., Manikanta, R. C. B., Reddy, K. Y., Yeswanth, P. L., Reddy, S. K. S., & Karthikeya, A., 2023. Comparative study on sentimental analysis using machine learning techniques. Mehran University Research Journal Of Engineering \& Technology, 42(1), 207–215. 10.22581/muet1982.2301.19
Goularas, D., & Kamis, S., 2019. Evaluation of deep learning techniques in sentiment analysis from twitter data. 2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML), 12–17. 10.1109/Deep-ML.2019.00011
Jagdale, R. S., Shirsat, V. S., & Deshmukh, S. N., 2019. Sentiment analysis on product reviews using machine learning techniques. Advances in Intelligent Systems and Computing, 768, 639–647. 10.1007/978-981-13-0617-4_61
Jain, A. P., & Dandannavar, P., 2016. Application of machine learning techniques to sentiment analysis. 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology (ICATccT), 628–632. 10.1109/ICATCCT.2016.7912076
Kanna, P. R., & Pandiaraja, P., 2019. An efficient sentiment analysis approach for product review using turney algorithm. Procedia Computer Science, 165, 356–362. 10.1016/j.procs.2020.01.038
Kaur, H., Mangat, V., et al., 2017. A survey of sentiment analysis techniques. 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC), 921–925. 10.1109/I-SMAC.2017.8058315
Kumar, A., & Garg, G., 2019. Sentiment analysis of multimodal twitter data. Multimedia Tools and Applications, 78, 24103–24119. 10.1007/s11042-019-7390-1
Kumar, A., Sebastian, T. M., et al., 2012. Sentiment analysis: A perspective on its past, present and future. International Journal of Intelligent Systems and Applications, 4(10), 1–14. 10.5815/ijisa.2012.10.01
Mai, L., & Le, B., 2021. Joint sentence and aspect-level sentiment analysis of product comments. Annals of Operations Research, 300, 493–513. 10.1007/s10479-020-03534-7
Mishra, P., Rajnish, R., & Kumar, P., 2016. Sentiment analysis of Twitter data: Case study on digital India. 2016 International Conference on Information Technology (InCITe)-The Next Generation IT Summit on the Theme-Internet of Things: Connect Your Worlds, 148–153. 10.1109/INCITE.2016.7857607
Mitra, A., 2020. Sentiment Analysis Using Machine Learning Approaches (Lexicon based on movie review dataset). Journal of Ubiquitous Computing and Communication Technologies, 2(3), 145–152. 10.36548/jucct.2020.3.004
Moralwar, S. B., & Deshmukh, S. N., 2015. Different approaches of sentiment analysis. Int. J. Comput. Sci. Eng, 3(3), 160–165.
Mumtaz, D., & Ahuja, B., 2016. Sentiment analysis of movie review data using Senti-lexicon algorithm. 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology (ICATccT), 592–597. 10.1109/ICATCCT.2016.7912069
Neethu, M. S., & Rajasree, R., 2013. Sentiment analysis in twitter using machine learning techniques. 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT), 1–5. 10.1109/ICCCNT.2013.6726818
Parkhe, V., & Biswas, B., 2016. Sentiment analysis of movie reviews: finding most important movie aspects using driving factors. Soft Computing, 20(9), 3373–3379. 10.1007/s00500-015-1779-1
Pujari, C., Aiswarya, & Shetty, N. P., 2018. Comparison of classification techniques for feature oriented sentiment analysis of product review data. Data Engineering and Intelligent Computing: Proceedings of IC3T 2016, 149–158. 10.1007/978-981-10-3223-3_14
Raghuvanshi, N., & Patil, J. M., 2016. A brief review on sentiment analysis. 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), 2827–2831. 10.1109/ICEEOT.2016.7755213
Rahman, A., & Hossen, M. S., 2019. Sentiment analysis on movie review data using machine learning approach. 2019 International Conference on Bangla Speech and Language Processing (ICBSLP), 1–4. 10.1109/ICBSLP47725.2019.201470
Ray, P., & Chakrabarti, A., 2017. Twitter sentiment analysis for product review using lexicon method. 2017 International Conference on Data Management, Analytics and Innovation (ICDMAI), 211–216. 10.1109/ICDMAI.2017.8073512
Ruz, G. A., Henriquez, P. A., & Mascareño, A., 2020. Sentiment analysis of Twitter data during critical events through Bayesian networks classifiers. Future Generation Computer Systems, 106, 92–104. 10.1016/j.future.2020.01.005
Saini, S., Punhani, R., Bathla, R., & Shukla, V. K., 2019. Sentiment analysis on twitter data using R. 2019 International Conference on Automation, Computational and Technology Management (ICACTM), 68–72. 10.1109/ICACTM.2019.8776685
Sharma, P., & Moh, T.-S., 2016. Prediction of Indian election using sentiment analysis on Hindi Twitter. 2016 IEEE International Conference on Big Data (Big Data), 1966–1971. 10.1109/BigData.2016.7840818
Shivaprasad, T. K., & Shetty, J., 2017. Sentiment analysis of product reviews: a review. 2017 International Conference on Inventive Communication and Computational Technologies (ICICCT), 298–301. 10.1109/ICICCT.2017.7975207
Smetanin, S., & Komarov, M., 2019. Sentiment analysis of product reviews in Russian using convolutional neural networks. 2019. IEEE 21st Conference on Business Informatics (CBI), 1, 482–486. 10.1109/CBI.2019.00062
Tripathy, A., 2017. Sentiment analysis using machine learning techniques. PhD thesis.
Singh, N., & Jaiswal, U. C. (2023). A Detailed Sentiment Analysis Survey Based on Machine Learning Techniques. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 12(1), e29105. https://doi.org/10.14201/adcaij.29105
Most read articles by the same author(s)
- Neha Singh, Umesh Chandra Jaiswal, Sentiment Analysis Using Machine Learning , ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal: Vol. 12 (2023)
- Neha Singh, Umesh Chandra Jaiswal, Sarcasm Text Detection on News Headlines Using Novel Hybrid Machine Learning Techniques , ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal: Vol. 13 (2024)
- Anil Kumar, Umesh Chandra Jaiswal, Comparative Analysis of Sentiments in Children with Neurodevelopmental Disorders , ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal: Vol. 12 (2023)
Downloads
Download data is not yet available.
+
−