A new plant intelligence-based method for sentiment analysis: Chaotic sunflower optimization

Abstract

Various social networking applications provide people with many opportunities such as expressing, commenting, disseminating and transmitting their opinions within certain limits. The emotions and ideas that people express in their messages make sense of thousands of articles and opinions published instantly. Trying to make sense of emotional data, generating meaningful information from these data, analyzing these data, and making predictions and inferences on these data is a new important study field. In this study, sentiment analysis is considered an optimization problem in order to achieve high performance. For this purpose, sunflower optimization, which is one of the new and successful plant intelligence-based algorithms, has been modelled as a sentiment analyzer for the first time. A chaotic sunflower optimization algorithm was used by combining sunflower optimization and chaos theory in order to make effective sentiment analysis. In order for the proposed method to effectively solve the sentiment analysis problem, a suitable representation form and fitness function have been proposed. The proposed method treats the data as a search space and searches for a solution for analysis by detecting emotion in this search space. An up-to-date data set including customer feedback and satisfaction information was used in the study. Results based on accuracy, precision, and recall metrics show that plant intelligence-based metaheuristic algorithms can provide high performance.

Keywords

• Sentiment analysis
• plant intelligence
• chaotic sunflower optimization
• chaos

Sentiment Analiz İçin Bitki Zekası Temelli Yaklaşım: Kaotik Ayçiçeği Algoritması

Abstract

Various social networking applications provide people with many opportunities such as expressing, commenting, disseminating and transmitting their opinions within certain limits. The emotions and ideas that people express in their messages make sense of thousands of articles and opinions published instantly. Trying to make sense of emotional data, generating meaningful information from these data, analyzing these data, and making predictions and inferences on these data is a new important study field. In this study, sentiment analysis is considered an optimization problem in order to achieve high performance. For this purpose, sunflower optimization, which is one of the new and successful plant intelligence-based algorithms, has been modelled as a sentiment analyzer for the first time. A chaotic sunflower optimization algorithm was used by combining sunflower optimization and chaos theory in order to make effective sentiment analysis. In order for the proposed method to effectively solve the sentiment analysis problem, a suitable representation form and fitness function have been proposed. The proposed method treats the data as a search space and searches for a solution for analysis by detecting emotion in this search space. An up-to-date data set including customer feedback and satisfaction information was used in the study. Results based on accuracy, precision, and recall metrics show that plant intelligence-based metaheuristic algorithms can provide high performance.

Keywords

• Sentiment analysis
• plant intelligence
• chaotic sunflower optimization
• chaos

References

1. Abdu S, Yousef A, Salem A (2021) Multimodal Video Sentiment Analysis Using Deep Learning Approaches, a Survey. Information Fusion 76:204-226.
2. Akyol S, Alatas B (2020) Sentiment classification within online social media using whale optimization algorithm and social impact theory based optimization. Physica A: Statistical Mechanics and its Applications 540:123094.
3. Alam MH, Ryu WJ, Lee S (2016) Joint multi-grain topic sentiment: modeling semantic aspects for online reviews. Information Sciences 339:206–223.
4. Al-Twairesh N, Al-Nagheimish H (2019) Surface and Deep Features Ensemble for Sentiment Analysis of Arabic Tweets. IEEE Access 7:84122-84131.
5. Aziz A, Starkey A (2020) Predicting Supervise Machine Learning Performances for Sentiment Analysis Using Contextual-Based Approaches. IEEE Access 8:17722-17733.
6. Basiri ME, Nemati S, Abdar M, Asadi S, Acharrya UR (2021) A novel fusion-based deep learning model for sentiment analysis of COVID-19 tweets. Knowledge-Based Systems 228:107242.
7. Birjali M, Kasri M, Beni-Hssane A (2021) A comprehensive survey on sentiment analysis: Approaches, challenges and trends. Knowledge-Based Systems 226:107134.
8. Carosia AE, Coelho GP, Silva AE (2021) Investment strategies applied to the Brazilian stock market: A methodology based on Sentiment Analysis with deep learning. Expert Systems with Applications 184:115470.

9. Fang Y, Tan H, Zhang J (2018) Multi-Strategy Sentiment Analysis of Consumer Reviews Based on Semantic Fuzziness. IEEE Access 6:20625-20631.
10. Gavilanes MF, Montenegro EC, Mendez SG, Castano FG, Martinez JJ (2021) Evaluation of online emoji description resources for sentiment analysis purposes. Expert Systems with Applications 184:115279.
11. Gomes GF, Cunha Jr SS, Ancelotti Jr AC (2019) A sunflower optimization (SFO) algorithm applied to damage identification on laminated composite plates. Engineering with Computers 35: 619-626.
12. Jindal K, Aron R (2021) A systematic study of sentiment analysis for social media data. Materials Today: Proceedings Article in Press. Kotsiantis SB (2013) Decision trees: a recent overview. Artif Intell Rev 39:261–283.
13. Liang H, Ganeshbabu U, Thorne T (2020) A Dynamic Bayesian Network Approach for Analysing Topic-Sentiment Evolution. IEEE Access 8:54164-54174.
14. Medhat W, Hassan A, Korashy H (2014) Sentiment analysis algorithms and applications:A survey. Ain Shams Engineering Journal 5(4):1093-1113.
15. Moayedi H, Bui D, Kalantar B, Foong LK (2019) Machine-Learning-Based Classification Approaches toward Recognizing Slope Stability Failure. Applied Sciences 9(21):4638.
16. Mukherjee P, Badr Y, Doppalapui S, Srinivasan SM, Sangwan RS, Sharma R (2021) Effect of Negation in Sentences on Sentiment Analysis and Polarity Detection. Procedia Computer Science 185:370-379.
17. Rish I (2001) An empirical study of the naive Bayes classifier, IJCAI 2001 workshop on empirical methods in artificial intelligence, IBM New York, pp.41-46.
18. Smetanin S (2020) The Applications of Sentiment Analysis for Russian Language Texts: Current Challenges and Future Perspectives. IEEE Access 8:110693-110719.
19. Yıldırım S, Yıldırım G, Alatas B (2021) Salınımlı Kaotik Ayçiçeği Optimizasyon Algoritması. Fırat Üniversitesi Mühendislik Bilimleri Dergisi Article in Press
20. Yue S, Li P, Hao P (2003) SVM classification: Its contents and challenges. Appl. Math. Chin. Univ. 18:332–342.