Open Access Subscription Access
Open Access Subscription Access
Sentiment Analysis using SVM Machine Learning Techniques on Social Media
Sentiment analysis is a branch of Natural Language Processing (NLP) and machine learning in which text is classified into two categories: positive and negative. Because the use of the internet and social media is growing at a rapid pace, the products created by these two are receiving far more client input than in the past. Text generated by social media, blogs, posts, and product reviews, among other places, has become the best-suited examples for consumer sentiment, delivering the best-suited notion for that particular product. As a result, the hybrid feature selection proposed in this paper is a combination of particle swarm optimization (PSO) and cuckoo search. The hybrid feature selection technique surpasses the standard technique due to the subjective nature of social media reviews. Support Vector Machine (SVM) classifier was used to examine performance criteria such as f-measure, recall, precision, and accuracy on the Twitter dataset, and it was compared to a convolution neural network. The proposed work outperforms the existing work, according to the findings of this paper's experiments based on various factors.
Data Mining, Machine Learning, Sentiment Analysis, Feature Optimization, Natural Language Processing
- G. Kesavaraj and S. Sukumaran, "A study on classification techniques in data mining," in 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT), Tiruchengode, pp. 1-7, 2013.
- M. S. Akhtar, D. Gupta, A. Ekbal, and P. Bhattacharyya, "Feature selection and ensemble construction: A two-step method for aspect based sentiment analysis," Knowl.-Based Syst., vol. 125, pp. 116-135, 2017.
- H. Saif, Y. He, and H. Alani, "Semantic Sentiment Analysis of Twitter," in The Semantic Web - ISWC 2012, pp. 508-524, 2012.
- S. Kiritchenko, X. Zhu, and S. M. Mohammad, "Sentiment Analysis of Short Informal Texts," J. Artif. Intell. Res., vol. 50, pp. 723-762, Aug. 2014.
- N. F. F. da Silva, E. R. Hruschka, and E. R. Hruschka, "Tweet sentiment analysis with classifier ensembles," Decis. Support Syst., vol. 66, pp. 170-179, Oct. 2014.
- M. Hagen, M. Potthast, M. Büchner, and B. Stein, "Twitter Sentiment Detection via Ensemble Classification Using Averaged Confidence Scores," in ECIR, 2015.
- Z. Jianqiang and C. Xueliang, "Combining Semantic and Prior Polarity for Boosting Twitter Sentiment Analysis," in 2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity), pp. 832-837, 2015.
- Z. Jianqiang, "Combing Semantic and Prior Polarity Features for Boosting Twitter Sentiment Analysis Using Ensemble Learning," in 2016 IEEE First International Conference on Data Science in Cyberspace (DSC), pp. 709-714, 2016.
- M. Thelwall, K. Buckley, and G. Paltoglou, "Sentiment Strength Detection for the Social Web 1," 2012.
- G. Paltoglou and M. Thelwall, "Twitter, MySpace, Digg: Unsupervised Sentiment Analysis in Social Media," ACM Trans. Intell. Syst. Technol., vol. 3, no. 4, pp. 1-19, Sep. 2012.
- A. Montejo?Ráez, E. Martínez?Cámara, M. T. Martín?Valdivia, and L. A. Ureña?López, "A knowledge-based approach for polarity classification in Twitter," J. Assoc. Inf. Sci. Technol., vol. 65, no. 2, pp. 414-425, 2014.
- S. Baccianella, A. Esuli, and F. Sebastiani, "SENTIWORDNET 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining," p. 5.
- Sharma, M. Sabharwal, V. Goyal, and M. Vij, "Sentiment Analysis Techniques for Social Media Data: A Review," has presented in First International Conference on Sustainable Technologies for Computational Intelligence, on 30th March 2019 given at Sri Balaji College of Engineering and Technology, Jaipur, Rajasthan, India Jaipur.
- A. Kumar, R. Khorwal, and S. Chaudhary, "A Survey on Sentiment Analysis using Swarm Intelligence," Indian J. Sci. Technol., vol. 9, no. 39, Oct. 2016.
- A. Agarwal, B. Xie, I. Vovsha, O. Rambow, and R. Passonneau, "Sentiment Analysis of Twitter Data," in Proceedings of the Workshop on Languages in Social Media, Stroudsburg, PA, USA, pp. 30-38, 2011.
- Z. Jianqiang, G. Xiaolin, and Z. Xuejun, "Deep Convolution Neural Networks for Twitter Sentiment Analysis," IEEE Access, vol. 6, pp. 23253-23260, 2018.
- R. Xia, J. Jiang, and H. He, "Distantly Supervised Lifelong Learning for Large-Scale Social Media Sentiment Analysis," IEEE Trans. Affect. Comput., vol. 8, no. 4, pp. 480-491, Oct. 2017.
- O. Udochukwu and Y. He, "A Rule-Based Approach to Implicit Emotion Detection in Text," in Natural Language Processing and Information Systems, vol. 9103, C. Biemann, S. Handschuh, A. Freitas, F. Meziane, and E. Métais, Eds. Cham: Springer International Publishing, pp. 197-203, 2015.
- G. Li and F. Liu, "Sentiment analysis based on clustering: a framework in improving accuracy and recognizing neutral opinions," Appl. Intell., vol. 40, no. 3, pp. 441-452, Apr. 2014.
- J. Luts, F. Ojeda, R. Van de Plas, B. De Moor, S. Van Huffel, and J. A. K. Suykens, "A tutorial on support vector machine-based methods for classification problems in chemometrics," Anal. Chim. Acta, vol. 665, no. 2, pp. 129-145, Apr. 2010.
- W. Zhao, Y. Wang, and D. Li, "A new feature selection algorithm in text categorization," in 2010 International Symposium on Computer, Communication, Control and Automation (3CA), vol. 1, pp. 146-149, 2010.
- Munish Sabharwal, "Contemporary Research: Intricacies and Aiding Software Tools Based on Expected Characteristics" in AIMA Journal of Management & Research, vol. 10, pp. 1-16, 2016.
- Presented Paper "The use of soft computing technique of Decision Tree in selection of appropriate statistical test for Hypothesis Testing" in the "International Conference on Soft Computing: Theories and Applications (SoCTA 2016)" organized by Amity University, Jaipur, India on December 28-30, 2016, Proceedings in AISC series of Springer Indexed in SCOPUS (Elsevier).
- H. M. Zin, N. Mustapha, M. A. A. Murad, and N. M. Sharef, "The effects of pre-processing strategies in sentiment analysis of online movie reviews," presented at the THE 2ND INTERNATIONAL CONFERENCE ON APPLIED SCIENCE AND TECHNOLOGY 2017 (ICAST'17), Kedah, Malaysia, p. 020089, 2017.
- Tripathy, A. Agrawal, and S. K. Rath, "Classification of Sentimental Reviews Using Machine Learning Techniques," in Procedia Computer Science, vol. 57, pp. 821-829, 2015.
- X. Fang and J. Zhan, "Sentiment analysis using product review data," J. Big Data, vol. 2, no. 1, p. 5, Jun. 2015.
- W. Medhat, A. Hassan, and H. Korashy, "Sentiment analysis algorithms and applications: A survey," Ain Shams Eng. J., vol. 5, no. 4, pp. 1093-1113, Dec. 2014.
- Z. Jianqiang and G. Xiaolin, "Comparison Research on Text Pre-processing Methods on Twitter Sentiment Analysis," IEEE Access, vol. 5, pp. 2870-2879, 2017.
- J. Kennedy and R. Eberhart, "Particle swarm optimization," in Proceedings of ICNN'95 - International Conference on Neural Networks, Perth, WA, Australia, vol. 4, pp. 1942-1948, 1995.
- T. Zeugmann et al., "Particle Swarm Optimization," in Encyclopedia of Machine Learning, C. Sammut and G. I. Webb, Eds. Boston, MA: Springer US, pp. 760-766, 2011.
- Koohi and V. Z. Groza, "Optimizing Particle Swarm Optimization algorithm," in 2014 IEEE 27th Canadian Conference on Electrical and Computer Engineering (CCECE), Toronto, ON, Canada, pp. 1 -5, 2014.
- X. S. Yang and S. Deb, "Engineering optimisation by cuckoo search," Int. J. Math. Model. Numer. Optim., vol. 1, no. 4, p. 330.
- O. Alm, D. Roth, and R. Sproat, "Emotions from Text: Machine Learning for Text-based Emotion Prediction," in HLT/EMNLP, 2005.
- J.-S. Chou, M.-Y. Cheng, Y.-W. Wu, and A.-D. Pham, "Optimizing parameters of support vector machine using fast messy genetic algorithm for dispute classification," Expert Syst. Appl., vol. 41, no. 8, pp. 3955-3964, Jun. 2014.
- P. Yang and Y. Chen, "A survey on sentiment analysis by using machine learning methods," in 2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), pp. 117-121, 2017.
Abstract Views: 172
PDF Views: 0