A Novel Method to Reduce False Positives and Negatives in Sentiment Analysis



Machine Learning, Sentiment Analysis, Natural Language Processing


Sentiment analysis focuses on the prediction of sentiment of the data by text processing, feature extraction, vectorization and classification techniques. The research areas in Sentiment analysis used to focus on the model that gives more accurate positive prediction. Reduction of False Positives and Negatives which are called Type I and II errors are not much dealt with. The best model may not be the best in reducing false positives and negatives. In this work rule-based and machine-learning algorithms are experimented to create a suitable model that gives equal importance to the reduction of positive and negative false values and accuracy. Experimented studies revealed that the model suggested using Linear Regression classifier that gave an overall accuracy of 62.35 % is the one that gives a better reduction in Type I and II error along with a competitive accuracy than SVM based model which was having the highest accuracy of 62.4%.


Download data is not yet available.


ZulfadzliDrus,HaliyanaKhalid “ Sentiment Analyis in social media and its applications”, Procedia Computer ScienceVolume 161, 2019, Pages 707-714

Slavko Žitnik ,Neli Blagus Marko Bajec,” Target-level sentiment analysis for news articles:, knowledge-Based SystemsVolume 249, 5 August 2022, 108939

Wankhade, M., Rao, A.C.S. & Kulkarni, C. A survey on sentiment analysis methods, applications, and challenges. Artif Intell Rev (2022). https://doi.org/10.1007/s10462-022-10144-1

Ananthakrishnan, B., V. . Padmaja, S. . Nayagi, and V. . M. “Deep Neural Network Based Anomaly Detection for Real Time Video Surveillance”. International Journal on Recent and Innovation Trends in Computing and Communication, vol. 10, no. 4, Apr. 2022, pp. 54-64, doi:10.17762/ijritcc.v10i4.5534.

Gupta, Brij Mohan & Dhawan, Surinder. (2020). Sentiment Analysis Research : A Scientometrics Assessment of Global Publications for the Period 2003-2020. International Journal of Information Dissemination and Technology. 10. 134-140. 10.5958/2249-5576.2020.00024.2.

R. A. Tuhin, B. K. Paul, F. Nawrine, M. Akter and A. K. Das, "An Automated System of Sentiment Analysis from Bangla Text using Supervised Learning Techniques", 2019 IEEE 4th International Conference on Computer and Communication Systems (ICCCS), pp. 360-364, february. 2019.

Hermina, J. ., Karpagam, N. S. ., Deepika, P. ., Jeslet, D. S. ., & Komarasamy, D. (2022). A Novel Approach to Detect Social Distancing Among People in College Campus. International Journal of Intelligent Systems and Applications in Engineering, 10(2), 153–158. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/1823

Acheampong FA, Wenyu C, Nunoo-Mensah H (2020) Text-based emotion detection: advances, challenges, and opportunities. Eng Rep 2(7):e12189

Ahmad S, Asghar MZ, Alotaibi FM, Awan I (2019) Detection and classification of social media-based extremist affiliations using sentiment analysis techniques. Hum Centric Comput Inf Sci 9(1):1–23

Ahmad SR, Bakar AA, Yaakub MR (2019) A review of feature selection techniques in sentiment analysis. Intell Data Anal 23(1):159–189

Akhtar N, Zubair N, Kumar A, Ahmad T (2017) Aspect based sentiment oriented summarization of hotel reviews. Procedia Comput Sci 115:563–571

Garg, D. K. . (2022). Understanding the Purpose of Object Detection, Models to Detect Objects, Application Use and Benefits. International Journal on Future Revolution in Computer Science &Amp; Communication Engineering, 8(2), 01–04. https://doi.org/10.17762/ijfrcsce.v8i2.2066

Borg A, Boldt M (2020) Using VADER sentiment and SVM for predicting customer response sentiment. Expert Syst Appl 162:113746

Cambria E, Das D, Bandyopadhyay S, Feraco A (2017) Affective computing and sentiment analysis. In: A practical g uide to sentiment analysis. Springer, pp 1–10

Ahmad, Munir & Aftab, Shabib & Salman, Muhammad & Hameed, Noureen. (2018). Sentiment Analysis using SVM: A Systematic Literature Review. International Journal of Advanced Computer Science and Applications. 9. 10.14569/IJACSA.2018.090226.

Zainuddin, Nurulhuda & Selamat, Ali. (2014). Sentiment analysis using Support Vector Machine. I4CT 2014 - 1st International Conference on Computer, Communications, and Control Technology, Proceedings. 333-337. 10.1109/I4CT.2014.6914200.

Yassine Al Amrani, Mohamed Lazaar, Kamal Eddine El Kadiri,Random Forest and Support Vector Machine based Hybrid Approach to Sentiment Analysis,Procedia Computer Science,Volume 127,2018,Pages 511-520,ISSN 1877-0509,https://doi.org/10.1016/j.procs.2018.01.150.

Yang, C.-S & Shih, H.-P. (2012). A rule-based approach for effective sentiment analysis. Proceedings - Pacific Asia Conference on Information Systems, PACIS 2012.

M. J. Traum, J. Fiorentine. (2021). Rapid Evaluation On-Line Assessment of Student Learning Gains for Just-In-Time Course Modification. Journal of Online Engineering Education, 12(1), 06–13. Retrieved from http://onlineengineeringeducation.com/index.php/joee/article/view/45

S. Zahoor and R. Rohilla, "Twitter Sentiment Analysis Using Lexical or Rule Based Approach: A Case Study," 2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), 2020, pp. 537-542, doi: 10.1109/ICRITO48877.2020.9197910.

R. Rafeek and R. Remya, "Detecting contextual word polarity using aspect based sentiment analysis and logistic regression," 2017 IEEE International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM), 2017, pp. 102-107, doi: 10.1109/ICSTM.2017.8089134.

St, Indra & Wikarsa, Liza & Turang, Rinaldo. (2016). Using logistic regression method to classify tweets into the selected topics. 385-390. 10.1109/ICACSIS.2016.7872727.

Singh, J., Singh, G. & Singh, R. Optimization of sentiment analysis using machine learning classifiers. Hum. Cent. Comput. Inf. Sci. 7, 32 (2017). https://doi.org/10.1186/s13673-017-0116-3

Le, B., Nguyen, H. (2015). Twitter Sentiment Analysis Using Machine Learning Techniques. In: Le Thi, H., Nguyen, N., Do, T. (eds) Advanced Computational Methods for Knowledge Engineering. Advances in Intelligent Systems and Computing, vol 358. Springer, Cham. https://doi.org/10.1007/978-3-319-17996-4_25

Outline of the proposed methodology




How to Cite

C. . Aloysius and P. . Tamilselvan, “A Novel Method to Reduce False Positives and Negatives in Sentiment Analysis”, Int J Intell Syst Appl Eng, vol. 10, no. 3, pp. 365–373, Oct. 2022.



Research Article