Sentiment Analysis of Community Response Indonesia Against Covid-19 on Twitter Based on Negation Handling

Viry Puspaning Ramadhan; Purwanto Purwanto; Farrikh Alzami

doi:10.22219/kinetik.v7i2.1429

Issue

Vol. 7, No. 2, May 2022

Issue Published : May 31, 2022

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

Sentiment Analysis of Community Response Indonesia Against Covid-19 on Twitter Based on Negation Handling

https://doi.org/10.22219/kinetik.v7i2.1429

Viry Puspaning Ramadhan

Universitas Dian Nuswantoro

Purwanto Purwanto

Universitas Dian Nuswantoro

Farrikh Alzami

Universitas Dian Nuswantoro

Corresponding Author(s) : Viry Puspaning Ramadhan

puspa.viry@gmail.com

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 7, No. 2, May 2022
Article Published : May 31, 2022

Abstract

The use of the internet globally, especially on the use of social media, includes Indonesia as one of the most active users in the world. The amount of information that can be obtained can be used to be processed into useful information, for example, information about the public sentiment on a particular topic. Tracking and analyzing tweets can be a method to find out people's thoughts, behavior, and reactions regarding the impact of Covid-19. The key to sentiment analysis is the determination of polarity, which determines whether the sentiment is positive or negative. The word negation in a sentence can change the polarity of the sentence so that if it is not handled properly it will affect the performance of the sentiment classification. In this study, the implementation of negation handling on sentiment analysis of Indonesian people's opinions regarding COVID-19 on Twitter has proven to be good enough to improve the performance of the classifier. Accuracy results obtained are 59.6% compared to adding negation handling accuracy obtained is 59.1%. Although the percentage result is not high, documents that include negative sentences have more meaning than negative sentences. However, for the evaluation using the MCC evaluation matrix, the results were quite good for the testing data. For the results of the proposed method whether it is suitable for data that has two classes or three classes when viewed from the results of the evaluation matrix, the proposed method is more suitable for binary data or data that has only two classes.

Keywords

Text Mining Analysis Sentiment Negation Handling MCC

Ramadhan, V. P., Purwanto, P., & Alzami, F. (2022). Sentiment Analysis of Community Response Indonesia Against Covid-19 on Twitter Based on Negation Handling. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 7(2). https://doi.org/10.22219/kinetik.v7i2.1429

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References

K. Chakraborty, S. Bhatia, S. Bhattacharyya, J. Platos, R. Bag, and A. E. Hassanien, “Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media,” Appl. Soft Comput. J., vol. 97, p. 106754, 2020, doi: 10.1016/j.asoc.2020.106754.
Imamah and F. H. Rachman, “Twitter sentiment analysis of Covid-19 using term weighting TF-IDF and logistic regresion,” Proceeding - 6th Inf. Technol. Int. Semin. ITIS 2020, pp. 238–242, 2020, doi: 10.1109/ITIS50118.2020.9320958.
I. U. W. Statistic, “Internet Users Worldwide Statistic,” 2020. .
Z. Tariq Soomro, S. H. Waseem Ilyas, and U. Yaqub, “Sentiment, Count and Cases: Analysis of Twitter discussions during COVID-19 Pandemic,” Proc. 2020 7th IEEE Int. Conf. Behav. Soc. Comput. BESC 2020, 2020, doi: 10.1109/BESC51023.2020.9348291.
K. Mouthami, K. N. Devi, and V. M. Bhaskaran, “Sentiment analysis and classification based on textual reviews,” 2013 Int. Conf. Inf. Commun. Embed. Syst. ICICES 2013, pp. 271–276, 2013, doi: 10.1109/ICICES.2013.6508366.
A. Tripathy, A. Agrawal, and S. K. Rath, “Classification of sentiment reviews using n-gram machine learning approach,” Expert Syst. Appl., vol. 57, pp. 117–126, 2016, doi: 10.1016/j.eswa.2016.03.028.
U. Farooq, “Negation Handling in Sentiment Analysis at Sentence Level,” J. Comput., no. January, pp. 470–478, 2017, doi: 10.17706/jcp.12.5.470-478.
C. Diamantini, A. Mircoli, and D. Potena, “A negation handling technique for sentiment analysis,” Proc. - 2016 Int. Conf. Collab. Technol. Syst. CTS 2016, pp. 188–195, 2016, doi: 10.1109/CTS.2016.46.
F. S. Fitri, M. N. S. Si, and C. Setianingsih, “Sentiment analysis on the level of customer satisfaction to data cellular services using the naive bayes classifier algorithm,” Proc. - 2018 IEEE Int. Conf. Internet Things Intell. Syst. IOTAIS 2018, pp. 201–206, 2019, doi: 10.1109/IOTAIS.2018.8600870.
A. M. Ningtyas and G. B. Herwanto, “The Influence of Negation Handling on Sentiment Analysis in Bahasa Indonesia,” Proc. 2018 5th Int. Conf. Data Softw. Eng. ICoDSE 2018, pp. 1–6, 2018, doi: 10.1109/ICODSE.2018.8705802.
R. Amalia, M. A. Bijaksana, and D. Darmantoro, “Negation handling in sentiment classification using rule-based adapted from Indonesian language syntactic for Indonesian text in Twitter,” J. Phys. Conf. Ser., vol. 971, no. 1, 2018, doi: 10.1088/1742-6596/971/1/012039.
P. H. Prastyo, A. S. Sumi, A. W. Dian, and A. E. Permanasari, “Tweets Responding to the Indonesian Government’s Handling of COVID-19: Sentiment Analysis Using SVM with Normalized Poly Kernel,” J. Inf. Syst. Eng. Bus. Intell., vol. 6, no. 2, p. 112, 2020, doi: 10.20473/jisebi.6.2.112-122.
A. Thakkar and K. Chaudhari, “Predicting stock trend using an integrated term frequency–inverse document frequency-based feature weight matrix with neural networks,” Appl. Soft Comput. J., vol. 96, p. 106684, 2020, doi: 10.1016/j.asoc.2020.106684.
M. A. Rosid, A. S. Fitrani, I. R. I. Astutik, N. I. Mulloh, and H. A. Gozali, “Improving Text Preprocessing for Student Complaint Document Classification Using Sastrawi,” IOP Conf. Ser. Mater. Sci. Eng., vol. 874, no. 1, 2020, doi: 10.1088/1757-899X/874/1/012017.
R. B. Trianto, A. Triyono, and D. M. P. Arum, “Klasifikasi Rating Otomatis pada Dokumen Teks Ulasan Produk Elektronik Menggunakan Metode N-gram dan Naïve Bayes,” J. Inform. Univ. Pamulang, vol. 5, no. 3, p. 295, 2020, doi: 10.32493/informatika.v5i3.6110.
B. Trstenjak, S. Mikac, and D. Donko, “KNN with TF-IDF based framework for text categorization,” Procedia Eng., vol. 69, pp. 1356–1364, 2014, doi: 10.1016/j.proeng.2014.03.129.
F. E. Cahyanti, Adiwijaya, and S. Al Faraby, “On the Feature Extraction for Sentiment Analysis of Movie Reviews Based on SVM,” 2020 8th Int. Conf. Inf. Commun. Technol. ICoICT 2020, 2020, doi: 10.1109/ICoICT49345.2020.9166397.
K. V. Ghag and K. Shah, “Negation Handling for Sentiment Classification,” Proc. - 2nd Int. Conf. Comput. Commun. Control Autom. ICCUBEA 2016, 2017, doi: 10.1109/ICCUBEA.2016.7860016.
S. -, A. Fadlil, and S. -, “Analisis Sentimen Menggunakan Metode Naïve Bayes Classifier Pada Angket Mahasiswa,” Saintekbu, vol. 10, no. 2, pp. 1–9, 2018, doi: 10.32764/saintekbu.v10i2.190.
U. Pujianto, M. F. Hidayat, and H. A. Rosyid, “Text Difficulty Classification Based on Lexile Levels Using K-Means Clustering and Multinomial Naive Bayes,” Proc. - 2019 Int. Semin. Appl. Technol. Inf. Commun. Ind. 4.0 Retrosp. Prospect. Challenges, iSemantic 2019, pp. 163–170, 2019, doi: 10.1109/ISEMANTIC.2019.8884317.
A. Tanzeh and S. Arikunto, “Bab III - Metode Penelitian Metode Penelitian,” Metod. Penelit., pp. 22–34, 2004.
N. Hasdyna and R. K. Dinata, “Analisis Matthew Correlation Coefficient pada K-Nearest Neighbor dalam Klasifikasi Ikan Hias,” INFORMAL Informatics J., vol. 5, no. 2, p. 57, 2020, doi: 10.19184/isj.v5i2.18907.
S. S. Bhanuse, S. D. Kamble, and S. M. Kakde, “Text Mining Using Metadata for Generation of Side Information,” Phys. Procedia, vol. 78, no. December 2015, pp. 807–814, 2016, doi: 10.1016/j.procs.2016.02.061.
S. Boughorbel, F. Jarray, and M. El-Anbari, “Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric,” PLoS One, vol. 12, no. 6, pp. 1–17, 2017, doi: 10.1371/journal.pone.0177678.
A. Hogenboom, P. Van Iterson, B. Heerschop, F. Frasincar, and U. Kaymak, “Determining negation scope and strength in sentiment analysis,” Conf. Proc. - IEEE Int. Conf. Syst. Man Cybern., pp. 2589–2594, 2011, doi: 10.1109/ICSMC.2011.6084066.

References

K. Chakraborty, S. Bhatia, S. Bhattacharyya, J. Platos, R. Bag, and A. E. Hassanien, “Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media,” Appl. Soft Comput. J., vol. 97, p. 106754, 2020, doi: 10.1016/j.asoc.2020.106754.

Imamah and F. H. Rachman, “Twitter sentiment analysis of Covid-19 using term weighting TF-IDF and logistic regresion,” Proceeding - 6th Inf. Technol. Int. Semin. ITIS 2020, pp. 238–242, 2020, doi: 10.1109/ITIS50118.2020.9320958.

I. U. W. Statistic, “Internet Users Worldwide Statistic,” 2020. .

Z. Tariq Soomro, S. H. Waseem Ilyas, and U. Yaqub, “Sentiment, Count and Cases: Analysis of Twitter discussions during COVID-19 Pandemic,” Proc. 2020 7th IEEE Int. Conf. Behav. Soc. Comput. BESC 2020, 2020, doi: 10.1109/BESC51023.2020.9348291.

K. Mouthami, K. N. Devi, and V. M. Bhaskaran, “Sentiment analysis and classification based on textual reviews,” 2013 Int. Conf. Inf. Commun. Embed. Syst. ICICES 2013, pp. 271–276, 2013, doi: 10.1109/ICICES.2013.6508366.

A. Tripathy, A. Agrawal, and S. K. Rath, “Classification of sentiment reviews using n-gram machine learning approach,” Expert Syst. Appl., vol. 57, pp. 117–126, 2016, doi: 10.1016/j.eswa.2016.03.028.

U. Farooq, “Negation Handling in Sentiment Analysis at Sentence Level,” J. Comput., no. January, pp. 470–478, 2017, doi: 10.17706/jcp.12.5.470-478.

C. Diamantini, A. Mircoli, and D. Potena, “A negation handling technique for sentiment analysis,” Proc. - 2016 Int. Conf. Collab. Technol. Syst. CTS 2016, pp. 188–195, 2016, doi: 10.1109/CTS.2016.46.

F. S. Fitri, M. N. S. Si, and C. Setianingsih, “Sentiment analysis on the level of customer satisfaction to data cellular services using the naive bayes classifier algorithm,” Proc. - 2018 IEEE Int. Conf. Internet Things Intell. Syst. IOTAIS 2018, pp. 201–206, 2019, doi: 10.1109/IOTAIS.2018.8600870.

A. M. Ningtyas and G. B. Herwanto, “The Influence of Negation Handling on Sentiment Analysis in Bahasa Indonesia,” Proc. 2018 5th Int. Conf. Data Softw. Eng. ICoDSE 2018, pp. 1–6, 2018, doi: 10.1109/ICODSE.2018.8705802.

R. Amalia, M. A. Bijaksana, and D. Darmantoro, “Negation handling in sentiment classification using rule-based adapted from Indonesian language syntactic for Indonesian text in Twitter,” J. Phys. Conf. Ser., vol. 971, no. 1, 2018, doi: 10.1088/1742-6596/971/1/012039.

P. H. Prastyo, A. S. Sumi, A. W. Dian, and A. E. Permanasari, “Tweets Responding to the Indonesian Government’s Handling of COVID-19: Sentiment Analysis Using SVM with Normalized Poly Kernel,” J. Inf. Syst. Eng. Bus. Intell., vol. 6, no. 2, p. 112, 2020, doi: 10.20473/jisebi.6.2.112-122.

A. Thakkar and K. Chaudhari, “Predicting stock trend using an integrated term frequency–inverse document frequency-based feature weight matrix with neural networks,” Appl. Soft Comput. J., vol. 96, p. 106684, 2020, doi: 10.1016/j.asoc.2020.106684.

M. A. Rosid, A. S. Fitrani, I. R. I. Astutik, N. I. Mulloh, and H. A. Gozali, “Improving Text Preprocessing for Student Complaint Document Classification Using Sastrawi,” IOP Conf. Ser. Mater. Sci. Eng., vol. 874, no. 1, 2020, doi: 10.1088/1757-899X/874/1/012017.

R. B. Trianto, A. Triyono, and D. M. P. Arum, “Klasifikasi Rating Otomatis pada Dokumen Teks Ulasan Produk Elektronik Menggunakan Metode N-gram dan Naïve Bayes,” J. Inform. Univ. Pamulang, vol. 5, no. 3, p. 295, 2020, doi: 10.32493/informatika.v5i3.6110.

B. Trstenjak, S. Mikac, and D. Donko, “KNN with TF-IDF based framework for text categorization,” Procedia Eng., vol. 69, pp. 1356–1364, 2014, doi: 10.1016/j.proeng.2014.03.129.

F. E. Cahyanti, Adiwijaya, and S. Al Faraby, “On the Feature Extraction for Sentiment Analysis of Movie Reviews Based on SVM,” 2020 8th Int. Conf. Inf. Commun. Technol. ICoICT 2020, 2020, doi: 10.1109/ICoICT49345.2020.9166397.

K. V. Ghag and K. Shah, “Negation Handling for Sentiment Classification,” Proc. - 2nd Int. Conf. Comput. Commun. Control Autom. ICCUBEA 2016, 2017, doi: 10.1109/ICCUBEA.2016.7860016.

S. -, A. Fadlil, and S. -, “Analisis Sentimen Menggunakan Metode Naïve Bayes Classifier Pada Angket Mahasiswa,” Saintekbu, vol. 10, no. 2, pp. 1–9, 2018, doi: 10.32764/saintekbu.v10i2.190.

U. Pujianto, M. F. Hidayat, and H. A. Rosyid, “Text Difficulty Classification Based on Lexile Levels Using K-Means Clustering and Multinomial Naive Bayes,” Proc. - 2019 Int. Semin. Appl. Technol. Inf. Commun. Ind. 4.0 Retrosp. Prospect. Challenges, iSemantic 2019, pp. 163–170, 2019, doi: 10.1109/ISEMANTIC.2019.8884317.

A. Tanzeh and S. Arikunto, “Bab III - Metode Penelitian Metode Penelitian,” Metod. Penelit., pp. 22–34, 2004.

N. Hasdyna and R. K. Dinata, “Analisis Matthew Correlation Coefficient pada K-Nearest Neighbor dalam Klasifikasi Ikan Hias,” INFORMAL Informatics J., vol. 5, no. 2, p. 57, 2020, doi: 10.19184/isj.v5i2.18907.

S. S. Bhanuse, S. D. Kamble, and S. M. Kakde, “Text Mining Using Metadata for Generation of Side Information,” Phys. Procedia, vol. 78, no. December 2015, pp. 807–814, 2016, doi: 10.1016/j.procs.2016.02.061.

S. Boughorbel, F. Jarray, and M. El-Anbari, “Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric,” PLoS One, vol. 12, no. 6, pp. 1–17, 2017, doi: 10.1371/journal.pone.0177678.

A. Hogenboom, P. Van Iterson, B. Heerschop, F. Frasincar, and U. Kaymak, “Determining negation scope and strength in sentiment analysis,” Conf. Proc. - IEEE Int. Conf. Syst. Man Cybern., pp. 2589–2594, 2011, doi: 10.1109/ICSMC.2011.6084066.

Issue

Vol. 7, No. 2, May 2022

Sentiment Analysis of Community Response Indonesia Against Covid-19 on Twitter Based on Negation Handling

Corresponding Author(s) : Viry Puspaning Ramadhan

Abstract

Keywords

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References

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