Aspect-based Sentiment Analysis on Social Media Using Convolutional Neural Network (CNN) Method
Abstract
Social media are a platform for people to express their opinions on various topics, one of which is Twitter. Movie reviews are a frequently found topic on Twitter that contains a person's opinion of a movie that has been watched. But since opinions are subjective, it is difficult to determine an accurate assessment of a movie. In addition, the diverse aspects of a movie make it difficult to judge whether a review is positive or negative. Referring to that problem, a method is needed to perform sentiment analysis of the problem. In this study, sentiment analysis of movie reviews was carried out based on aspects of plot, acting, and director. This research also performs classification using a CNN model and combines several techniques, namely TF-IDF feature extraction, FastText feature expansion, and SMOTE to calculate the accuracy value and F1-Score. The final results obtained in this study are in the aspect of the plot getting an accuracy of 73.81% (+12,22%) and F1-score 73.72% (+15,93%), the acting aspect obtaining an accuracy value of 89.30% (+0,54%) and F1-score 89.26% (+50,80%), and in the aspect of the director having an accuracy of 87.37% (+0,28%) and F1-score 87.35% (+84,39%). Based on these results, each application of techniques such as TF-IDF, FastText, and SMOTE can increase the accuracy value and F1-Score of the model built.
Downloads
References
F. Li, J. Larimo, and L. C. Leonidou, “Social media marketing strategy: definition, conceptualization, taxonomy, validation, and future agenda,” J. Acad. Mark. Sci., vol. 49, no. 1, pp. 51–70, 2021, doi: 10.1007/s11747-020-00733-3.
Z. Drus and H. Khalid, “Sentiment analysis in social media and its application: Systematic literature review,” Procedia Comput. Sci., vol. 161, pp. 707–714, 2019, doi: 10.1016/j.procs.2019.11.174.
N. A. AlSomaikhi and Z. A. Alzamil, “Twitter Users’ Classification Based on Interest,” Int. J. Inf. Retr. Res., vol. 10, no. 1, pp. 1–12, 2020, doi: 10.4018/ijirr.2020010101.
C. Nanda, M. Dua, and G. Nanda, “Sentiment Analysis of Movie Reviews in Hindi Language Using Machine Learning,” Proc. 2018 IEEE Int. Conf. Commun. Signal Process. ICCSP 2018, pp. 1069–1072, 2018, doi: 10.1109/ICCSP.2018.8524223.
N. A. Shafirra and I. Irhamah, “Klasifikasi Sentimen Ulasan Film Indonesia dengan Konversi Speech-to-Text (STT) Menggunakan Metode Convolutional Neural Network (CNN),” J. Sains dan Seni ITS, vol. 9, no. 1, 2020, doi: 10.12962/j23373520.v9i1.51825.
V. Parkhe and B. Biswas, “Aspect based sentiment analysis of movie reviews: Finding the polarity directing aspects,” Proc. - 2014 Int. Conf. Soft Comput. Mach. Intell. ISCMI 2014, pp. 28–32, 2014, doi: 10.1109/ISCMI.2014.16.
A. Alsaeedi and M. Z. Khan, “A study on sentiment analysis techniques of Twitter data,” Int. J. Adv. Comput. Sci. Appl., vol. 10, no. 2, pp. 361–374, 2019, doi: 10.14569/ijacsa.2019.0100248.
M. Cindo and D. P. Rini, “Literatur Review: Metode Klasifikasi Pada Sentimen Analisis,” Semin. Nas. Teknol. Komput. Sains, pp. 66–70, 2019, [Online]. Available: https://seminar-id.com/semnas-sainteks2019.html
P. A. Nugroho, I. Fenriana, and R. Arijanto, “Implementasi Deep Learning Menggunakan Convolutional Neural Network Pada Ekspresi Manusia.” Jurnal ALGOR, 2020.
R. Kumar and S. Garg, Aspect-Based Sentiment Analysis Using Deep Learning Convolutional Neural Network, vol. 933. Springer Singapore, 2020. doi: 10.1007/978-981-13-7166-0_5.
M. T. Ari Bangsa, S. Priyanta, and Y. Suyanto, “Aspect-Based Sentiment Analysis of Online Marketplace Reviews Using Convolutional Neural Network,” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 14, no. 2, p. 123, 2020, doi: 10.22146/ijccs.51646.
A. Nurdin, B. A. S. Aji, A. Bustamin, and Z. Abidin, “Perbandingan Kinerja Word Embedding Word2Vec , Glove ,” J. TEKNOKOMPAK, vol. 14, no. 2, pp. 74–79, 2020.
Aytuğ Onan, Deep Learning Based Sentiment Analysis on Product Reviews on Twitter, vol. 1054. 2019. doi: 10.1007/978-3-030-27355-2_8.
S. D. Lestari and E. B. Setiawan, “Sentiment Analysis Based on Aspects of Telkomsel Users on Twitter Using FastText Feature Expansion and NBSVM Classification,” J. Comput. Syst. Informatics, vol. 3, no. 4, pp. 469–477, 2022, doi: 10.47065/josyc.v3i4.2202.
H. R. Alhakiem and E. B. Setiawan, “Aspect-Based Sentiment Analysis on Twitter Using Logistic Regression with FastText Feature Expansion,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 6, no. 5, pp. 840–846, 2022, doi: 10.29207/resti.v6i5.4429.
Muhammad Afif Raihan and Erwin Budi Setiawan, “Aspect Based Sentiment Analysis with FastText Feature Expansion and Support Vector Machine Method on Twitter,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 6, no. 4, pp. 591–598, 2022, doi: 10.29207/resti.v6i4.4187.
R. A. Annisa and E. B. Setiawan, “Aspect Based Sentiment Analysis on Twitter Using Word2Vec Feature Expansion Method and Gradient Boosting Decision Tree Classification Method,” IEEE, 2022.
A. M. Zakaria and E. B. Setiawan, “Aspect-Based Analysis of Telkomsel User Sentiment on Twitter Using the Random Forest Classification Method and Glove Feature Expansion,” J. Teknol. dan Sist. …, 2022, [Online]. Available: https://jtsiskom.undip.ac.id/article/view/14558
D. A. Widodo, N. Iksan, and B. Sunarko, “Sentiment Analysis of Twitter Media for Public Reaction Identification on Covid-19 Monitoring System using Hybrid Feature Extraction Method.” Internasional Journal of Intelligent System and Applications In Engineering, 2022.
N. Ritha, N. Hayaty, T. Matulatan, A. Uperiati, M. Rathomi, and M. Bettiza, “Sentiment Analysis of Health Protocol Policy Using K- Nearest Neighbor and Cosine Similarity,” 2023, doi: 10.4108/eai.11-10-2022.2326274.
W. Chaipanha and P. Kaewwichian, “Smote Vs. Random Undersampling for Imbalanced Data-Car Ownership Demand Model,” Commun. - Sci. Lett. Univ. Žilina, vol. 24, no. 3, pp. D105–D115, 2022, doi: 10.26552/com.C.2022.3.D105-D115.
A. Harris and E. Yanti, “Optimizing Attack Detection for High Dimensionality and Imbalanced Data with SMOTE , Chi-Square and Random Forest Classifier,” Int. J. Informatics Comput. Sci., vol. 6, no. 1, pp. 1–14, 2022, doi: 10.30865/ijics.v6i1.3890.
E. M. Dharma, F. L. Gaol, H. L. H. S. Warnars, and B. Soewito, “the Accuracy Comparison Among Word2Vec, Glove, and Fasttext Towards Convolution Neural Network (Cnn) Text Classification,” J. Theor. Appl. Inf. Technol., vol. 100, no. 2, pp. 349–359, 2022.
T. Yao, Z. Zhai, and B. Gao, “Text Classification Model Based on fastText,” Proc. 2020 IEEE Int. Conf. Artif. Intell. Inf. Syst. ICAIIS 2020, pp. 154–157, 2020, doi: 10.1109/ICAIIS49377.2020.9194939.
B. Kuyumcu, C. Aksakalli, and S. Delil, “An automated new approach in fast text classification (fastText): A case study for Turkish text classification without pre-processing,” ACM Int. Conf. Proceeding Ser., pp. 1–4, 2019, doi: 10.1145/3342827.3342828.
J. Choi and S. W. Lee, “Improving FastText with inverse document frequency of subwords,” Pattern Recognit. Lett., vol. 133, pp. 165–172, 2020, doi: 10.1016/j.patrec.2020.03.003.
R. A. Yahya and E. B. Setiawan, “Feature Expansion with FastText on Topic Classification Using the Gradient Boosted Decision Tree on Twitter,” pp. 322–327, 2022, doi: 10.1109/icoict55009.2022.9914896.
A. Jacovi, O. S. Shalom, and Y. Goldberg, “Understanding Convolutional Neural Networks for Text Classification,” EMNLP 2018 - 2018 EMNLP Work. BlackboxNLP Anal. Interpret. Neural Networks NLP, Proc. 1st Work., pp. 56–65, 2018, doi: 10.18653/v1/w18-5408.
I. Santos, N. Nedjah, and L. De Macedo Mourelle, “Sentiment analysis using convolutional neural network with fasttext embeddings,” 2017 IEEE Lat. Am. Conf. Comput. Intell. LA-CCI 2017 - Proc., vol. 2018-Janua, pp. 2–6, 2017, doi: 10.1109/LA-CCI.2017.8285683.
Z. Wang and Z. Qu, “Research on Web text classification algorithm based on improved CNN and SVM,” Int. Conf. Commun. Technol. Proceedings, ICCT, vol. 2017-Octob, pp. 1958–1961, 2018, doi: 10.1109/ICCT.2017.8359971.
D. Normawati and S. A. Prayogi, “Implementasi Naïve Bayes Classifier Dan Confusion Matrix Pada Analisis Sentimen Berbasis Teks Pada Twitter,” J-SAKTI (Jurnal Sains Komput. dan Inform., vol. 5, no. 2, pp. 697–711, 2021.
F. Novitasari and M. D. Purbolaksono, “Analysis Sentiment Aspect Level on Beauty Product Reviews Using Chi-Square and Naïve Bayes,” J. Data Sci. Its Appl., vol. 4, no. 1, pp. 18–30, 2021, [Online]. Available: https://commdis.telkomuniversity.ac.id/jdsa/index.php/jdsa/article/view/72
A. Indriani, “Klasifikasi Data Forum dengan menggunakan Metode Naïve Bayes Classifier,” Semin. Nas. Apl. Teknol. Inf. Yogyakarta, vol. 1, no. 1, pp. 21–2014, 2014, [Online]. Available: www.bluefame.com,
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Aspect-based Sentiment Analysis on Social Media Using Convolutional Neural Network (CNN) Method
Pages: 1828−1836
Copyright (c) 2023 Ananta Ihza Ramadhan, Erwin Budi Setiawan
![Creative Commons License](http://i.creativecommons.org/l/by/4.0/88x31.png)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).