Sentiment Analysis Using Random Forest Algorithm-Online Social Media Based
DOI:
https://doi.org/10.30818/jitu.2.2.2695Keywords:
sentiment analysis, random forest algorithm, clasification, machine learningAbstract
Every day billions of data in the form of text flood the internet be it sourced from forums, blogs, social media, or review sites. With the help of sentiment analysis, previously unstructured data can be transformed into more structured data and make this data important information. The data can describe opinions / sentiments from the public, about products, brands, community services, services, politics, or other topics. Sentiment analysis is one of the fields of Natural Language Processing (NLP) that builds systems for recognizing and extracting opinions in text form. At the most basic level, the goal is to get emotions or 'feelings' from a collection of texts or sentences. The field of sentiment analysis, or also called 'opinion mining', always involves some form of data mining process to get the text that will later be carried out the learning process in the mechine learning that will be built. this study conducts a sentimental analysis with data sources from Twitter using the Random Forest algorithm approach, we will measure the evaluation results of the algorithm we use in this study. The accuracy of measurements in this study, around 75%. the model is good enough. but we suggest trying other algorithms in further research.
References
C. J. Hutto and E. E. Gilbert, “VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Eighth International Conference on Weblogs and Social Media (ICWSM-14).”,” Proc. 8th Int. Conf. Weblogs Soc. Media, ICWSM 2014, 2014.
N. Bahrawi, “Online Realtime Sentiment Analysis Tweets by Utilizing Streaming API Features From Twitter,” J. Penelit. Pos dan Inform., vol. 9, no. 1, pp. 53–62, 2019.
Y. Wan and Q. Gao, “An Ensemble Sentiment Classification System of Twitter Data for Airline Services Analysis,” 2015.
L. Dey, S. Chakraborty, A. Biswas, B. Bose, and S. Tiwari, “Sentiment Analysis of Review Datasets using Naïve Bayes’ and K-NN Classifier.”
F. Nurhuda, S. Widya Sihwi, and A. Doewes, “Analisis Sentimen Masyarakat terhadap Calon Presiden Indonesia 2014 berdasarkan Opini dari Twitter Menggunakan Metode Naive Bayes Classifier,” J. Teknol. Inf. ITSmart, vol. 2, no. 2, p. 35, 2016.
A. Hamzah, “Sentiment Analysis Untuk Memanfaatkan Saran Kuesioner Dalam Evaluasi Pembelajaran Dengan Menggunakan Naive Bayes Classifier (NBC,” 2014.
D. Setyawan and E. Winarko, “Analisis Opini Terhadap Fitur Smartphone Pada Ulasan Website Berbahasa Indonesia,” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 10, no. 2, pp. 183–194, 2016.
I. Zulfa and E. Winarko, “Sentimen Analisis Tweet Berbahasa Indonesia Dengan Deep Belief Network,” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 11, no. 2, p. 187, 2017.
D. P. Artanti, A. Syukur, A. Prihandono, and D. R. I. M. Setiadi, “Analisa Sentimen Untuk Penilaian Pelayanan Situs Belanja Online Menggunakan Algoritma Naïve Bayes,” pp. 8–9, 2018.
R. Feldman and J. Sanger, “The Text Mining Handbook,” 2006.
M. Anjali and G. Jivani, “A Comparative Study of Stemming Algorithms.”
R. Stephen, “Understanding inverse document frequency: on theoretical arguments for IDF,” J. Doc., vol. 60, no. 5, pp. 503–520, Jan. 2004.
S. J. Karen, “IDF term weighting and IR research lessons,” J. Doc., vol. 60, no. 5, pp. 521–523, Jan. 2004.
Ö. Akar, O. Gungor, and O. Güngör, “Classification of Multispectral Images Using Random Forest Algorithm View project 3D mapping View project Classification of multispectral images using Random Forest algorithm,” vol. 1, no. , pp. 105–112, 2012.
L. Breiman, “RANDOM FORESTS,” 2001.
K. Archer and R. Kimes, “Empirical characterization of random forest variable importance measures,” Comput. Stat. Data Anal., vol. 52, pp. 2249–2260, 2008.
L. Breiman and A. Cutler, “INTERFACE WORKSHOP-APRIL 2004 RFtools-for Predicting and Understanding Data.”
L. B. and A. Cutler, “Random forests - copyright.” [Online]. Available: https://www.stat.berkeley.edu/~breiman/RandomForests/cc_papers.htm. [Accessed: 26-Nov-2019].
A. Liaw and M. Wiener, “Classification and Regression by RandomForest,” 2002.
Downloads
Published
Issue
Section
License
The proposed policy for journals that offer open access
Authors who publish with this journal agree to the following terms:
- Copyright on any article is retained by the author(s).
- Author grant the journal, right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement 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 acknowledgement 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.
- The article and any associated published material is distributed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License
