Kecenderungan Tanggapan Masyarakat terhadap Ekonomi Indonesia berbasis Lexicon Based Sentiment Analysis
DOI:
https://doi.org/10.30865/mib.v3i4.1283Abstract
Sentiment analysis aims to find opinions, identify sentiments expressed, and then classify their polarity values. One method of sentiment analysis is Lexicon-based. This study implements the Lexicon based sentiment analysis to analyze the polarity of public responses to the topic of the development of "the Indonesian economy". The dataset is collected from social media from 2017 to 2019. Preprocessing used is folding cases, deleting newline characters, changing non-standard words, deleting mentions, deleting hashtags, removing URL strings, changing word negation, and translating text into English with TextBlob library. Then extract the sentiment values from adjectives, adverbs, nouns, and verbs found in the text. Based on the results of sentiment analysis, it can be seen that there are 63.6% positive responses from the public to the development of the Indonesian economy, 7.4% negative responses, and 29% neutral.
References
Badan Pusat Statistik, “Jumlah dan Distribusi Penduduk,†2010. [Daring]. Tersedia pada: https://sp2010.bps.go.id/. [Diakses: 02-Jul-2019].
Badan Pusat Statistik, “Berita Resmi Statistik (6 Mei 2019),†06-Mei-2019.
Y. Pratomo, “APJII: Jumlah Pengguna Internet di Indonesia Tembus 171 Juta Jiwa,†kompas.com, 16-Mei-2019.
W. Medhat, A. Hassan, dan H. Korashy, “Sentiment Analysis Algorithms and Applications: A Survey,†Ain Shams Eng. J., vol. 5, hal. 1093–1113, 2014.
F. M. Matulatuwa, E. Sediyono, dan A. Iriani, “Text Mining dengan Metode Lexicon Based untuk Sentiment Analysis Pelayanan PT. Pos Indonesia Melalui Media Sosial Twitter,†J. Masy. Inform. Indones., vol. 2, no. 3, hal. 52–65, 2017.
C. Sutami, “Perbandingan Metode Klasifikasi Naive Bayes Classifier Dan Lexicon Based,†Universitas Widyatama, Bandung, 2015.
I. Kusumawati, “Analisa Sentimen Menggunakan Lexicon Based Kenaikan Harga Rokok Pada Media Sosial Twitter,†Universitas Muhamadiyah Surakarta, 2017.
S. Chatterjee dan M. Krystyanczuk, Python Social Media Analytics. Birmingham: Packt Publishing, 2017.
G. A. Buntoro, T. B. Adji, dan A. E. Purnamasari, “Sentiment Analysis Twitter dengan Kombinasi Lexicon Based dan Double Propagation,†CITEE, hal. 39–43, 2014.
S. Baccianella, A. Esuli, dan F. Sebastiani, “SENTIWORDNET 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining,†in Proceeding of Language Resources and Evaluation Conference, 2010, vol. 10, hal. 2200–2204.
Erik Cambria, E. Cambria, D. Olsher, dan D. Rajagopal, “SenticNet 3: A common and common-sense knowledge base for cognition-driven sentiment analysis,†in Proceedings of AAAI, 2014, hal. 1515–1521.
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