A Framework for Sentiment Analysis Implementation of Indonesian Language Tweet on Twitter

Asniar, Asniar; Aditya, Bayu Rima

doi:10.1088/1742-6596/801/1/012065

Cited by 4 publications

(4 citation statements)

References 5 publications

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“…Proses Data Cleaning adalah proses untuk mengubah data yang tidak terstruktur menjadi data yang terstruktur [25]. Hasil contoh dari proses data tweets awal, data yang dinormalisasi, dan data yang sudah mengalami proses data cleaning akan dapat dilihat di Tabel I. Proses data cleaning yang dilakukan adalah:…”

Section: Data Cleaningunclassified

Pengaruh Metode Penyeimbangan Kelas Terhadap Tingkat Akurasi Analisis Sentimen pada Tweets Berbahasa Indonesia

Husada¹,

Toba²

2020

JuTISI

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Nowadays internet access is getting easier to get. Because of the ease of access to the internet, almost all internet users have social media. Social media is widely used by users to call out their opinions or even to make complaints about a matter and also discuss a topic with other social media users. From many existing social media, one that is popularly used for that activity is Twitter. Sentiment analysis on Twitter has become possible because of the activities of these Twitter users. In this research, the authors explore sentiment analysis with bag-of-words and Term Frequency Inverse Document Frequency (TF-IDF) features extraction based on tweets from Indonesian Twitter users. The data obtained is in imbalanced condition, so that it requires a method to overcome them. The method for overcoming imbalanced dataset uses a resampling approach which combines over and under sampling strategies. The results of sentiment analysis accuracies with Naïve Bayes and neural networks before and after input data resampling are also compared. Naïve Bayes methods that will be used are Multinomial Naïve Bayes and Complement Naïve Bayes, while the Neural Network architecture that will be used as a comparison are Recurrent Neural Networks, Long Short-Term Memory, Gated Recurrent Units, Convolutional Neural Networks, and a combination of Convolutional Neural Networks and Long Short-Term Memory. Our experiments show the following harmonic scores (F1) of the sentiment analysis models: the Multinomial Naïve Bayes F1 score is 55.48, Complement Naïve Bayes is 51.33, Recurrent Neural Network is 75.70, Long Short-Term Memory is 78.36, Gated Recurrent Unit is 77.96, Convolutional Neural Network is 76.12, and finally the combination of Convolutional Neural Networks and Long Short-Term Memory achieves 81.14.

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Section: Data Cleaningunclassified

Pengaruh Metode Penyeimbangan Kelas Terhadap Tingkat Akurasi Analisis Sentimen pada Tweets Berbahasa Indonesia

Husada¹,

Toba²

2020

JuTISI

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“…There are many variations of rule-based technique. Because the meaning of each word must be known and the rule of words in a sentence depends on the language used [24]. Even though any standard rule in language, but it cannot be applied completely [24].…”

Section: Sentiment Analysismentioning

confidence: 99%

“…Because the meaning of each word must be known and the rule of words in a sentence depends on the language used [24]. Even though any standard rule in language, but it cannot be applied completely [24]. The rule can define by SUM value form each word value and then compare the result but some researchers consider types of the word [9] [18] and it uses the certain rule-based.…”

Section: Sentiment Analysismentioning

confidence: 99%

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Rule-based Sentiment Degree Measurement of Opinion Mining of Community Participatory in the Government of Surabaya

Putra

Helen

Barakbah

2018

emitter

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Diskominfo Surabaya, as a government agency, received much community participatory for improvement of governmental services, with increasing number of 698, 2717, 4176 and 4298 participatory data respectively in 2011, 2012, 2013 and 2014. It is challenging for Diskominfo Surabaya to set a target by giving the response back within 24 hours. Due to task complexity to address the degree of participatory and to categorize the group of participatory, they faced difficulty to fulfill the target. In this research, we present a new system for measuring the sentiment degree of community participatory. We provide 5 functions in our system, which are: (1) Data Collection, (2) Data Preprocessing, (3) Text Mining, (4) Sentiment Analysis and (5) Validation. We propose our rule-based technique for the sentiment analysis of opinion mining with detection of 8 important parts, which are (1) Verb, (2) Adjective, (3) Preposition, (4) Noun, (5) Adverb, (6) Symbol, (7) Phrase, and (8) Complimentary. For applicability of our proposed system, we made a series of experiment with 410 data of community participatory in Twitter for Diskominfo Surabaya and compared with other sentiment classification algorithms which are SVM and Naive Bayes Classifier. Our system performed 77.32% rate of accuracy and outperformed to other comparing algorithms.

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Sentiment Analysis of Local College in the Philippines using Facebook Posts towards Good Governance: A Framework Proposal

Almonte

Malizon²,

Montano

et al. 2021

2021 4th International Conference on Information and Computer Technologies (ICICT)

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A Framework for Sentiment Analysis Implementation of Indonesian Language Tweet on Twitter

Cited by 4 publications

References 5 publications

Pengaruh Metode Penyeimbangan Kelas Terhadap Tingkat Akurasi Analisis Sentimen pada Tweets Berbahasa Indonesia

Pengaruh Metode Penyeimbangan Kelas Terhadap Tingkat Akurasi Analisis Sentimen pada Tweets Berbahasa Indonesia

Rule-based Sentiment Degree Measurement of Opinion Mining of Community Participatory in the Government of Surabaya

Sentiment Analysis of Local College in the Philippines using Facebook Posts towards Good Governance: A Framework Proposal

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