Comparison of Naive Bayes smoothing methods for Twitter sentiment analysis

Ramadhani, Rif'at Ahdi; Indriani, Fatma; Nugrahadi, Dodon Turianto

doi:10.1109/icacsis.2016.7872720

Cited by 19 publications

(9 citation statements)

References 8 publications

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“…The 1000 labelled target tweets (containing specific sentiment expressions for interdisciplinarity, multidisciplinarity and transdisciplinarity) were added to training data to subsequently build the classification model. Additionally, to mitigate some risk of misclassification due to the absence of sample features in the training data, we used Laplace smoothing [50]—for the algorithms that allow smoothing. All pre-processing steps described in §2.4 were similarly applied on the training tweets and the target tweets so that the learned features of the training data could be correctly applied to the target tweets.…”

Section: Methodsmentioning

confidence: 99%

Interdisciplinary optimism? Sentiment analysis of Twitter data

Weber

Syed

2019

R. Soc. open sci.

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Interdisciplinary research has faced many challenges, including institutional, cultural and practical ones, while it has also been reported as a ‘career risk’ and even ‘career suicide’ for researchers pursuing such an education and approach. Yet, the propagation of challenges and risks can easily lead to a feeling of anxiety and disempowerment in researchers, which we think is counterproductive to improving interdisciplinarity in practice. Therefore, in the search of ‘bright spots’, which are examples of cases in which people have had positive experiences with interdisciplinarity, this study assesses the perceptions of researchers on interdisciplinarity on the social media platform Twitter. The results of this study show researchers’ many positive experiences and successes of interdisciplinarity, and, as such, document examples of bright spots. These bright spots can give reason for optimistic thinking, which can potentially have many benefits for researchers’ well-being, creativity and innovation, and may also inspire and empower researchers to strive for and pursue interdisciplinarity in the future.

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Section: Methodsmentioning

confidence: 99%

Interdisciplinary optimism? Sentiment analysis of Twitter data

Weber

Syed

2019

R. Soc. open sci.

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“…For investigating performance of classification, the number of selected features is varied from 10% to 90%. Then they are investigated to classify opinion orientation by using Naïve Bayes that is a simple classifier and effective in opinion classification [18][19][20][21][22][23][24][25]. 10-fold cross-validation is used to divide dataset the experiments.…”

Section: Experimental Evaluations 41 Experimental Setupmentioning

confidence: 99%

Opinion classification on a social network by a novel feature selection technique

Choompol

Songram

Chomphuwiset

2020

IJEECS

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Most of the opinion comments on social networks are short and ambiguous. In general, opinion classification on the comments is difficult because of lacking dominant features. A feature extraction technique is therefore necessary for improving accuracy of the classification and computational time. This paper proposes an effective feature selection method for opinion classification on a social network. The proposed method selects features based on the concept of a filter model, together with association rules. Support and confidence are used to calculate the weights of features. The features with high weight are selected for classification. Unlike supports in association rules, supports in our method are normalized to 0-1 to remove outlier supports. Moreover, a tuning parameter is used to emphasize the degree of support or confidence. The experimental results show that the proposed method provides high classification efficiency. The proposed method outperforms Information Gain, Chi-Square, and Gini Index in both computational time and accuracy.

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“…Metode smoothing merupakan metode untuk menghindari hasil klasifikasi bernilai 0 dikarenakan data testing tidak ditemukan pada data training. Pada penelitian ini digunakan metode laplace smoothing, yaitu metode smoothing yang paling sederhana karena hanya menambahkan angka 1, tapi metode laplace smoothing memiliki perfomance yang cukup baik dibandingkan dengan metode smoothing lainnya [8], pada penerapan text atau numerik. Salah satunya pada perbandingan smoothing untuk pengkategorian soal ujian [2], sebagai penerapan pada data text (text mining).…”

Section: Peminatan Siswaunclassified

Implementasi Naïve Bayessian dengan Laplacian Smoothing untuk Peminatan dan Lintas Minat Siswa SMAN 5 Pamekasan

Listiowarni

2019

SISFOKOM

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Kurikulum 2013 memiliki beberapa perubahan dasar dari kurikulum sebelumnya, salah satunya adalah penyaluran dan penempatan siswa pada program peminatan. Setelah dilakukan klasifikasi peminatan, siswa akan diklasifikasikan lagi menggunakan nilai tes, yang disebut sebagai Lintas Minat Siswa. Penelitian ini berkonsentrasi untuk menerapkan metode Naive Bayessian pada sebuah sistem untuk menanggulangi permasalahan rumitnya proses klasifikasi dua tingkatan dan banyaknya data setiap tahunnya.Naive Bayes merupakan salah satu metode machine learning yang menggunakan perhitungan probabilitas, dan memggunakan laplacian smoothing untuk menghindari hasil akhir bernilai 0. Nilai perhitungan accuracy dan error rate pada 720 data training dengan pengambilan 5 kali jumlah data testing yang berbeda menggunakan naive bayessian dan laplacian smoothing, didapat nilai accuracy : 92,11% dan nilai error rate : 7,02%

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Comparison of Naive Bayes smoothing methods for Twitter sentiment analysis

Cited by 19 publications

References 8 publications

Interdisciplinary optimism? Sentiment analysis of Twitter data

Interdisciplinary optimism? Sentiment analysis of Twitter data

Opinion classification on a social network by a novel feature selection technique

Implementasi Naïve Bayessian dengan Laplacian Smoothing untuk Peminatan dan Lintas Minat Siswa SMAN 5 Pamekasan

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