Analysis on Opinion Mining Using Combining Lexicon-Based Method and Multinomial Naïve Bayes

Isabelle, Geriska; Maharani, Warih; Asror, Ibnu

doi:10.2991/icoiese-18.2019.38

Cited by 9 publications

(7 citation statements)

References 4 publications

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“…The lexicon-based approach uses a dictionary that includes word polarity [8]. The dictionary, also known as sentiment lexicon, is a compilation of sentiment terms.…”

Section: B Lexicon-based Approachmentioning

confidence: 99%

Political Security Threat Prediction Framework Using Hybrid Lexicon-Based Approach and Machine Learning Technique

et al. 2023

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The internet offers a powerful medium for expressing opinions, emotions and ideas, using online platforms supported by smartphone usage and high internet penetration. Most internet posts are textual based and can include people's emotional feelings for a particular moment or sentiment. Monitoring online sentiments or opinions is important for detecting any excessive emotions triggered by citizens which can lead to unintended consequences and threats to national security. Riots and civil war, for instance, must be addressed due to the risk of jeopardizing social stability and political security, which are crucial elements of national security. Mining opinions according to the national security domain is a relevant research topic that must be enhanced. Mechanisms and techniques that can mine opinions in the aspect of political security require significant improvements to obtain optimum results. Researchers have noted that there is a strong relationship between emotion, sentiment and political security threats. This study proposes a new theoretical framework for predicting political security threats using a hybrid technique: the combination of lexicon-based approach and machine learning in cyberspace. In the proposed framework, Decision Tree, Naive Bayes, and Support Vector Machine have been deployed as threat classifiers. To validate our proposed framework, an experimental analysis is accomplished. The performance of each technique used in the experiments is reported. In this study, our proposed framework reveals that the hybrid Lexicon-based approach with the Decision Tree classifier recorded the highest performance score for predicting political security threats. These findings offer valuable insight to ongoing research on opinion mining in predicting threats based on the political security domain.

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“…The lexicon-based approach uses a dictionary that includes word polarity [8]. The dictionary, also known as sentiment lexicon, is a compilation of sentiment terms.…”

Section: B Lexicon-based Approachmentioning

confidence: 99%

Political Security Threat Prediction Framework Using Hybrid Lexicon-Based Approach and Machine Learning Technique

et al. 2023

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“…Seperti metode supervised learning lainnya proses multinomial naïve bayes dibagi menjadi dua yaitu pelatihan dan validasi. Untuk pelatihan, probabilitas setiap kata dalam kelas dihitung dengan menggunakan rumus berikut [12]:…”

Section: Multinomial Naïve Bayesunclassified

Analisis Sentimen Opini Pengguna Twitter Pada Aplikasi Bibit Menggunakan Multinomial Naïve Bayes

Ngilo

Nuryuliani

2023

JTS

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The COVID-19 pandemic has increased the interest number of capital market investors in Indonesia. One of the factor in Indonesian’s investment interest is the emergence of fintech in the investment sector. One of the fintech companies in mutual fund investment is "Bibit". To find out user opinions on the Bibit application, a sentiment analysis was carried out on Twitter’s users. This study aims to analyze the sentiments of twitter users' opinions on the Bibit application using a combination of Lexicon-Based and Multinomial Naïve Bayes methods. The training data used were 2211 tweets and the validation data was 553 tweets. In the model training process, the training accuracy level is 91.50% and the validation accuracy rate is 85.35%. Model testing was carried out using 39 new tweet data and obtained an accuracy rate of 88%. Sentiment analysis using this method is visualized in the form of pie charts, graphs, and wordclouds. Based on the results of visualization of Twitter social media user sentiment towards the seedling application, it tends to be positive with a percentage of 52% positive and 48% negative.

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“…NBM relies on a probabilistic method with separated training and testing processes [61]. For the training process, suppose t = t i represents the flash flood and non-flash flood classes and c = c i (i = 1n) is defined as flash flood conditioning factors (n is the number of the factors used).…”

Section: Multinomial Naïve Bayes (Nbm)mentioning

confidence: 99%

A Comparative Study of Kernel Logistic Regression, Radial Basis Function Classifier, Multinomial Naïve Bayes, and Logistic Model Tree for Flash Flood Susceptibility Mapping

Pham

Phong

Nguyen

et al. 2020

Water

105

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Risk of flash floods is currently an important problem in many parts of Vietnam. In this study, we used four machine-learning methods, namely Kernel Logistic Regression (KLR), Radial Basis Function Classifier (RBFC), Multinomial Naïve Bayes (NBM), and Logistic Model Tree (LMT) to generate flash flood susceptibility maps at the minor part of Nghe An province of the Center region (Vietnam) where recurrent flood problems are being experienced. Performance of these four methods was evaluated to select the best method for flash flood susceptibility mapping. In the model studies, ten flash flood conditioning factors, namely soil, slope, curvature, river density, flow direction, distance from rivers, elevation, aspect, land use, and geology, were chosen based on topography and geo-environmental conditions of the site. For the validation of models, the area under Receiver Operating Characteristic (ROC), Area Under Curve (AUC), and various statistical indices were used. The results indicated that performance of all the models is good for generating flash flood susceptibility maps (AUC = 0.983–0.988). However, performance of LMT model is the best among the four methods (LMT: AUC = 0.988; KLR: AUC = 0.985; RBFC: AUC = 0.984; and NBM: AUC = 0.983). The present study would be useful for the construction of accurate flash flood susceptibility maps with the objectives of identifying flood-susceptible areas/zones for proper flash flood risk management.

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Analysis on Opinion Mining Using Combining Lexicon-Based Method and Multinomial Naïve Bayes

Cited by 9 publications

References 4 publications

Political Security Threat Prediction Framework Using Hybrid Lexicon-Based Approach and Machine Learning Technique

Political Security Threat Prediction Framework Using Hybrid Lexicon-Based Approach and Machine Learning Technique

Analisis Sentimen Opini Pengguna Twitter Pada Aplikasi Bibit Menggunakan Multinomial Naïve Bayes

A Comparative Study of Kernel Logistic Regression, Radial Basis Function Classifier, Multinomial Naïve Bayes, and Logistic Model Tree for Flash Flood Susceptibility Mapping

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