Sentiment Analysis System of Indonesian Tweets using Lexicon and Naïve Bayes Approach

Ahmad, Mubarik; Octaviansyah, Mochamad Ferdy; Kardiana, Aan; Prasetyo, Kukuh Fadli

doi:10.1109/icic47613.2019.8985930

Cited by 5 publications

(3 citation statements)

References 6 publications

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“…Lexicon-based dapat menghitung polaritas suatu tweet dengan rumus sederhana. Metode ini dapat mengategorikan sebuah kalimat berdasarkan sifatnya yaitu positif, netral, atau negatif (Ahmad et al, 2019).…”

Section: Lexicon-basedunclassified

Klasifikasi Lexicon-Based Sentiment Analysis Tragedi Kanjuruhan pada Twitter Menggunakan Algoritma Convolutional Neural Network

Arif Widiasan Subagio,

Anggraini Puspita Sari,

Andreas Nugroho Sihananto

2024

JUISIK

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This study aims to conduct a sentiment analysis of conversations on social media Twitter related to the Kanjuruhan Tragedy. Social media, especially Twitter, has become a significant platform for Indonesians to share their thoughts and feelings regarding this tragic event. We used two approaches for sentiment analysis, namely Lexicon-based and Convolutional Neural Network (CNN), with a focus on classifying sentiments in positive, negative, and neutral categories. This study also involves references to several previous studies that implemented various sentiment analysis methods. It is hoped that the results of this study can provide deep insight into the responses and feelings of the public on social media related to the Kanjuruhan Tragedy. The lexicon-based sentiment analysis classification of the Kanjuruhan Tragedy on twitter social media using the CNN algorithm successfully analyzed the sentiment results of tweets related to the tragedy where most of the tweets obtained had negative sentiments with test results of precision value 87.74%, recall 87.51%, and f1-score 87.27% with a classification accuracy of 87.27% and took 3 minutes 23 seconds of training time.

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Section: Lexicon-basedunclassified

Klasifikasi Lexicon-Based Sentiment Analysis Tragedi Kanjuruhan pada Twitter Menggunakan Algoritma Convolutional Neural Network

Arif Widiasan Subagio,

Anggraini Puspita Sari,

Andreas Nugroho Sihananto

2024

JUISIK

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“…In lexicon based or dictionary based classification technique, lexicon score of tweets or a piece of writing is calculated by using the sentiment dictionary [35]. In this approach, the polarity score of each word in BOG is calculated by using already present polarity dictionaries like WorldNet and SentiWordNet.…”

Section: Lexicon Based Classification Techniquementioning

confidence: 99%

Analyzing impact of number of features on efficiency of hybrid model of lexicon and stack based ensemble classifier for twitter sentiment analysis using WEKA tool

Rani¹,

Gill²,

Gulia³

2021

IJEECS

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<span>Twitter is used by millions of people across the world, so the data collected from Twitter can be highly valuable for research and helpful in decision support. Here in this paper ‘Twitter US Airline data’ from Kaggle data repository is used for sentiment classification of customers’ reviews. The current research aims to implement various machine learning classifiers, Stack-based ensemble classifiers and hybrid of lexicon classifier with other classifiers. 11 different classification models are implemented for different sized feature sets. Also, all the 11 models are re-implemented by adding sentiment score of lexicon based classifier as one of the features in the feature set. Results are analyzed by varying number of input feature variables used in the classification. Four different size feature sets having 301,501, 701, and 1301 number of features are used to analyze the variations in the final findings. Chi-Square and Information gain techniques are used for feature selection. The results show that an increase in the number of features increases the accuracy up to 701 features. After that, accuracy is stable or decreases with increase in feature set size. Also, the cost of adding sentiment score of lexicon classifier to the input feature set is nominal, but the results are improved consistently. WEKA and R Studio tools are used for analysis and implementation. Accuracy and Kappa are used for representing and comparing the efficiency of models.</span>

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“…Although NB performance is not as good as other machine learning classifiers, but NB is implemented in various ensemble techniques due to easy and fast implementation. M. Ahmad et al [37] implemented NB and lexicon based classifier and show that NB performed better with accuracy 84 % as compared to lexicon classifier with accuracy 72%. N. Ardhianie et al [38] also implemented NB on "Indonesian No Dating Campaign" and results in an accuracy of 74.77%.…”

Section: B Nb (Naïve Bayes)mentioning

confidence: 99%

Hybrid Model using Stack-Based Ensemble Classifier and Dictionary Classifier to Improve Classification Accuracy of Twitter Sentiment Analysis

Rani¹

2020

IJETER

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Ensemble classifiers are widely used for the enhancement of accuracy of twitter sentiment classification. In the present research, a hybrid model based on stack based ensemble classifiers and dictionary based classifier is used for tweet classification as positive and negative. To enhance accuracy of classification, sentiment score retrieved from dictionary based classifier is added to the feature vector to get enhanced feature set and the hybrid stack based ensemble model is implemented on this enhanced feature set. Three machine learning classifiers svmRadial, C5.0, NB are used to build stacked based ensemble classifier using GLM and RF as Meta learners. Three data sets viz. Kaggle -US Airline Twitter Sentiment Data Set, Sentiment 140 Twitter Data Set, and Real time manually labeled data set related to 'Clean India Mission' are used for the implementation of the proposed model. Caret library of R Studio is used for creating the stack based ensemble of classifiers. The results show that the proposed hybrid model that used sentiment score as one of the features in feature set performed better with an accuracy of 0.8742223 for Kaggle -US Airline Twitter Sentiment Data Set, 0.8881453 for data set related to 'Clean India Mission' and 0.9953593 for Sentiment 140 Twitter Data Set, as compared to machine learning classifiers and other ensemble classifiers.

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Sentiment Analysis System of Indonesian Tweets using Lexicon and Naïve Bayes Approach

Cited by 5 publications

References 6 publications

Klasifikasi Lexicon-Based Sentiment Analysis Tragedi Kanjuruhan pada Twitter Menggunakan Algoritma Convolutional Neural Network

Klasifikasi Lexicon-Based Sentiment Analysis Tragedi Kanjuruhan pada Twitter Menggunakan Algoritma Convolutional Neural Network

Analyzing impact of number of features on efficiency of hybrid model of lexicon and stack based ensemble classifier for twitter sentiment analysis using WEKA tool

Hybrid Model using Stack-Based Ensemble Classifier and Dictionary Classifier to Improve Classification Accuracy of Twitter Sentiment Analysis

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