Detecting Emotion in Indonesian Tweets: A Term-Weighting Scheme Study

Nugroho, Kuncahyo Setyo; Bachtiar, Fitra A.; Mahmudy, Wayan Firdaus

doi:10.20473/jisebi.8.1.61-70

Cited by 4 publications

(5 citation statements)

References 33 publications

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“…Previous studies also used KNN to analyze 5 emotion labels, namely anger, happiness, sadness, love, and fear, using TF-IDF. The accuracy results achieved were still below the accuracy results obtained in this study, which is 0.51% (Nugroho et al, 2022). Furthermore, this study also conducted sentiment analysis on emotions, but it calculated the accuracy for each label separately, resulting in accuracy above 90% (Kaur & Bhardwaj, 2019).…”

Section: Results and Discussion Knn 6 Label Using Tf-idfmentioning

confidence: 61%

Classification of Multiple Emotions in Indonesian Text Using The K-Nearest Neighbor Method

Zamsuri

Defit

Nurcahyo

2023

JAETS

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Emotions are expressions manifested by individuals in response to what they see or experience. In this study, emotions were examined through individuals' tweets regarding the election issues in Indonesia in 2024. The collected tweets were then labeled based on emotions using the emotion wheel, which consisted of six categories: joy, love, surprise, anger, fear, and sadness. After the labeling process, the next step involved weighting using TF-IDF (Term Frequency-Inverse Document Frequency) and Bag-of-Words (BoW) techniques. Subsequently, the model was evaluated using the K-Nearest Neighbor (KNN) algorithm with three different data splitting ratios: 80:20, 70:30, and 60:40. From the six labels used in the modeling process, the accuracy was then calculated, and the labels were subsequently merged into positive and negative categories. Then the modeling was conducted using the same process with the six labels. The results of this study revealed that the utilization of TF-IDF outperformed BoW. The highest accuracy was achieved with the 80:20 data splitting ratio, attaining 58% accuracy for the six-label classification and 79% accuracy for the two-label classification

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Section: Results and Discussion Knn 6 Label Using Tf-idfmentioning

confidence: 61%

Classification of Multiple Emotions in Indonesian Text Using The K-Nearest Neighbor Method

Zamsuri

Defit

Nurcahyo

2023

JAETS

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“…Other studies examining machine learning models with 6 labels or more also achieved accuracies below 70%. In a prior study [73], accuracies of 50% for KNN with TF-IDF, 66% for SVM with TF-IDF, and 55% for Decision Tree with TF-ICF were reported. Another research [74] found accuracies of 61% for naï ve Bayes, logistic regression, SVM, 64% for Decision Tree, and 57% for Adaboost.…”

Section: Discussionmentioning

confidence: 98%

Development and Comparison of Multiple Emotion Classification Models in Indonesia Text Using Machine Learning

Zamsuri,

Defit,

Nurcahyo

2024

JAIT

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Emotion is an individual's response to an event or situation. Research related to emotions in the field of data science falls under sentiment analysis, where sentiment analysis mostly focuses on determining the positive or negative emotional tone. This study attempts to classify emotions into labels such as Happy, Love, Surprise, Anger, Fear, and Sadness. Then, these six emotion labels are further categorized into positive and negative groups. The dataset for this research comprises tweets from Twitter related to the 2024 presidential election in Indonesia. Several machine learning algorithms are employed in this study, including Naï ve Bayes (Multinomial Bayes, Bernoulli Bayes, Complement Bayes), K-Nearest Neighbors (KNN), and Support Vector Machines (SVM), comparing two feature extraction methods: Term Frequency-Inverse Document Frequency (TF-IDF) and Bag of Words (BoW). The resultsshow that the use of SVM does not yield better accuracy, whether using TF-IDF or BoW. Therefore, this study improves the accuracy of the SVM algorithm by combining kernels available in SVM (Polynomial, Radial Basis Function (RBF), and Linear). From the conducted experiments, it is evident that SVM with a combined kernel, referred to as SVM Polynomial, RBF, Linier (PoRLi) in this study, achieves better accuracy compared to a single kernel. This is reflected in the classification accuracy with 6 labels, reaching 62%, indicating a 2% increase from SVM Linear, which obtained the highest accuracy using a single kernel. Furthermore, in the classification of 2 labels, there is also a 1% increase when using SVM PoRLi. It can be concluded that SVM PoRLi can enhance accuracy across various labels.

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“…A more extensive, less common feature space is an n-gram. A larger n increases information and computational expense [21]. In this research, we combine the unigram, the bigram, and the trigram forms of the n-gram.…”

Section: N-grammentioning

confidence: 99%

“…IDF gives greater weight to terms that appear more frequently in the document, regardless of whether those words are used often or infrequently [22] [23]. TF-IDF is now the most popular text classification and document categorization scheme [24] [21].…”

Section: E Term Frequency -Inverse Document Frequency (Tf-idf)mentioning

confidence: 99%

Public Response to the Legalization of The Criminal Code Bill with Twitter Data Sentiment Analysis

Irawan¹,

Sensuse²,

Putro³

et al. 2023

IJACSA

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The Criminal Code Bill, also known as Rancangan Kitab Undang-undang Hukum Pidana (RKUHP), passed in the House of Representatives (DPR) on December 6, 2022, is being debated because several issues need to be fixed. Therefore, research was conducted to determine the public's reaction to the ratification of the Criminal Code Bill by analyzing Twitter data. This study aims to obtain a general response to the legalized RKUHP. We use sentiment analysis, a text-processing method, to get data from the public. To do this, we used N-grams (unigrams, bigrams, and trigrams) along with three algorithms: Naïve Bayes, Classification and Regression Tree (CART), and Support Vector Machine (SVM). The result of sentiment analysis found that 51% of tweets were positive about the ratification of the RKUHP, and 49% were negative. In addition, it was also found that SVM has the best accuracy compared to other algorithms, with an accuracy value of 0.81 on the unigram combination.

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Detecting Emotion in Indonesian Tweets: A Term-Weighting Scheme Study

Cited by 4 publications

References 33 publications

Classification of Multiple Emotions in Indonesian Text Using The K-Nearest Neighbor Method

Classification of Multiple Emotions in Indonesian Text Using The K-Nearest Neighbor Method

Development and Comparison of Multiple Emotion Classification Models in Indonesia Text Using Machine Learning

Public Response to the Legalization of The Criminal Code Bill with Twitter Data Sentiment Analysis

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