Study of Sentiment of Governor's Election Opinion in 2018

Saputro, Agung Eddy Suryo; Notodiputro, Khairil Anwar; Indahwati, A

doi:10.32628/ijsrset21841124

Cited by 8 publications

(2 citation statements)

References 9 publications

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“…In the multi-class classification, the performance calculation is different from the binary calculation, in the multi-class classification performance calculation, the true positive and true negative locations will be explained in the following table 1. Based on table 1, we can measure accuracy, precision and recall, as shown in Equation ( 6) to Equation (12) [24].…”

Section: Confusion Matrix For Multi-class Classificationmentioning

confidence: 99%

Understanding People Opinion on Artificial Intelligence Ethics through Machine Learning-based Sentiment Analysis

Saputra,

Arwin Datumaya Wahyudi Sumari,

Vian Satria Maulana Navalino

et al. 2023

IJFTE

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Artificial Intelligence (AI) ethics is so near to human existence. It contains a collection of beliefs, concepts, and methods that utilize generally recognized moral standards to govern moral behavior in developing and using AI technology. Besides providing many advantages to people, the development and use of AI also pose risks that may harm the humankind. At the crossroads between the need for AI and the risks, we want to know what people think about AI. For that purpose, we devised a sentiment analysis system powered by Naive Bayes Classifier and Term Frequency-Inverse Document Frequency (TF-IDF) methods. After analyzing 1.138 data taken from YouTube and Twitter which it is categorized into three opinion labels, namely positive, neutral, and negative, the system can achieve accuracy of 71% with average precision of 66%, average recall of 71%, and average F1-Score of 64% through K-Fold Cross-Validation.

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Section: Confusion Matrix For Multi-class Classificationmentioning

confidence: 99%

Understanding People Opinion on Artificial Intelligence Ethics through Machine Learning-based Sentiment Analysis

Saputra,

Arwin Datumaya Wahyudi Sumari,

Vian Satria Maulana Navalino

et al. 2023

IJFTE

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“…The confusion matrix is a tabular representation that provides a detailed breakdown of a model's predictions. It displays the true positive, true negative, false positive, and false negative values for each class, allowing for an evaluation of the sentiment classification performance [30]. It helps to evaluate the correctness of classification approaches in multiclass classification problems.…”

Section: ) Model Performance Evaluationmentioning

confidence: 99%

Hybrid Approach with VADER and Multinomial Logistic Regression for Multiclass Sentiment Analysis in Online Customer Review

Arief,

Samsudin

2023

IJACSA

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Sentiment analysis is crucial for businesses to understand customer reviews and assess sentiment polarity. A hybrid technique combining VADER and Multinomial LogisticRegression was used to analyze customer sentiment in online customer review data. VADER is a lexicon-based approach that labels reviews with sentiment using a predefined lexicon, whereas Multinomial Logistic Regression can determine the polarity of sentiment using VADER data. This study employed multiclass classification using TF-IDF vectorization to categorize sentiment as a positive, negative, or neutral class. Correctly managing neutral sentiments can assist businesses in identifying improvement opportunities. The utilization of the VADER lexicon and Multinomial Logistic Regression has been shown to significantly improve the performance of sentiment analysis in the context of multiclass classification problems. With a 75.213% accuracy rate, the VADER lexicon accurately recognizes neutral sentiment and is appropriate to adapt in categorizing sentiment related to customer reviews. Combined with Multinomial Logistic Regression, accuracy increases to 92.778%. In conclusion, the hybrid approach with VADER and Multinomial Logistic Regression can leverage the accuracy and reliability of multiclass customer sentiment analysis.

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Sentiments analysis of fMRI using automatically generated stimuli labels under naturalistic paradigm

Mahrukh

Shakil

Malik

2023

Sci Rep

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Our emotions and sentiments are influenced by naturalistic stimuli such as the movies we watch and the songs we listen to, accompanied by changes in our brain activation. Comprehension of these brain-activation dynamics can assist in identification of any associated neurological condition such as stress and depression, leading towards making informed decision about suitable stimuli. A large number of open-access functional magnetic resonance imaging (fMRI) datasets collected under naturalistic conditions can be used for classification/prediction studies. However, these datasets do not provide emotion/sentiment labels, which limits their use in supervised learning studies. Manual labeling by subjects can generate these labels, however, this method is subjective and biased. In this study, we are proposing another approach of generating automatic labels from the naturalistic stimulus itself. We are using sentiment analyzers (VADER, TextBlob, and Flair) from natural language processing to generate labels using movie subtitles. Subtitles generated labels are used as the class labels for positive, negative, and neutral sentiments for classification of brain fMRI images. Support vector machine, random forest, decision tree, and deep neural network classifiers are used. We are getting reasonably good classification accuracy (42–84%) for imbalanced data, which is increased (55–99%) for balanced data.

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Study of Sentiment of Governor's Election Opinion in 2018

Cited by 8 publications

References 9 publications

Understanding People Opinion on Artificial Intelligence Ethics through Machine Learning-based Sentiment Analysis

Understanding People Opinion on Artificial Intelligence Ethics through Machine Learning-based Sentiment Analysis

Hybrid Approach with VADER and Multinomial Logistic Regression for Multiclass Sentiment Analysis in Online Customer Review

Sentiments analysis of fMRI using automatically generated stimuli labels under naturalistic paradigm

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