Implementation Naïve Bayes Classification for Sentiment Analysis on Internet Movie Database

Samsir, Samsir; Kusmanto, Kusmanto; Dalimunthe, Abdul Hakim; Aditiya, Rahmad; Watrianthos, Ronal

doi:10.47065/bits.v4i1.1468

Cited by 9 publications

(5 citation statements)

References 12 publications

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“…Common simple machine learning models used for Chinese text classification are SVM [24][25][26], K-nearest Neighbor [27], Random Forest [28], and Naïve Bayes [29]. To adapt to Chinese texts, many improved simple machine learning models have emerged to improve the classification effect of Chinese texts.…”

Section: Related Workmentioning

confidence: 99%

Research on performance variations of classifiers with the influence of pre-processing methods for Chinese short text classification

Zhang,

Li,

Xie

et al. 2023

PLoS ONE

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Text pre-processing is an important component of a Chinese text classification. At present, however, most of the studies on this topic focus on exploring the influence of preprocessing methods on a few text classification algorithms using English text. In this paper we experimentally compared fifteen commonly used classifiers on two Chinese datasets using three widely used Chinese preprocessing methods that include word segmentation, Chinese specific stop word removal, and Chinese specific symbol removal. We then explored the influence of the preprocessing methods on the final classifications according to various conditions such as classification evaluation, combination style, and classifier selection. Finally, we conducted a battery of various additional experiments, and found that most of the classifiers improved in performance after proper preprocessing was applied. Our general conclusion is that the systematic use of preprocessing methods can have a positive impact on the classification of Chinese short text, using classification evaluation such as macro-F1, combination of preprocessing methods such as word segmentation, Chinese specific stop word and symbol removal, and classifier selection such as machine and deep learning models. We find that the best macro-f1s for categorizing text for the two datasets are 92.13% and 91.99%, which represent improvements of 0.3% and 2%, respectively over the compared baselines.

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Section: Related Workmentioning

confidence: 99%

Research on performance variations of classifiers with the influence of pre-processing methods for Chinese short text classification

Zhang,

Li,

Xie

et al. 2023

PLoS ONE

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show abstract

“…Numerous languages, including English, have investigated sentiment analysis thoroughly in [3][4][5][6][7][8][9]. Despite Arabic being the fourth most used language online, there has not been much research on the usage of sentiment analysis in it [1].…”

Section: Arabic Sentiment Analysismentioning

confidence: 99%

“…SVM is a form of supervised learning model that provides a mapping between inputs and classifications by constructing a decision function that takes in an input and outputs a probability indicating the likelihood that the input belongs to a particular class [4]. The SVM hyperplane is a line that separates the data into two categories.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

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Arabic Sentiment Analysis of YouTube Comments: NLP-Based Machine Learning Approaches for Content Evaluation

Musleh,

Alkhwaja,

Alkhwaja

et al. 2023

BDCC

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YouTube is a popular video-sharing platform that offers a diverse range of content. Assessing the quality of a video without watching it poses a significant challenge, especially considering the recent removal of the dislike count feature on YouTube. Although comments have the potential to provide insights into video content quality, navigating through the comments section can be time-consuming and overwhelming work for both content creators and viewers. This paper proposes an NLP-based model to classify Arabic comments as positive or negative. It was trained on a novel dataset of 4212 labeled comments, with a Kappa score of 0.818. The model uses six classifiers: SVM, Naïve Bayes, Logistic Regression, KNN, Decision Tree, and Random Forest. It achieved 94.62% accuracy and an MCC score of 91.46% with NB. Precision, Recall, and F1-measure for NB were 94.64%, 94.64%, and 94.62%, respectively. The Decision Tree had a suboptimal performance with 84.10% accuracy and an MCC score of 69.64% without TF-IDF. This study provides valuable insights for content creators to improve their content and audience engagement by analyzing viewers’ sentiments toward the videos. Furthermore, it bridges a literature gap by offering a comprehensive approach to Arabic sentiment analysis, which is currently limited in the field.

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“…Local wisdom serves as a guide for practicing noble values and maintaining a balanced and harmonious life. Introducing local wisdom at an early age helps children connect with their cultural roots, fostering a sense of pride and love for their heritage (Samsir et al, 2022). Therefore, education based on local wisdom is appropriate for preserving and passing on these values to future generations (Prahesti & Fauziah, 2021).…”

Section: Introductionmentioning

confidence: 99%

Cultivating Patriotism: Independent Curriculum and Strengthening Pancasila Profile (P5) in Kindergarten

Citra,

Syaodih,

Rachmawati

et al. 2023

Indonesian Journal of Early Childhood Educational Research

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This research aims to understand the presence of the independent curriculum in early childhood education with a specific focus on the correlation between this curriculum and the locally infused P5 program. The study formulates research objectives centered on planning P5 programs themed around local wisdom, implementing these programs, and evaluating their effectiveness. Employing a qualitative approach with a descriptive method, data collection methods include observation, interviews, and documentation. The research findings emphasize the essential role of the P5 program within the independent curriculum. This program successfully enriches students' knowledge and understanding of local wisdom, suggesting a need for further integration of local wisdom elements into the independent curriculum at the early childhood education level. It is crucial to enhance parental involvement in the implementation of the P5 program. Additionally, developing more holistic evaluation strategies is recommended to measure the long-term impact of the independent curriculum and the P5 program on the overall development of students.

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Implementation Naïve Bayes Classification for Sentiment Analysis on Internet Movie Database

Cited by 9 publications

References 12 publications

Research on performance variations of classifiers with the influence of pre-processing methods for Chinese short text classification

Research on performance variations of classifiers with the influence of pre-processing methods for Chinese short text classification

Arabic Sentiment Analysis of YouTube Comments: NLP-Based Machine Learning Approaches for Content Evaluation

Cultivating Patriotism: Independent Curriculum and Strengthening Pancasila Profile (P5) in Kindergarten

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