Obtaining Value From The Constraints in Finding User Habitual Words

Lubis, Arif Ridho; Nasution, Mahyuddin K. M.; Sitompul, Opim Salim; Zamzami, Elviawaty Muisa

doi:10.1109/icadeis49811.2020.9277443

Cited by 9 publications

(8 citation statements)

References 13 publications

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“…It is difficult to identify whether such platforms are profitable or detrimental to social media users, even though people around the world spend a large part of their days on social media platforms. This is related to the research conducted by Lubis et al [18] finding a framework for social media users, both the disclosure of words on social media is the keyword as the habit of social media users with initialized steps by reviewing current postings in order to have good data that is more particular and precise than those obtained from netizens [19]. These exact keywords can then be used by the social media system to detect the profile of a certain user in the online domain in which a search engine can access.…”

Section: Introductionmentioning

confidence: 85%

The feature extraction for classifying words on social media with the Naïve Bayes algorithm

Lubis

Nasution

Sitompul

et al. 2022

IJ-AI

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To classify Naïve Bayes classification (NBC), however, it is necessary to have a previous pre-processing and feature extraction. Generally, pre-processing eliminates unnecessary words while feature extraction processes these words. This paper focuses on feature extraction in which calculations and searches are used by applying word2vec while in frequency using term frequency-Inverse document frequency (TF-IDF). The process of classifying words on Twitter with 1734 tweets which are defined as a document to weight the calculation of frequency with TF-IDF with words that often come out in tweet, the value of TF-IDF decreases and vice versa. Following the achievement of the weight value of the word in the tweet, the classification is carried out using Naïve Bayes with 1734 test data, yielding an accuracy of 88.8% in the Slack word category tweet and while in the tweet category of verb 78.79%. It can be concluded that the data in the form of words available on twitter can be classified and those that refer to slack words and verbs with a fairly good level of accuracy. so that it manifests from the habit of twitter social media user.

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Section: Introductionmentioning

confidence: 85%

The feature extraction for classifying words on social media with the Naïve Bayes algorithm

Lubis

Nasution

Sitompul

et al. 2022

IJ-AI

Self Cite

View full text Add to dashboard Cite

show abstract

“…The total probability theorem should be first comprehended before starting to discuss about Bayes' theorem, theorem [24]. Starting with the regulation to add two events A and B, it is clear form Figure 1 and Figure 2 that the (5). Suppose that the sample space is subdivided in n independent occurrence Di, i=1.n, as seen in Figure 3.…”

Section: Total Probability Theoremmentioning

confidence: 99%

“…Currently, social media produces huge amounts of data every day [3], [4]. This very large data can be used for various purposes and depends on what purpose the data is used for [5]. Consciously or unconsciously, Twitter social media users every time they tweet produce a maximum of 140 characters of letters in it, where each character forms a word and then forms a sentence.…”

Section: Introductionmentioning

confidence: 99%

A new approach to achieve the users’ habitual opportunities on social media

Lubis

Nasution

Sitompul

et al. 2023

IJ-AI

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<span lang="EN-US">The data generated from social media is very large, while the use of data from social media has not been fully utilized to become new knowledge. One of the things that can become new knowledge is user habits on social media. Searching for user habits on Twitter by using user tweets can be done by using modeling, the use of modeling lies when the data has been preprocessed, and the ranking will then be checked in the dictionary, this is where the role of the model is carried out to get a chance that the words that have been ranked will perform check the word in the dictionary. The benefit of the model in general is to get an understanding of the mechanism in the problem so that it can predict events that will arise from a phenomenon which in this case is user habits. So that with the availability of this model, it can be a model in getting opportunities for user habits on Twitter social media.</span>

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“…Text preprocessing is an initial, integral stage in the field of natural language processing that involves performing a series of operations on text data prior to further processing [25,26]. The primary objective of text preprocessing is to cleanse, modify, or reduce text data, thereby enhancing its suitability for more efficient and accurate processing.…”

Section: Text Preprocessingmentioning

confidence: 99%

Improving Spell Checker Performance for Bahasa Indonesia Using Text Preprocessing Techniques with Deep Learning Models

Lubis,

Lase,

Rahman

et al. 2023

ISI

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Spell checking capabilities, crucial within the domain of natural language processing, often encounter limitations in the context of Bahasa Indonesia due to data irregularities and the scarcity of high-quality training data. This study aims to enhance spell checker performance through the implementation of various text preprocessing techniques, including case folding, tokenization, stemming, and the removal of stop words. A Convolutional Neural Network (CNN), a deep learning model, was employed in this research to facilitate the overall process. The study utilized data gathered from social media communities, comprising a total of 10,000 entries. This data was divided into two subsets; 80% (8,000 entries) was allocated for training and the remaining 20% (2,000 entries) was designated for testing. A series of tests were conducted on datasets subject to different preprocessing approaches: without case folding, without stop words removal, without stemming, and with all text preprocessing stages implemented. The evaluation metrics employed in this study included accuracy, recall, precision, and the F1 score. The results demonstrated notable improvements in spell checker performance with appropriate text preprocessing. Specifically, the accuracy reached 0.86 for the dataset without stemming, 0.74 for the dataset without stop words removal, 0.7 for the dataset without case folding, and 0.89 for the dataset where all preprocessing stages were applied. These findings suggest that a comprehensive text preprocessing approach, paired with deep learning models, can significantly enhance spell checker performance for Bahasa Indonesia.

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Obtaining Value From The Constraints in Finding User Habitual Words

Cited by 9 publications

References 13 publications

The feature extraction for classifying words on social media with the Naïve Bayes algorithm

The feature extraction for classifying words on social media with the Naïve Bayes algorithm

A new approach to achieve the users’ habitual opportunities on social media

Improving Spell Checker Performance for Bahasa Indonesia Using Text Preprocessing Techniques with Deep Learning Models

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