Effects of Light Stemming on Feature Extraction and Selection for Arabic Documents Classification

Alhaj, Yousif A.; Al-qaness, Mohammed A. A.; Dahou, Abdelghani; Elaziz, Mohamed Abd; Zhao, Dongdong; Xiang, Jianwen

doi:10.1007/978-3-030-34614-0_4

Cited by 10 publications

(4 citation statements)

References 21 publications

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“…Reviews that have been cleaned undergo a tokenization process, where the text is broken down into its smallest units (tokens) [10]. Stopword removal and stemming techniques are also applied to reduce data complexity and increase feature relevance [11]. Table 2 shows the results of data pre-processing.…”

Section: Figure 1 Research Methodsmentioning

confidence: 99%

Improved playstore review sentiment classification accuracy with stacking ensemble

Santoso,

Munna,

Untari Ningsih

2024

J. Soft Comput. Explor.

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In today's digital era, user reviews on the Playstore platform are an invaluable source of information for developers, offering insights that are critical for service improvement. Previous research has explored the application of stacking ensemble methods, such as in the context of predicting depression among university students, to enhance prediction accuracy. However, these studies often do not explicitly detail the data acquisition process, leaving a gap in understanding the applicability of these methods to different domains. This research aims to bridge this gap by applying the stacking ensemble approach to improve the accuracy of sentiment classification in Playstore reviews, with a clear exposition of the data collection method. Utilizing Logistic Regression as the meta classifier, this methodology is executed in several stages. Initially, data was collected from user reviews of online loan applications on Google Playstore, ensuring transparency in the data acquisition process. The data is then classified using three basic models: Random Forest, Naive Bayes, and SVM. The outputs of these models serve as inputs to the Logistic Regression meta model. A comparison of each base model output with the meta model was subsequently carried out. The test results on the Playstore review dataset demonstrated an increase in accuracy, precision, recall, and F1 score compared to using a single model, achieving an accuracy of 87.05%, which surpasses Random Forest (85.6%), Naive Bayes (85.55%), and SVM (86.5%). This indicates the effectiveness of the stacking ensemble method in providing deeper and more accurate insights into user sentiment, overcoming the limitations of single models and previous research by explicitly addressing data acquisition methods.

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Section: Figure 1 Research Methodsmentioning

confidence: 99%

Improved playstore review sentiment classification accuracy with stacking ensemble

Santoso,

Munna,

Untari Ningsih

2024

J. Soft Comput. Explor.

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“…1-Remove duplicated: This process deletes any duplicated tweets in the dataset. 2-Tokenization: It is the process of splitting up tweets into words called tokens, separated by commas [22]. 3-Normalization: This is the process of transforming a text into a canonical form by removing noises, such as dates, whitespaces, abbreviations, acronyms, and diacritics [23], [24].…”

Section: Methodology and Experimentsmentioning

confidence: 99%

Comparing the Random Forest vs. Extreme Gradient Boosting using Cuckoo Search Optimizer for Detecting Arabic Cyberbullying

Saadi,

Dhannoon

2023

Iraqi Journal of Science

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Cyberbullying is one of the major electronic problems, and it is not a new phenomenon. It was present in the traditional form before the emergence of social networks, and cyberbullying has many consequences, including emotional and physiological states such as depression and anxiety. Given the prevalence of this phenomenon and the importance of the topic in society and its negative impact on all age groups, especially adolescents, this work aims to build a model that detects cyberbullying in the comments on social media (Twitter) written in the Arabic language using Extreme Gradient Boosting (XGBoost) and Random Forest methods in building the models. After a series of pre-processing, we found that the accuracy of classification of these comments was 0.861 in XGBoost, and 0.849 in Random Forest. Then the results of this model were improved by using one of the optimization algorithms called cuckoo search to adjust the parameters in two methods. The results are improved clearly in the random forest method, which obtained results similar to the extreme gradient boosting method, with a value of 0.867.

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“…Below is a visualization of the Average Engagement Rate daily by audience within a specific time and day of all activities during the COVID-19 pandemic. Engagement Rate is often used in social media marketing to measure the extent to which the users will show "interest" in and interact with the advertised (Alhaj et al 2020). Congruence was also positively related to the engagement rate.…”

Section: Average Engagement Rates Dailymentioning

confidence: 99%

“…Figure 5. Comparison of Average Engagement Rate Daily of 5 Local Skincare Brands Figure 5 above shows the comparison average engagement rate daily of 5 local skincare brands, we can see that each account has a daily Average Engagement rate whose graph display varies depending on how their strategy is for each account.Engagement Rate is often used in social media marketing to measure the extent to which the users will show "interest" in and interact with the advertised(Alhaj et al 2020). Congruence was also positively related to the engagement rate.…”

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confidence: 99%

Comparative Analysis of Social Media Activities on Local Skincare Brands in Indonesia during the COVID-19 Pandemic

Proceedings of the International Conference on Industrial Engineering and Operations Management

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Improvements in things done using Instagram have now made various skincare brands reach the broader community with two-way communication. The business value proposition of skincare brands requires a customer-centered approach that can understand their needs, wants, expectations, preferences, and behaviors. Generally, social media analytics tools for Instagram have three main features, namely activity, audience, and content in the form of data visualization. The result of visualizing data from Instagram analytics is called engagement. Instagram engagement are numbers that show the two-way interaction between the audience and the brand through their official Instagram account. The primary purpose of this scientific article is to analyze the comparison of Instagram engagement on wellknown local skincare brands in Indonesia during the COVID-19 pandemic, and to identify the factors that influence the engagement of the skincare brand's Instagram account audience. This scientific article using qualitative research methods, from several previous research journal articles and data visualization results from social media analytics tool, namely analisa.io. The results of this scientific article obtained three main factors that influence the engagement of the Instagram account audience on skincare brands, including: (1) Brand Awareness and Brand Image, (2) Parasocial Interaction, (3) Brand Post or Type of Content.

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Effects of Light Stemming on Feature Extraction and Selection for Arabic Documents Classification

Cited by 10 publications

References 21 publications

Improved playstore review sentiment classification accuracy with stacking ensemble

Improved playstore review sentiment classification accuracy with stacking ensemble

Comparing the Random Forest vs. Extreme Gradient Boosting using Cuckoo Search Optimizer for Detecting Arabic Cyberbullying

Comparative Analysis of Social Media Activities on Local Skincare Brands in Indonesia during the COVID-19 Pandemic

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