Exploring Classification For Sentiment Analysis From Halal Based Tweets

Setik, Roziyani; Ahmad, Raja Mohd Tariqi Raja Lope; Marjudi, Suziyanti

doi:10.1109/aidas53897.2021.9574255

Cited by 6 publications

(4 citation statements)

References 17 publications

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“…Studies have employed machine learning techniques to address halal-related issues as per the existing literature. For instance, two studies have conducted sentiment analysis of halal products using Twitter data [10], [11], while deep learning methods have been employed to detect non-halal ingredients in food [12]. However, these studies primarily utilize supervised learning methods.…”

Section: Related Workmentioning

confidence: 99%

Halal Supply Chain Risk using Unsupervised Learning Methods for Clustering Leather Industries

Kurniawan,

Lestari,

Mawardi

et al. 2024

IJCDS

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Cowhide plays a significant role in Indonesia's culinary and leather industries. It caters to the preferences of a predominantly Muslim population that emphasizes halal products. Regulatory authorities must understand its characteristics comprehensively to provide effective halal assurance to the diverse entities within Indonesia's leather industry. Traditional statistical methods for assessing halal compliance are inefficient due to the complexity and diversity of the leather industry's supply chain. This study addresses these challenges by employing unsupervised learning methods, specifically K-Means and Hierarchical clustering algorithms to analyze a dataset comprising 100 Cowhide Small and Medium Enterprises (SMEs) located in Garut Regency, West Java Province. This dataset includes 62 features that facilitate the clustering of these industries based on various halal risk factors. Experimental results indicate that the optimal number of clusters is four. The K-Means algorithm outperforms the Hierarchical clustering algorithm with a higher average silhouette score of 0.59 compared to 0.31. Furthermore, the K-Means algorithm demonstrates stability in clustering the data, making it a robust choice for this analysis. These clustering outcomes offer valuable insights into the SMEs operational characteristics and halal compliance risks, significantly enhancing the ability of regulatory authorities to implement effective halal assurance measures. Consequently, this study provides a robust framework for improving halal certification processes and aiding risk management within Indonesia's leather industry.

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Section: Related Workmentioning

confidence: 99%

Halal Supply Chain Risk using Unsupervised Learning Methods for Clustering Leather Industries

Kurniawan,

Lestari,

Mawardi

et al. 2024

IJCDS

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“…Data inconsistency can occur in a variety of ways. For example, it might occur due to data corruption during transmission or storage or user entry errors [19]. Therefore, it is essential to clean up data so it can be used in models and produce better results.…”

Section: B Data Pre-processingmentioning

confidence: 99%

Sentiment Analysis on Acceptance of New Normal in COVID-19 Pandemic using Naïve Bayes Algorithm

Samsudin¹,

Sabri²,

Isa³

et al. 2022

IJACSA

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The COVID-19 pandemic has such a significant impact and causes difficulties in many aspects that the new normal rules should be implemented to reduce the effects. New normal rules have been implemented by governments worldwide to break the virus chain and stop its transmission among the society. Even if the COVID-19 outbreak is under control, governments still need to know whether society could adapt and adjust to their new daily lifestyles. Many precautions still must be addressed as the transition to endemic status does not mean that COVID-19 will naturally eventually disappear. The World Health Organization also has warned that it is too early to treat COVID-19 as an endemic disease. Since the pandemic, many interactions have been done online, leading to the increasing social media usage to express opinions about COVID-19. The objective of the study is to explore the capability of the Naïve Bayes algorithm in the sentiment classification of the public's acceptance on the new normal in the COVID-19 pandemic. Naïve Bayes has been chosen for its good performance in solving various other classification problems. In this study, Twitter data were used for the analysis and were collected between March and June 2022. The evaluation results have shown that Naïve Bayes could generate excellent and acceptable performance in the classification with an accuracy of 83%. According to the findings of this research, many people have accepted the new normal in their daily lives. The future works would include scrapping more data based on geolocation, improving the feature extraction technique, balancing the dataset and comparing Naïve Bayes performance with other well-known classifiers. The subsequent study could also focus on detecting the emotions of the public and processing non-English tweets.

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“…Sentiment analysis can be defined as a technique that makes use of machine learning (ML) and natural language processing (NLP) models for the determination of the polarity value (negative, neutral or positive) of a text or opinion regarding a product, service, organization or person [11], [12], [13]. In different application contexts, sentiment analysis techniques have been used to analyze the polarity of customer opinions concerning the products and services offered by companies [14], [15], [16], [17]. However, challenges remain in determining intelligible perception levels based on polarity and combining them with traditional methods of perception identification (quantitative perception surveys, star ratings).…”

Section: Introductionmentioning

confidence: 99%

Fuzzy System for Perception Level Estimation in E-Commerce Web Sites

Chanchí G.,

Ospina A.,

Saba

2023

TEM Journal

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Customer reviews on e-commerce platforms significantly impact the acceptance of products offered by companies. In this paper we propose a software system based on fuzzy logic for estimating the level of perception of users of e-commerce portals regarding a product or service. The constructed system receives as its first input the level of perception of opinion, determined through sentiment analysis, while the second input is the star rating provided by each user for the product or service. For the development of the proposed fuzzy system, the RAD (Rapid Application Development) methodology was used. The fuzzy system presented in this paper aims to support decision making in the marketing divisions of companies with respect to the products and services offered.

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Exploring Classification For Sentiment Analysis From Halal Based Tweets

Cited by 6 publications

References 17 publications

Halal Supply Chain Risk using Unsupervised Learning Methods for Clustering Leather Industries

Halal Supply Chain Risk using Unsupervised Learning Methods for Clustering Leather Industries

Sentiment Analysis on Acceptance of New Normal in COVID-19 Pandemic using Naïve Bayes Algorithm

Fuzzy System for Perception Level Estimation in E-Commerce Web Sites

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