Global halal food discourse on social media: a text mining approach

Mostafa, Mohamed M.

doi:10.1080/13216597.2020.1795702

Cited by 18 publications

(17 citation statements)

References 118 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…, 2020). Basically, social networks are a free way for consumers to express their unbiased feelings towards a product (Mostafa, 2020). Also, social media is today one of the main sources of information (Faour-Klingbeil et al.…”

Section: Literature Frameworkmentioning

confidence: 99%

See 1 more Smart Citation

Forecasting the next revolution: food technology’s impact on consumers' acceptance and satisfaction

Virto

Valilla-Arróspide

2022

BFJ

View full text Add to dashboard Cite

PurposeIn a sector that needs to satisfy a fast-increasing population, advancements like cultivated meat and bio-circular economy are basic to sustain the industry and the society. As innovations are key for economic and social progress, it is crucial to understand consumers' position on this matter.Design/methodology/approachBased on text data mining, 7,030 tweets were collected and organised into 14 different food-related topics. Of the total, 6 of these categories were positive, 5 were negative and 3 were neutral.FindingsIn total, 6 categories related to food technologies were positively perceived by Twitter users, such as innovative solutions and sustainable agriculture, while 5 like the virtual dimensions of the industry or crisis-related scenarios were negatively perceived. It is remarkable that 3 categories had a neutral sentiment, which gives ground to improvement before consumers have a negative opinion and consequently will be more complicated to change their minds.Originality/valueTechnological innovations are becoming predominant in the food industry. The SARS-CoV-2 pandemic has made the sector improve even faster. Traditional methods needed to be substituted and technologies such as robots, artificial intelligence, blockchain and genetics are here to stay.

show abstract

Section: Literature Frameworkmentioning

confidence: 99%

“…The impact of food technology classification depending on the aroused feeling (i.e. positive, neutral or negative) (Dondokova et al, 2019;Mostafa, 2020;Chen et al, 2020). Thus, the next RQ is proposed: RQ2.…”

Section: Research Questions Approachmentioning

confidence: 99%

Forecasting the next revolution: food technology’s impact on consumers' acceptance and satisfaction

Virto

Valilla-Arróspide

2022

BFJ

View full text Add to dashboard Cite

show abstract

“…The fi ndings have shown that halal tourism is a global market and not only restricted to Muslim countries. Mostafa (2020) analyzed 11.700 world-wide random tweets from 20 January 2015 to 19 January 2019 as sample data. The NRC and AFINN lexicons were used to measure global sentiments regarding halal food.…”

Section: Introductionmentioning

confidence: 99%

“…Halal cosmetics had a higher sentiment score compared to halal tourism, which shows a higher level of a keen interest in this fi eld. Mostafa (2020) conducted research on global halal food on social media using data from Twi er which concluded that sentiment towards halal food tends to be positive and there are general concerns about animal welfare. Feizollah, et al (2019) examined the sentiments of Twi er users towards halal tourism and halal cosmetics which concluded that public sentiment was positive.…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis on Halal Certification

Hakim¹,

Mujahidah²,

Rusydiana³

2022

Harmoni

View full text Add to dashboard Cite

Halal Certificate is a halal guarantee certificate for a product issued by an institution, be it a private institution or a government agency. There are pros and cons among the public and researchers regarding the existence of Halal certification. Therefore, this research was conducted to examine halal certification within the scope of the economy in terms of the opinions of Twitter social media users. The method used in this research is a qualitative approach with descriptive statistics from literature studies with the theme of halal certification. The data used in this study are 13,824 tweets related to Halal Certification. To classify tweets, a Python library called VADER (Valence Aware Dictionary and Sentiment Reasoner) is used. VADER (Valence Aware Dictionary and Sentiment Reasoner) is a lexicon and rule-based sentiment analysis tool adapted to social media sentiment but also works on texts from other domains. The results of the sentiment analysis show that there is a tendency for positive perceptions of Halal Certification in the opinion of Twitter users, with positive sentiment of 41.8%, neutral sentiment of 30.8% and negative sentiment of 27.4%. This diversity of sentiment results is obtained because of the pros and cons of the existence of Halal Certification. The benefit of this research is for literature material and considerations related to Halal Certification. As well as efforts to identify threats, negative perceptions, and lack of Halal certification. In addition, to find out positive perceptions, advantages and benefits of Halal Certification.

show abstract

“…There is a growing interest in quantitative text analysis in communication studies, but researchers in the field still depend on a small number of analytic tools. Some of them apply off-the-shelf dictionaries to English texts (Hoffman & Jengelley, 2020;Kim et al, 2016;Mostafa, 2020;Rodrigues et al, 2019;Smirnova et al, 2017;Su et al, 2017), but others construct original dictionaries (Petkevic, 2018) or employ supervised machine learning models (Ceron & Splendore, 2018;Kim et al, 2016;Sabatovych, 2019;Su et al, 2017) to analyze texts in other languages. Sometimes only Englishlanguage texts are included in analysis in studies of countries where English is not the most common language due to the lack of appropriate dictionary (e.g., Jaidka et al, 2019).…”

mentioning

confidence: 99%

Latent Semantic Scaling: A Semisupervised Text Analysis Technique for New Domains and Languages

Watanabe

2020

Communication Methods and Measures

View full text Add to dashboard Cite

Many social scientists recognize that quantitative text analysis is a useful research methodology, but its application is still concentrated in documents written in European languages, especially English, and few sub-fields of political science, such as comparative politics and legislative studies. This seems to be due to the absence of flexible and cost-efficient methods that can be used to analyze documents in different domains and languages. Aiming to solve this problem, this paper proposes a semisupervised document scaling technique, called Latent Semantic Scaling (LSS), which can locate documents on various pre-defined dimensions. LSS achieves this by combining user-provided seed words and latent semantic analysis (word embedding). The article demonstrates its flexibility and efficiency in largescale sentiment analysis of New York Times articles on the economy and Asahi Shimbun articles on politics. These examples show that LSS can produce results comparable to that of the Lexicoder Sentiment Dictionary (LSD) in both English and Japanese with only small sets of sentiment seed words. A new heuristic method that assists LSS users to choose a near-optimal number of singular values to obtain word vectors that best capture differences between documents on target dimensions is also presented. Many social scientists recognize that quantitative text analysis is a useful research methodology, but its application is still concentrated in documents written in European languages, especially English, and few sub-fields of political science, such as comparative politics and legislative studies. This is not because they are only interested in domestic politics in North America and Europe but because the existing quantitative text analysis toolkit is not suitable for analysis of documents in non-European languages or in other fields. The domination of European languages in quantitative text analysis is partially due to its history: many of the text analysis dictionaries, including the Lexicoder Sentiment Dictionary (LSD) (Young & Soroka, 2012) and the Linguistic Inquiry and Word Count (LIWC), were created based on the General Inquirer dictionaries (Stone et al., 1966), which were developed to analyze English texts during the Cold War; statistical analysis of textual data was also introduced to political science for CONTACT Kohei Watanabe

show abstract

Global halal food discourse on social media: a text mining approach

Cited by 18 publications

References 118 publications

Forecasting the next revolution: food technology’s impact on consumers' acceptance and satisfaction

Forecasting the next revolution: food technology’s impact on consumers' acceptance and satisfaction

Sentiment Analysis on Halal Certification

Latent Semantic Scaling: A Semisupervised Text Analysis Technique for New Domains and Languages

Contact Info

Product

Resources

About