Extracting User's Food Preferences by Sentiment Analysis

Asani, Elham; Vahdat-Nejad, Hamed; Hosseinabadi, Saeed; Sadri, Javad

doi:10.1109/cfis49607.2020.9238725

Cited by 8 publications

(3 citation statements)

References 15 publications

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“…Food names from comments made by users were extracted and analyzed. This analysis showed a high level of precision in knowing the user's preference [48]. In another study, twitter messages were also explored and analyzed to determine the contents of tweets related to four eating 4 Human Behavior and Emerging Technologies situations-breakfast, lunch, dinner, and snack.…”

Section: Text Analysismentioning

confidence: 99%

Sentiment Analysis to Understand the Perception and Requirements of a Plant-Based Food App for Cancer Patients

Dalayya

Elsaid

et al. 2023

Human Behavior and Emerging Technologies

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Understanding human perception and requirements on food for cancer prevention and condition management is important so that food applications can be catered to cancer patients. In this paper, web scraping was conducted to understand the public’s perception, attitude, and requirements related to a plant-based diet as a recommended diet for cancer prevention and condition management. Text and sentiment analyses were carried out on results gathered from 82 social sites to determine whether noncancer and cancer patients use plant-based diets, how they have been consumed, their benefits in the prevention and condition management of cancers, the existing myths/fake news about cancer, and what do cancer patients need in a food app. The results of the text analysis highlighted gaps in existing apps, including a lack of credibility as there were a lot of fake news and myths about cancer and endorsement by professionals. Future food apps should provide personalized diets to include both plant-based diets as well as meat, symptom management, good user experience, credibility, and emotional and mental health support.

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Section: Text Analysismentioning

confidence: 99%

Sentiment Analysis to Understand the Perception and Requirements of a Plant-Based Food App for Cancer Patients

Dalayya

Elsaid

et al. 2023

Human Behavior and Emerging Technologies

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“…In 2020, Asani et al [23] presented a novel approach to extracting the food preferences of social media users using sentiment analysis. The authors collect a large dataset of food-related tweets, preprocess the data, and classify the tweets into positive, negative, and neutral sentiments.…”

Section: Related Workmentioning

confidence: 99%

Food Interests Analysis (FIA) model to extract the food preferences and interests of Twitter users

Mohamed

Al-Feel

Taie

2023

Labyrinth: Fayoum Journal of Science and Interdisciplinary Stud

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Online social networks like Facebook and Twitter have played an important role in networking, disseminating information, and sharing interests and entertainment since the internet's advent into our daily lives. Twitter has significantly contributed to the analysis of its user-generated data for personalization and tasks of recommendation due to its rapid growth as a social networking platform. Twitter posts serve as an important source of information for identifying users' positive interests and creating intelligent recommendation systems. These posts provide a lot of information that may be analyzed to determine users' preferences on various topics, including food. Twitter post analysis is an interesting field of study. Several studies have studied the sentiment analysis of tweets. Also, market forecasting is a crucial issue that requires careful consideration. Business intelligence (BI) becomes an important analytical technique for assessing consumer satisfaction and market demand. Since business intelligence requires indepth analysis, sentiment analysis is the process of using natural language processing (NLP) and machine learning (ML) techniques to identify the emotional tone and attitude in text, making it useful for analyzing Twitter posts and customer reviews and identifying user preferences and market demand. As a result, it's critical to choose relevant advertisements for users at particular locations to capture their attention and generate profit. This paper develops a proposed model for a Food Interests Analysis (FIA). It collects 20,000 publicly available tweets, and then the sentiments conveyed in the tweets are captured and normalized, then clustered according to the common topic. This paper also examines the accuracy of two lexicon-based sentiment analysis approaches for tweets. Also, this study proposes an approach that combines both topic modeling and sentiment analysis (SA) by Latent Dirichlet Allocation (LDA) using the term frequency-inverse document frequency (TF-IDF) and extracting sentiment from tweets. Thus, this approach can identify the food preference categories in which users are interested.

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“…Food names from comments made by users were extracted and analyzed. This analysis showed a high level of precision in knowing the user's preference [49]. In another study, twittermessages were also explored and analyzed to determine the contents of tweets related to four eating situations -breakfast, lunch, dinner, and snack.…”

Section: Text Analysismentioning

confidence: 99%

Understanding the perception and requirements of a plant-based nutrition app for cancer patients

Dalayya

Elsaid

et al. 2022

Preprint

View full text Add to dashboard Cite

Understanding human perception and requirements on nutrition for cancer prevention and condition management is important so that nutrition applications can be catered for cancer patients. In this paper, web-scraping was conducted to understand the public’s perception, attitude and requirements related to a plant-based diet as a recommended diet for cancer prevention and condition management. Text and sentiment analysis were carried out on results gathered from 73 social sites to determine whether non-cancer and cancer patients use plant-based diets, how they have been consumed, their benefits in the prevention and condition management of cancers, the existing myths/fake news about cancer and what do cancer patients need in a nutrition app. Results of the text analysis highlight missing gaps in existing apps to include a lack of credibility and endorsement by professionals. Future nutrition apps should provide personalized diet, symptoms management, good user experience, credibility, and emotional and mental health support.

show abstract

Extracting User's Food Preferences by Sentiment Analysis

Cited by 8 publications

References 15 publications

Sentiment Analysis to Understand the Perception and Requirements of a Plant-Based Food App for Cancer Patients

Sentiment Analysis to Understand the Perception and Requirements of a Plant-Based Food App for Cancer Patients

Food Interests Analysis (FIA) model to extract the food preferences and interests of Twitter users

Understanding the perception and requirements of a plant-based nutrition app for cancer patients

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