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Food related consumer behavior is a topic of major interest to areas such as health and marketing. Social media offers a scenario in which people share information about preferences, interests and motivations about eating habits and food products that have not been exploded as appropriate. In this work we present an algorithm to exploit the potenti...
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... emoji characters and sequences from Unicode standard. This list has a total of 2620 emojis. -Food emoji list: Felbo et al., [10] used the emoji prediction to find topics related to feeling in different domains. In our proposal we use a subset of the 95 emojis in the emoji list, named as the food emoji list. An extract of this list is presented in Fig. 2, where both the emoji and its meaning in English and Spanish, can be seen. -What list: to construct this list, the initial auto generated food list (see Sect. 3) was manually reviewed, generating a new list which considers only unigrams, used on the food consumption context. It contains 776 words including their stem. -Where list: this ...
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... For example, social media influencers often initiate messages with emoji in order to attract consumers [53]; this may be particularly effective for engaging younger audiences [152]. Since businesses use emoji to understand consumers' emotional reflections on their products, services, or branding [108,109,121], some work also explores evaluative uses of emoji-based surveys. For example, Jaeger et al. studied a survey tool based on 33 facial emoji, finding that consumers are generally able to interpret and discriminate between these emoji, with very few differences based on age, gender, or frequency of emoji usage [69]; yet such surveys should not rely exclusively on emoji, but should consider them as complementary to text-based ways of understanding sentiment [66,67]. ...
Following Facebook's introduction of the "Like" in 2009, CaringBridge (a nonprofit health journaling platform) implemented a "Heart" symbol as a single-click reaction affordance in 2012. In 2016, Facebook expanded its Like into a set of emotion-based reactions. In 2021, CaringBridge likewise added three new reactions: "Prayer", "Happy", and "Sad." Through user surveys ($N=808$) and interviews ($N=13$), we evaluated this product launch. Unlike Likes on mainstream social media, CaringBridge's single-click Heart was consistently interpreted as a simple, meaningful expression of acknowledgement and support. Although most users accepted the new reactions, the product launch transformed user perceptions of the feature and ignited major disagreement regarding the meanings and functions of reactions in the high stakes context of health crises. Some users found the new reactions to be useful, convenient, and reducing of caregiver burden; others felt they cause emotional harms by stripping communication of meaningful expression and authentic care. Overall, these results surface tensions for small social media platforms that need to survive amidst giants, as well as highlighting crucial trade-offs between the cognitive effort, meaningfulness, and efficiency of different forms of Computer-Mediated Communication (CMC). Our work provides three contributions to support researchers and designers in navigating these tensions: (1) empirical knowledge of how users perceived the reactions launch on CaringBridge; (2) design implications for improving health-focused CMC; and (3) concrete questions to guide future research into reactions and health-focused CMC.
... In previous agrifood-related research using Twitter data, wordcount analysis has dominated (Carr et al., 2015;Fried et al., 2014;Ruggeri and Samoggia, 2018). Other attempts to generate knowledge from UGC for the benefit of marketers were largely confined to content analysis (Vidal et al., 2015), sentiment analysis (Alaparthi and Mishra, 2021;Chakraborty et al., 2020;Ibrahim, Wang and Bourne, 2017;Mostafa, 2019;Recuero-Virto and Valilla-Arróspide, 2021) or text analysis using machine learning (ML) techniques (Moreno-Sandoval et al., 2018;Singha, Shuklab, and Mishrac, 2018). However, these investigations were not enriched by the identification of hidden patterns (Mishra, 2021). ...
Twitter es una destacada plataforma de medios sociales utilizada ampliamente por las empresas alimentarias para compartir información con los consumidores. Este estudio tiene como objetivo determinar el comportamiento en Twitter de diferentes minoristas de alimentación que operan en España y el Reino Unido para arrojar luz sobre sus intereses y afinidades. El estudio recopiló y analizó un total de 54.000 tweets de las cuentas oficiales de Twitter de 17 minoristas de alimentación. Analizando el contenido generado por los minoristas de alimentación en Twitter con el recuento de palabras, el análisis de contenido generado por estos usuarios y el análisis de redes sociales, se detectaron algunas características que podrían ser relevantes para los proveedores de estos minoristas de alimentación. La identificación de las diferencias en la actividad y las comunicaciones en Twitter, así como también las afinidades entre algunos de ellos, confirman el potencial de los datos de Twitter como fuente de información para realizar estudios de marketing en general. Del mismo modo, descubrimos que la adopción de la analítica de datos de Twitter por los responsables de marketing de las cooperativas agroalimentarias podría ser muy útil para avanzar en las estrategias centradas en el cliente. Finalmente, la investigación presenta las limitaciones y propone nuevas líneas de trabajo futuro.
... The results show that detection, extraction, and classification of emotions, feelings, and opinions are the main applications coded as private states analysis. This category records 33 techniques, algorithms, or methods for performing [125] (TSSE) Tweet Sentiment Score Estimator [104] (BM) Naive Bayes, Bayesian Logistic [126]; [127] (LSA) Latent Semantic Analysis [128] (LIWC) Linguistic Inquiry and Word Count [129]; [130]; [131] (SANT) Sociological Approach to handling Noisy and short Texts [132] (SC) Sarcasm (TPR) True Positive Ratio [92] (SVM) Support Vector Machine [92] (LRS) Linguistic Rules Sarcasm [133]; [124] (TC) Text Classification (SVM) Support Vector Machine [134]; [135] (ENS) Ensemble Classifiers [135]; [136] (LECM) Latent Event Category Model [137] (BM) Naive Bayes, Bayesian Logistic [137]; [138] (RF) Random Forest [ [144]; [145]; [95]; [146]; [147] (SI) Social Influence (PN) Proximity Networks [102] (PR) Pagerank [113] (ST) Statistical techniques [100] (BM) Naive Bayes, Bayesian Logistic [111] (DF) Difussion (RM) Rumor Model [117] (BM) Naive Bayes, Bayesian Logistic [127] (ST) Statistical techniques [122] (VAM) Vector Autoregressive Model [136] (MAC) Modified Adsorption with celebrity removal [110]; [118] the analysis. Among the most exciting findings is that the most common technique is BoW (Bag of Words), followed by SVM and SENTIStrenght, a tool that generically performs sentiment calculation. ...
This research aims to analyze the Digital Social Networks (DSN) behavior, constructed from the network’s relationships, interactions, and expressions of users’ private states through collective subjectivity. For this purpose, an onion-ring system called COSSOL has been built in a case study for Twitter, following a hybrid approach to integrate Machine Learning classifiers and structural metrics from Computational Linguistics and Computational Sociology disciplines, respectively. The paper designs two experimentation scenarios divided into cases of collective subjectivity analysis for Colombia under different levels of communities’ granularity. The first case validates the system by performing a cointegration test on the metrics of each construct for the onion rings’ communities. The results show that some communities better propagate their subjective expressions against the disclosed topic when they have a higher network density and a common polarity. Moreover, the most stable communities in polarity towards a topic are those whose members are highly connected. Conversely, communities with a higher centrality index in a subset of members do not exhibit stability in collective subjectivity towards a topic disclosed in that community. The second case validates the model with a series of Social Network Analysis (SNA) metrics with a polarity layer to describe the second onion ring subcommunities and their temporal variation through community recalculation. The results show no polar distributions similar to the bimodal ones representing consensus in the values of the common Thinking Acting and Feeling (TAF) forms. In addition, general negative sentiment is identified for the ten most representative nodes of the subcommunities analyzed.
... Исследователи применяют различные лингвистические теории, преимущественно из области риторики, для изучения характера воздействия эмодзи на потребителя, стимулирования взаимодействия с потенциальным покупателем и повышения его заинтересованности в покупке. Доминирующая роль эмодзи в выражении эмоционального состояния в цифровой коммуникации делает их эффективным инструментом для отслеживания и измерения эмоций пользователя по отношению к продуктам, брендам и услугам [27][28][29]. ...
Technical advances and digital means of communication have led to the development of digital semiotics which is characterised by its multimodality and abounds in paralinguistic elements such as emojis, emoticons, memes, etc. These extralinguistic elements serve as a compensatory mechanism in the new communication means. The increasing interest of users in various iconic signs and symbols generates the research interest in different fields of knowledge. The study aims to consider cognitive, semiotic and psycholinguistic features of emojis in interpersonal communication through analysing their functions in text messages and in social network messages. An attempt to reveal their persuasive mechanism is made. The research is based on a large scale dataset comprised of the private text messages as well as public posts on social networks which include verbal and nonverbal / iconic elements. The research data presents a multilingual bank of English, Russian and French sources. The research methods include context analysis, linguistic and pragmatic analysis and content analysis. The findings show that emojis in private interpersonal communication perform a number of functions, namely nonverbal, emotive, pragmatic, punctuation, substitutional, decorative and rhetorical functions. These iconic symbols incorporated in the interpersonal digital communication present a compensatory mechanism and the means of persuasion of a message addressee / recipient. The combination of verbal and iconic elements triggers a double focusing mechanism, and the perception is shaped by all cognitive mechanisms including rational and emotional, unconscious components.
... Some authors have analyzed tweets in areas such as consumer food preferences or habits [13][14][15] or communication of Corporate Social Responsibility (CSR) of agri-food companies [16], but on agri-food research it has not been explored as appropriate [14] and, to date, we did not find any research that investigates the usefulness of SNSs media marketing to increase in the competitiveness of the agri-producer sector. ...
... Some authors have analyzed tweets in areas such as consumer food preferences or habits [13][14][15] or communication of Corporate Social Responsibility (CSR) of agri-food companies [16], but on agri-food research it has not been explored as appropriate [14] and, to date, we did not find any research that investigates the usefulness of SNSs media marketing to increase in the competitiveness of the agri-producer sector. ...
... However, in our digitized media environment, Automated Content Analysis (ACA) has gained importance and popularity [43]. Recently, quantitative techniques for extracting intelligence from food-related tweets as sentiment analysis [44,45] using Partition Around Medoids (PAM) and clustering algorithms [46]; or text analysis using Machine Learning (ML) such as Support Vector Machine (SVM) and hierarchical clustering [47], or n-gram [14] are being used. ...
Data are currently characterized as the world’s most valuable resource and agriculture is responding to this global trend. The challenge in that particular field of study is to create a Digital Agriculture that help the agri-food sector grow in a fair, competitive environment. As automated machine learning techniques and big data are global research trends in agronomy, this paper aims at comparing different marketing techniques based on Content Analysis to determine the feasibility of using Twitter to design marketing strategies and to determine which techniques are more effective, in particular, for the strawberry industry. A total of 2249 hashtags were subjected to Content Analysis using the Word-count technique, Grounded Theory Method (GTM), and Network Analysis (NA). Findings confirm the results of previous studies regarding Twitter’s potential as a useful source of information due to its lower execution and analysis costs. In general, NA is more effective, cheaper, and faster for Content Analysis than that based both on GTM and automated Word-count. This paper reveals the potential of strawberry-related Twitter data for conducting berry consumer studies, useful in increasing the competitiveness of the berry sector and filling an important gap in the literature by providing guidance on the challenge of data science in agronomy.
... SpaCy includes a statistical model for the tagging, parsing, and entity recognition in CNN [7], [13], [14]. It is a free library that can be configurated depending on specific requirements of each domain including features to process the Spanish language. ...
The importance of the gathered information in Integrated Security Services as ECU911 in Ecuador is evidenced in terms of its quality and availability in order to perform decision-making tasks. It is a priority to avoid the loss of relevant information such as event address, places references, names, etc. In this context it is present Named Entity Recognition (NER) analysis for discovering information into informal texts. Unlike structured corpus and labeled for NER analysis like CONLL2002 or ANCORA, informal texts generated from emergency call dialogues have a very wide linguistic variety; in addition, there is a strong tending to lose important information in their processing. A relevant aspect to considerate is the identification of texts that denotes entities such as the physical address where emergency events occurred. This study aims to extract the locations in which an emergency event has been issued. A set of experiments was performed with NER models based on Convolutional Neural Network (CNN). The performance of models was evaluated according to parameters such as training dataset size, dropout rate, location dictionary, and denoting location. An experimentation methodology was proposed, with it follows the next steps: i) Data preprocessing, ii) Dataset labeling, iii) Model structuring, and iv) Model evaluating. Results revealed that the performance of a model improves when having more training data, an adequate dropout rate to control overfitting problems, and a combination of a dictionary of locations and replacing words denoting entities.
... Emoji can also be a way of reflecting consumers' emotions, describing user's profiles (Moreno-Sandoval et al., 2018), and especially monitoring the emotions users feel toward products, brands, and services (Rathan et al., 2017(Rathan et al., , 2018Phand et al., 2018;Moussa, 2019). It has been found that gender, age and frequency of usage do not affect consumers' ability to describe and distinguish stimuli with emoji (Jaeger et al., 2018b), and certain emojis can help consumers better differentiate product samples (Schouteten et al., 2019). ...
A growing body of research explores emoji, which are visual symbols in computer mediated communication (CMC). In the 20 years since the first set of emoji was released, research on it has been on the increase, albeit in a variety of directions. We reviewed the extant body of research on emoji and noted the development, usage, function, and application of emoji. In this review article, we provide a systematic review of the extant body of work on emoji, reviewing how they have developed, how they are used differently, what functions they have and what research has been conducted on them in different domains. Furthermore, we summarize directions for future research on this topic.
The collection and analysis of digital data from social media is a rapidly growing methodology in sensory-consumer science, with a wide range of applications for research studying consumer attitudes, preferences, and sensory responses to food. The aim of this review article was to critically evaluate the potential of social media research in sensory-consumer science with a focus on advantages and disadvantages. This review began with an exploration into different sources of social media data and the process by which data from social media is collected, cleaned, and analyzed through natural language processing for sensory-consumer research. It then investigated in detail the differences between social media-based and conventional methodologies, in terms of context, sources of bias, the size of data sets, measurement differences, and ethics. Findings showed participant biases are more difficult to control using social media approaches, and precision is inferior to conventional methods. However, findings also showed social media methodologies may have other advantages including an increased ability to investigate trends over time and easier access to cross-cultural or global insights. Greater research in this space will identify when social media can best function as an alternative to conventional methods, and/or provide valuable complementary information.
Digital social networks have become an essential source of information because celebrities use them to share their opinions, ideas, thoughts, and feelings. This makes digital social networks one of the preferred means for celebrities to promote themselves and attract new followers. This paper proposes a model of feature selection for the classification of celebrities profiles based on their use of a digital social network Twitter. The model includes the analysis of lexical, syntactic, symbolic, participation, and complementary information features of the posts of celebrities to estimate, based on these, their demographic and influence characteristics. The classification with these new features has an F1-score of 0.65 in Fame, 0.88 in Gender, 0.37 in Birth year, and 0.57 in Occupation. With these new features, the average accuracy improve up to 0.14 more. As a result, extracted features from linguistic cues improved the performance of predictive models of Fame and Gender and facilitate explanations of the model results. Particularly, the use of the third person singular was highly predictive in the model of Fame.
Consumer psychology is a window to understand the behavior of the consumer. Marketers should understand consumer psychology to design and implement better marketing activities. This article will discuss how artificial intelligence helps the marketer to understand consumer psychology. There are many ways to understand the behavior of the buyer. Nevertheless, this article explains how Twitter data can be used to know about customer perception by performing sentiment analysis.
