Drink2Vec: Improving the classification of alcohol-related tweets using distributional semantics and external contextual enrichment

Grzeça, Marcos A.; Becker, Karin; Galante, Renata

doi:10.1016/j.ipm.2020.102369

Cited by 8 publications

(8 citation statements)

References 17 publications

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“…Word embedding techniques are widely employed in social media research (Grzeça et al. , 2020; Lindow et al.…”

Section: Methodsmentioning

confidence: 99%

A content-based metric for social media influencer marketing

Chang,

2023

IMDS

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PurposeThe study aims to propose an instrument for measuring product-centeredness (i.e. the extent to which comment content is related to a product) using word embedding techniques as well as explore its determinants.Design/methodology/approachThe study collected branded posts from 205 Instagram influencers and empirically examined how four factors (i.e. authenticity, vividness, coolness and influencer–product congruence) influence the content of the comments on branded posts.FindingsPost authenticity and congruence are shown to have positive effects on product-centeredness. The interaction between coolness and authenticity is also significant. The number of comments or likes on branded posts is not correlated with product-centeredness.Originality/valueIn social media influencer marketing, volume-based metrics such as the numbers of likes and comments have been researched and applied extensively. However, content-based metrics are urgently needed, as fans may ignore brands and focus on influencers. The proposed instrument for assessing comment content enables marketers to construct content-based metrics. Additionally, the authors' findings enhance the understanding of social media users' engagement behaviors.

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“…Word embedding techniques are widely employed in social media research (Grzeça et al. , 2020; Lindow et al.…”

Section: Methodsmentioning

confidence: 99%

A content-based metric for social media influencer marketing

Chang,

2023

IMDS

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“… Phat and Anh ( 2020 ) Vietnamese text classification Vietnamese news articles LSTM, CNN, SVM, NB Word2Vec LSTM + Word2Vec achieves an F1-score of 95.74% 39. Grzeça et al ( 2020 ) Social networking site tweets analysis for identification of alcohol-related tweets Datasets DS1-Q1, Q2, Q3 SVM, XGBoost, CNN, BiLSTM DSWE(Drink2Vec), BERT CNN + Drink2Vec achieves an F1-score of 94.45% SANAD Single-label Arabic news articles datasets, NADiA News articles datasets in Arabic with multi-labels, HAN Hierarchical attention network, HDBSCAN Hierarchical Density-Based Spatial Clustering of Applications with Noise, LDA Logistic regression, linear discriminant analysis, QDA Quadratic discriminant analysis, NB Naïve Bayes, SVM Support vector machine, KNN k-nearest neighbor, DT Decision tree, RF Random forest, XGBoost MLP Multilayer perceptron, LIWC Linguistic Inquiry and Word Count features, NER Named entity recognition, PMMC Process model matching contest dataset, DLMF Digital Library of Mathematical Functions, GB Gradient Boosting, SGC Stochastic Gradient Descent, HAN Hierarchical attention network, DFFNN Deep feed-forward neural network. …”

Section: Appendix Amentioning

confidence: 99%

“…Social media tweets analysis (Hammar et al 2020). Domain-specific word embedding outperforms the BERT embedding model and achieves an F1-score of 94.45% (Grzeça et al 2020), (Zuheros et al 2019), (Xiong et al 2021). Ensemble deep learning model with RoBERT embedding achieves an accuracy of 90.30% to classify tweets for information collection (Malla and Alphonse 2021), (Hasni and Faiz 2021), (Zheng et al 2020).…”

Section: Text Classificationmentioning

confidence: 99%

Impact of word embedding models on text analytics in deep learning environment: a review

2023

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The selection of word embedding and deep learning models for better outcomes is vital. Word embeddings are an n-dimensional distributed representation of a text that attempts to capture the meanings of the words. Deep learning models utilize multiple computing layers to learn hierarchical representations of data. The word embedding technique represented by deep learning has received much attention. It is used in various natural language processing (NLP) applications, such as text classification, sentiment analysis, named entity recognition, topic modeling, etc. This paper reviews the representative methods of the most prominent word embedding and deep learning models. It presents an overview of recent research trends in NLP and a detailed understanding of how to use these models to achieve efficient results on text analytics tasks. The review summarizes, contrasts, and compares numerous word embedding and deep learning models and includes a list of prominent datasets, tools, APIs, and popular publications. A reference for selecting a suitable word embedding and deep learning approach is presented based on a comparative analysis of different techniques to perform text analytics tasks. This paper can serve as a quick reference for learning the basics, benefits, and challenges of various word representation approaches and deep learning models, with their application to text analytics and a future outlook on research. It can be concluded from the findings of this study that domain-specific word embedding and the long short term memory model can be employed to improve overall text analytics task performance.

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“…Semantic analysis of OSN published content is a current hot research area that allows to detect and prevent undesirable uses of the OSN. For instance, the semantic analysis at word level has been reported to allow to detect cyberbullying [ 30 ], helps detecting drunken tweets [ 24 ], and the age of users [ 56 ]. Also, social media posts content analysis allows to predict depression levels [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Neuro-semantic prediction of user decisions to contribute content to online social networks

Cleveland

Ríos

Aguilera

et al. 2022

Neural Comput & Applic

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Understanding at microscopic level the generation of contents in an online social network (OSN) is highly desirable for an improved management of the OSN and the prevention of undesirable phenomena, such as online harassment. Content generation, i.e., the decision to post a contributed content in the OSN, can be modeled by neurophysiological approaches on the basis of unbiased semantic analysis of the contents already published in the OSN. This paper proposes a neuro-semantic model composed of (1) an extended leaky competing accumulator (ELCA) as the neural architecture implementing the user concurrent decision process to generate content in a conversation thread of a virtual community of practice, and (2) a semantic modeling based on the topic analysis carried out by a latent Dirichlet allocation (LDA) of both users and conversation threads. We use the similarity between the user and thread semantic representations to built up the model of the interest of the user in the thread contents as the stimulus to contribute content in the thread. The semantic interest of users in discussion threads are the external inputs for the ELCA, i.e., the external value assigned to each choice.. We demonstrate the approach on a dataset extracted from a real life web forum devoted to fans of tinkering with musical instruments and related devices. The neuro-semantic model achieves high performance predicting the content posting decisions (average F score 0.61) improving greatly over well known machine learning approaches, namely random forest and support vector machines (average F scores 0.19 and 0.21).

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Drink2Vec: Improving the classification of alcohol-related tweets using distributional semantics and external contextual enrichment

Cited by 8 publications

References 17 publications

A content-based metric for social media influencer marketing

A content-based metric for social media influencer marketing

Impact of word embedding models on text analytics in deep learning environment: a review

Neuro-semantic prediction of user decisions to contribute content to online social networks

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