Deep text classification of Instagram data using word embeddings and weak supervision1

Hammar, Kim; Jaradat, Shatha; Dokoohaki, Nima; Matskin, Mihhail

doi:10.3233/web-200428

Cited by 5 publications

(3 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Text classification by transforming knowledge from one domain to another using LSTM and Word2Vec embedding model achieves an accuracy of 90.07% (Pan et al 2019a). 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).…”

Section: Text Classificationmentioning

confidence: 99%

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

2023

View full text Add to dashboard Cite

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.

show abstract

Section: Text Classificationmentioning

confidence: 99%

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

2023

View full text Add to dashboard Cite

show abstract

“…Illustration of nonsuicidal self-injury (NSSI) on Instagram has been explored by Scherr et al (2019) by developing an image-recognition Convolutional Neural Network (CNN) detecting the presence or absence of NSSI in digital pictures. In the fashion domain, Hammar et al (2020) devised an innovative method to classify captions, comments, and tags of Instagram posts with weak supervision using NLTK (Natural Language Toolkit) (Loper and Bird 2002) and CNN and also word embedding algorithms such as Word2Vec 5 (Mikolov et al 2013), Glove (Pennington et al 2014), and FastText 6 . Detection of drug abuse and dealing on Instagram multimodal data (captions, hashtags, comments, and photos) was analyzed by Yang and Luo (2017) by first exploring drug-related posts and second, specifying drug dealers' accounts based on a Multi-Layer Perceptron (MLP) neural network.…”

Section: Research Papermentioning

confidence: 99%

Evaluation of COVID-19 Spread Effect on the Commercial Instagram Posts using ANN: A Case Study on The Holy Shrine in Mashhad, Iran

Shooshtari¹,

Etemadfard²,

Shad³

2022

Preprint

View full text Add to dashboard Cite

The widespread deployment of social media has helped researchers access an enormous amount of data in various domains, including the the COVID-19 pandemic. This study draws on a heuristic approach to classify Commercial Instagram Posts (CIPs) and explores how the businesses around the Holy Shrine were impacted by the pandemic. Two datasets of Instagram posts (one gathered data from March 14th to April 10th, 2020, when Holy Shrine and nearby shops were closed, and one extracted data from the same period in 2019), two word embedding models – aimed at vectorizing associated caption of each post, and two neural networks – multi-layer perceptron and convolutional neural network – were employed to classify CIPs in 2019. Among the scenarios defined for the 2019 CIPs classification, the results revealed that the combination of MLP and CBoW achieved the best performance, which was then used for the 2020 CIPs classification. It was found out that the fraction of CIPs to total Instagram posts has increased from 5.58% in 2019 to 8.08% in 2020, meaning that business owners were using Instagram to increase their sales and continue their commercial activities to compensate for the closure of their stores during the pandemic. Moreover, the portion of non-commercial Instagram posts (NCIPs) in total posts has decreased from 94.42% in 2019 to 91.92% in 2020, implying the fact that since the Holy Shrine was closed, Mashhad residents and tourists could not visit it and take photos to post on their Instagram accounts.

show abstract

“…Illustration of non-suicidal self-injury (NSSI) on Instagram has been explored by (Scherr et al 2019) by developing an image-recognition Convolutional Neural Network (CNN) detecting the presence or absence of NSSI in digital pictures. In the fashion domain, (Hammar et al 2020) devised an innovative method to classify captions, comments, and tags of Instagram posts with weak supervision using NLTK (Natural Language Toolkit) (Loper and Bird 2002) and CNN and also word embedding algorithms such as Word2Vec 2 (Mikolov et al 2013), Glove (Pennington et al 2014), and FastText 3 . Detection of drug abuse and dealing on Instagram multimodal data (captions, hashtags, comments, and photos) was analyzed by (Yang and Luo 2017) by first exploring drug-related posts and second, specifying drug dealers' accounts based on a Multi-Layer Perceptron (MLP) neural network.…”

Section: -Introductionmentioning

confidence: 99%

Evaluation of COVID-19 spread effect on the Commercial Instagram Posts (CIPs) using ANN: a case study on Holy Shrine, Mashhad, Iran

Shooshtari¹,

Etemadfard²,

Shad³

2021

Preprint

View full text Add to dashboard Cite

The widespread deployment of social media has helped researchers access an enormous amount of data in various domains, including the pandemic caused by the COVID-19 spread. This study presents a heuristic approach to classify Commercial Instagram Posts (CIPs) and explores how the businesses around the Holy Shrine – a sacred complex in Mashhad, Iran, surrounded by numerous shopping centers – were impacted by the pandemic. Two datasets of Instagram posts (one gathered data from March 14th to April 10th, 2020, when Holy Shrine and nearby shops were closed, and one extracted data from the same period in 2019), two word embedding models – aimed at vectorizing associated caption of each post, and two neural networks – multi-layer perceptron and convolutional neural network – were employed to classify CIPs in 2019. Among the scenarios defined for the 2019 CIPs classification, the results revealed that the combination of MLP and CBoW achieved the best performance, which was then used for the 2020 CIPs classification. It is found out that the fraction of CIPs to total Instagram posts has increased from 5.58% in 2019 to 8.08% in 2020, meaning that business owners were using Instagram to increase their sales and continue their commercial activities to compensate for the closure of their stores during the pandemic. Moreover, the portion of non-commercial Instagram posts (NCIPs) in total posts has decreased from 94.42% in 2019 to 91.92% in 2020, implying the fact that since the Holy Shrine was closed, Mashhad citizens and tourists could not visit it and take photos to post on their Instagram accounts.

show abstract

Deep text classification of Instagram data using word embeddings and weak supervision1

Cited by 5 publications

References 12 publications

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

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

Evaluation of COVID-19 Spread Effect on the Commercial Instagram Posts using ANN: A Case Study on The Holy Shrine in Mashhad, Iran

Evaluation of COVID-19 spread effect on the Commercial Instagram Posts (CIPs) using ANN: a case study on Holy Shrine, Mashhad, Iran

Contact Info

Product

Resources

About