Text Classification Models for the Automatic Detection of Nonmedical Prescription Medication Use from Social Media

Al-Garadi, Ali; Dan, Cai; Ruan,; O'Connor,; Gonzalez-Hernandez,; Perrone,; Sarker, Abeed

doi:10.1101/2020.04.13.20064089

Cited by 3 publications

(1 citation statement)

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“…The goal of this study is to make the use of our resources in text categorization as efficient as possible. Automatic and semi-automatic annotation services facilitate the annotation process for text classification tasks [7], [15], but a real-data set improves the training process. Furthermore, a consistent set of annotated data is required to develop an annotation service.…”

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confidence: 99%

Active Learning Based on Transfer Learning Techniques for Text Classification

Onita

2023

IEEE Access

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Text preprocessing is a common task in machine learning applications that involves handlabeling sets. Although automatic and semi-automatic annotation of text data is a growing field, researchers need to develop models that use resources as efficiently as possible for a learning task. The goal of this work was to learn faster with fewer resources. In this paper, the combination of active and transfer learning was examined with the purpose of developing an effective text categorization method. These two forms of learning have proven their efficiency and capacity to train correct models with substantially less training data. We considered three types of criteria for selecting training points: random selection, uncertainty sampling criterion and active transfer selection. Experimental evaluation was performed on five data sets from different domains. The findings of the experiments suggest that by combining active and transfer learning, the algorithm performs better with fewer labels than random selection of training points. INDEX TERMS active learning, active transfer learning, text classification, transfer learning I. INTRODUCTION

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Active Learning Based on Transfer Learning Techniques for Text Classification

Onita

2023

IEEE Access

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A review on machine learning techniques for text classification

Kavitha

Prabhavathy

2021

2021 4th International Conference on Computing and Communications Technologies (ICCCT)

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Inexpensive Detection of Substance Abuse Based on Social Media Data using Machine Learning

Potineni¹

2022

IJSRCSEIT

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Over the past few years, substance abuse has become one of the most severe public health problems in the United States. The annual cost of substance abuse aftereffects in the United States alone is approximately $3.73 Trillion. The societal costs of substance abuse include premature deaths, lost productivity, and increased crime rates. Unfortunately, many victims, especial¬ly in lower-income families, don't have access to early detection and early family intervention tools due to limited access to traditional diagnostic tools and rehab specialists. Currently, there is no complete diagnostic pipeline to inexpensively detect substance abuse and automatically inform family members or trusted contacts. To combat this, the experimenter developed the SOS 280 system, which utilizes machine learning techniques in a smartphone application. SOS 280 works through social media monitorin¬g and automatic notification using SMS and GPS location. The SOS280 algorithm primarily uses social media data, namely publicly available Twitter, and Instagram posts, to identify substance abuse-related activity. The experimenter collected and classified data by applying for the Twitter and Instagram Developer API Platforms, mining tweets and posts with specific drug keywords present. The investigator trained a Natural Language Processing (NLP) text classification model to analyze the sentiments on the tweets, then classifying them as positives (containing substance abuse-related keywords) and negatives. The master model is a Bidirectional Encoder Representations (BERT) derivative that uses a transformer-based architecture to detect emotions in sentences and conversations to classify substance abuse instances. In total, the researchers looked at 55,551 tweets and Instagram posts indicative of potentially alarming substance usage. Finally, the experimenter developed a smartphone application to capture trusted contact information and GPS location, send data to a remote server housing the neural network, output the network's detection, and send automated alerts to trusted contacts via SMS and GPS location. The experimenter further validated the system's effectiveness through a partnership with national nonprofit Faces and Voices of Recovery, which works with 23 million addiction recovery victims. SOS280 is an inexpensive, reliable, easy to use, and timely tool for families of young adults in predicting substance abuse.

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Text Classification Models for the Automatic Detection of Nonmedical Prescription Medication Use from Social Media

Cited by 3 publications

References 40 publications

Active Learning Based on Transfer Learning Techniques for Text Classification

Active Learning Based on Transfer Learning Techniques for Text Classification

A review on machine learning techniques for text classification

Inexpensive Detection of Substance Abuse Based on Social Media Data using Machine Learning

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