Mining Twitter Data for Potential Drug Effects

Jiang, Keyuan; Zheng, Yujing

doi:10.1007/978-3-642-53914-5_37

Cited by 57 publications

(39 citation statements)

References 18 publications

(13 reference statements)

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“…Bian et al have proposed an approach to describe drug users and their potential adverse events [4] by analyzing the twitter messages using Natural Language Processing (NLP) which in turn builds Support Vector Machine (SVM) classifiers. Due to the nature of volume in the dataset (huge i.e., nearly 2 billion Tweets), the process is being conducted on High-Performance Computing(HPC) platform using Map Reduce [2], which shows the trend of big data analytics.…”

Section: Analysis Of Bian Et Al Methodsmentioning

confidence: 99%

Analysis of Opinion Mining on Social Media Data Streams using Hadoop

Gayatri¹,

Raghuvamshi²

2016

IJCA

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Twitter is a social networking site in which the data to be processed is in rich amounts and which can be structured, semi-structured and unstructured data streams. Opinion mining over the Twitter offers organizations a fast and effective way to monitor the feelings of public towards their services. It focuses on predicting the polarity of words and then classifies them into positive and negative feelings with the aim of identifying attitude and opinions that are expressed in any form or language. Bian et al. (2014) explains how the Map -Reduce paradigm can be applied to existing Naïve Bayes algorithm to handle a large number of tweets. All these approaches say about the real-world data sets at its accuracy level by using Hadoop File System. This paper analyses all the above methods comparatively.

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Section: Analysis Of Bian Et Al Methodsmentioning

confidence: 99%

Analysis of Opinion Mining on Social Media Data Streams using Hadoop

Gayatri¹,

Raghuvamshi²

2016

IJCA

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“…It has been observed that personal pronouns appear frequently in social media posts related to personal experiences (Elgersma and de Rijke, 2008;Jiang and Zheng, 2013). Personal pronouns were considered as a feature to classify personal and impersonal sentences (Li et al, 2010).…”

Section: Featuresmentioning

confidence: 99%

Construction of a Personal Experience Tweet Corpus for Health Surveillance

Jiang

Calix

Gupta

2016

Proceedings of the 15th Workshop on Biomedical Natural Language Processing

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Studies have shown that Twitter can be used for health surveillance, and personal experience tweets (PETs) are an important source of information for health surveillance. To mine Twitter data requires a relatively balanced corpus and it is challenging to construct such a corpus due to the labor-intensive annotation tasks of large data sets. We developed a bootstrap method of finding PETs with the use of the machine learning-based filter. Through a few iterations, our approach can efficiently improve the balance of two class dataset with a reduced amount of annotation work. To demonstrate the usefulness of our method, a PET corpus related to effects caused by 4 dietary supplements was constructed. In 3 iterations, a corpus of 8,770 tweets was obtained from 108,528 tweets collected, and the imbalance of two classes was significantly reduced from 1:31 to 1:3. In addition, two out of three classifiers used showed improved performance over iterations. It is conceivable that our approach can be applied to various other health surveillance studies that use machine learning-based classifications of imbalanced Twitter data.

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“…Bian et al[84] and Jiang et al[85] proposed methods to mine Twitter. Both approaches focused on a small set of drugs, employed MetaMap to perform NER, and used machine learning methods to identify posts of real experiences based on semantic features generated by MetaMap (e.g., presence and frequency of UMLS semantic types such as ‘ disease or syndrome’ ), along with other features such as the number and type of pronouns mentioned (assumed indicative of real experiences).…”

Section: Social Mediamentioning

confidence: 99%

Text Mining for Adverse Drug Events: the Promise, Challenges, and State of the Art

et al. 2014

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Text mining is the computational process of extracting meaningful information from large amounts of unstructured text. Text mining is emerging as a tool to leverage underutilized data sources that can improve pharmacovigilance, including the objective of adverse drug event detection and assessment. This article provides an overview of recent advances in pharmacovigilance driven by the application of text mining, and discusses several data sources—such as biomedical literature, clinical narratives, product labeling, social media, and Web search logs—that are amenable to text-mining for pharmacovigilance. Given the state of the art, it appears text mining can be applied to extract useful ADE-related information from multiple textual sources. Nonetheless, further research is required to address remaining technical challenges associated with the text mining methodologies, and to conclusively determine the relative contribution of each textual source to improving pharmacovigilance.

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Mining Twitter Data for Potential Drug Effects

Cited by 57 publications

References 18 publications

Analysis of Opinion Mining on Social Media Data Streams using Hadoop

Analysis of Opinion Mining on Social Media Data Streams using Hadoop

Construction of a Personal Experience Tweet Corpus for Health Surveillance

Text Mining for Adverse Drug Events: the Promise, Challenges, and State of the Art

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