Adverse Drug Reaction Related Post Detecting Using Sentiment Feature.

Jingfang Liu, Xiaoyan Jiang, Qiangyuan Chen, Mei Song, Jia Li

Abstract read

In one paragraph

Article in Iranian journal of public health, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Sentiment analysis of social media posts on pharmacotherapy: A scoping review.Pharmacology research & perspectives · 2020
Article

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Jingfang LiuDept. of Information Management, School of Management, Shanghai University, Shanghai 200444, China.

Xiaoyan JiangDept. of Information Management, School of Management, Shanghai University, Shanghai 200444, China.

Qiangyuan ChenDept. of Economics, School of Economics, Shanghai University, Shanghai 200444, China.

Mei SongDept. of Software Engineering, School of Smart Education, Jiangsu Normal University, Xuzhou 221116, China.

Jia LiDept. of Management Science and Engineering, School of Business, East China University of Science and Technology, Shanghai 200237, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe posts related to Adverse Drug Reaction (ADR) on social websites are believed to be valuable resource for post-marketing drug surveillance. Beyond domain feature, the aim of this study was to find a more effective method to detect ADR related post.

methodsWe conducted experiment on posts using sentiment features from March 8 to May 20 in 2016 in Shanghai of China. Firstly, the diabetes posts were collected; the 1814 posts were annotated by hand. Secondly, sentiment features set were generated and the

resultsBy comparing the posts detection performance of different feature sets, using sentiment features by CHI statistics can improve ADR related post detection performance. By comparing the ADR-related group with the non-ADR group, performance of ADR related post detection was better than the performance of non-ADR post detection. We could obtain highest performance owing to introducing sentiment feature and using CHI feature selection technique, and the method was proved to be effective during detecting post related to ADR.

conclusionBy using sentiment feature and CHI feature selection technique, we can get an effective method to detect post related to ADR.

Indexed as

Adverse drug reactionPostSentiment feature

Identifiers

PMID30087872

PMCPMC6077625

What OpenQuestion holds

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.