Analysis of the effect of sentiment analysis on extracting adverse drug reactions from tweets and forum posts

Korkontzelos, Ioannis; Nikfarjam, Azadeh; Shardlow, Matthew; Sarker, Abeed; Ananiadou, Sophia; González, Graciela

doi:10.1016/j.jbi.2016.06.007

Cited by 149 publications

(84 citation statements)

References 24 publications

(42 reference statements)

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“…As can be seen, the main topics addressed are adverse drug reactions [36, 39] and cancer posts [28, 38]. Also, only a few are focused on clinical opinions [29] and hearing loss [27].…”

Section: Methodsmentioning

confidence: 99%

Sentiment Analysis on Tweets about Diabetes: An Aspect-Level Approach

Salas-Zárate

Medina-Moreira

Lagos-Ortíz

et al. 2017

Computational and Mathematical Methods in Medicine

110

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In recent years, some methods of sentiment analysis have been developed for the health domain; however, the diabetes domain has not been explored yet. In addition, there is a lack of approaches that analyze the positive or negative orientation of each aspect contained in a document (a review, a piece of news, and a tweet, among others). Based on this understanding, we propose an aspect-level sentiment analysis method based on ontologies in the diabetes domain. The sentiment of the aspects is calculated by considering the words around the aspect which are obtained through N-gram methods (N-gram after, N-gram before, and N-gram around). To evaluate the effectiveness of our method, we obtained a corpus from Twitter, which has been manually labelled at aspect level as positive, negative, or neutral. The experimental results show that the best result was obtained through the N-gram around method with a precision of 81.93%, a recall of 81.13%, and an F-measure of 81.24%.

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“…As can be seen, the main topics addressed are adverse drug reactions [36, 39] and cancer posts [28, 38]. Also, only a few are focused on clinical opinions [29] and hearing loss [27].…”

Section: Methodsmentioning

confidence: 99%

Sentiment Analysis on Tweets about Diabetes: An Aspect-Level Approach

Salas-Zárate

Medina-Moreira

Lagos-Ortíz

et al. 2017

Computational and Mathematical Methods in Medicine

110

View full text Add to dashboard Cite

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“…The dataset used in this paper is publicly available and can be obtained from the 2nd Social Media Mining for Health Applications Shared Task at AMIA 2017 website 5 . The organizers of the task provided 8000 annotated tweets as a training dataset and 2260 additional tweets as development dataset.…”

Section: Datasetmentioning

confidence: 99%

Detecting Personal Intake of Medicine from Twitter

Mahata

Friedrichs²,

Shah

et al. 2018

IEEE Intell. Syst.

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Mining social media messages such as tweets, blogs, and Facebook posts for health and drug related information has received significant interest in pharmacovigilance research. Social media sites (e.g., Twitter), have been used for monitoring drug abuse, adverse reactions of drug usage and analyzing expression of sentiments related to drugs. Most of these studies are based on aggregated results from a large population rather than specific sets of individuals. In order to conduct studies at an individual level or specific cohorts, identifying posts mentioning intake of medicine by the user is necessary. Towards this objective we develop a classifier for identifying mentions of personal intake of medicine in tweets. We train a stacked ensemble of shallow convolutional neural network (CNN) models on an annotated dataset. We use random search for tuning the hyper-parameters of the CNN models and present an ensemble of best models for the prediction task. Our system produces state-of-the-art result, with a micro-averaged F-score of 0.693. We believe that the developed classifier has direct uses in the areas of psychology, health informatics, pharmacovigilance and affective computing for tracking moods, emotions and sentiments of patients expressing intake of medicine in social media.

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“…Moreover, Nikfarjam et al [12] added clusters of words formed from word2vec to their supervised model as an additional feature. In a recent paper, Korkontzelos et al [9] analysed the effect of sentiment analysis features in ADR classification, which made use of rules such as "negation" to improve the performance of their system. Dai et al [3] also investigated features to use for finding ADR in Twitter posts.…”

Section: A Motivating Examplementioning

confidence: 99%

Natural Language Analysis of Online Health Forums

Hasan

Levene

Weston

2017

Advances in Intelligent Data Analysis XVI

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Abstract. Despite advances in concept extraction from free text, finding meaningful health related information from online patient forums still poses a significant challenge. Here we demonstrate how structured information can be extracted from posts found in such online health related forums by forming relationships between a drug/treatment and a symptom or side effect, including the polarity/sentiment of the patient. In particular, a rule-based natural language processing (NLP) system is deployed, where information in sentences is linked together though anaphora resolution. Our NLP relationship extraction system provides a strong baseline, achieving an F1 score of over 80% in discovering the said relationships that are present in the posts we analysed.

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Analysis of the effect of sentiment analysis on extracting adverse drug reactions from tweets and forum posts

Abstract: Graphical abstract

Cited by 149 publications

References 24 publications

Sentiment Analysis on Tweets about Diabetes: An Aspect-Level Approach

Sentiment Analysis on Tweets about Diabetes: An Aspect-Level Approach

Detecting Personal Intake of Medicine from Twitter

Natural Language Analysis of Online Health Forums

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