SentiHealth-Cancer: A sentiment analysis tool to help detecting mood of patients in online social networks

Rodrigues, Ramon; Dores, Rafael Marques das; Camilo-Junior, Celso G.; Rosa, Thierson Couto

doi:10.1016/j.ijmedinf.2015.09.007

Cited by 87 publications

(44 citation statements)

References 33 publications

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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%

“…Furthermore, the English language is the most used [27–30, 36, 37, 39], although the work [38] presented a proposal based on Portuguese. Regarding the sentiment analysis level, the document-level is the most attended [28–30, 36, 38, 39]. Conversely, sentence-level and aspect-level have been little studied [27, 37].…”

Section: Methodsmentioning

confidence: 99%

“…Results show that multinomial NB performed better than SVM. Rodrigues et al [38] presented SentiHealth (SCH-pt), a tool that allows detecting positive and negative sentiments of cancer patients in Portuguese language. The dataset used for the experiments was collected from Facebook social network.…”

Section: Related Workmentioning

confidence: 99%

“…Finally, regarding sentiment analysis levels, most works above cited have focused on document-level sentiment classification [28–30, 36, 38, 39], where a polarity positive, negative, or neutral is assigned to the whole document. Also, some approaches have worked in a sentence-level and aspect-level also called feature-level [27, 37].…”

Section: Related Workmentioning

confidence: 99%

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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

117

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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%

Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

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

117

View full text Add to dashboard Cite

show abstract

“…Although there are several studies and tools of SA, few tools in the field of pharmaceutical industries use SA to analyze opinion considering the person himself or other entities as target of analysis [1]. Thus, the performance of SA tools in this field is mainly related with polarity classification task, when bad scoring of terms can effectively affect the global sentiment polarity, that is occurs when a piece of text stating an opinion on a single issue that can be classified as one of two opposing sentiments regarding to a specific context.…”

Section: T R a N S A C T I O N S O N M A C H I N E L E A R N I N G A mentioning

confidence: 99%

Sentiment Analysis Tool for Pharmaceutical Industry & Healthcare

Grissette

Nfaoui

Bahir³

2017

TMLAI

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Sentiment analysis (SA) is broadly used to analyze people's opinions about a product or an event to identify breakpoints in public opinion. Particularly, pharmaceutical companies use SA to ensure they gain a competitive edge through better understanding of patients' experiences allowing for more personalization and high responsiveness to consumers on social media. Patients selfreports on social media, frequently capture varied elements ranging from medical issues, product accessibility issues to potential side effects. The exploitation of such overwhelming unstructured data on social media through SA is of critical importance, however, the generalpurpose sentiment analysis tools are not adapted and do not incorporate the specific lexicon used in the life sciences/pharma context which reduces the ability of such tools to accurately detect the meanings of the sentiments expressed towards treatments/scientific studies and pharma companies at large. Indeed, those tools involve generalized dictionaries, techniques and methods that face several challenges to detecting the suitable sentiment polarity. In this paper, we develop a dedicated research tool that extract in real time the sentiments and emotions conveyed by users on social media regarding pharmaceutical industries. Our focus is to improve the analyze of opinion about a target in pharmaceutical industries based on hybrid approaches used in single unified system throughout sentiment analysis process to seek the relevant sentiment polarity, which is tailored specifically to detect positive, negative or neutral opinions of patients and consumer health. This paper proposes a first architecture validated by a real and industry case.

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Interactive Attention Network for Adverse Drug Reaction Classification

Alimova

Solovyev

2018

Communications in Computer and Information Science

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SentiHealth-Cancer: A sentiment analysis tool to help detecting mood of patients in online social networks

Cited by 87 publications

References 33 publications

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

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

Sentiment Analysis Tool for Pharmaceutical Industry & Healthcare

Interactive Attention Network for Adverse Drug Reaction Classification

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