Sentiment analysis in health and well-being: Systematic review

 

Abstract

Background: 

Sentiment analysis (SA) is a subfield of natural language processing whose aim is to automatically classify the sentiment expressed in a free text. 

It has found practical applications across a wide range of societal contexts including marketing, economy, and politics. 

This review focuses specifically on applications related to health, which is defined as “a state of complete physical, mental, and social well-being and not merely the absence of disease or infirmity.”

 Objective: 

This study aimed to establish the state of the art in SA related to health and well-being by conducting a systematic review of the recent literature. 

To capture the perspective of those individuals whose health and well-being are affected, we focused specifically on spontaneously generated content and not necessarily that of health care professionals.

Methods: 

Our methodology is based on the guidelines for performing systematic reviews. 

In January 2019, we used PubMed, a multifaceted interface, to perform a literature search against MEDLINE. 

We identified a total of 86 relevant studies and extracted data about the datasets analyzed, discourse topics, data creators, downstream applications, algorithms used, and their evaluation. 

Results: 

The majority of data were collected from social networking and Web-based retailing platforms. 

The primary purpose of online conversations is to exchange information and provide social support online. 

These communities tend to form around health conditions with high severity and chronicity rates.

Different treatments and services discussed include medications, vaccination, surgery, orthodontic services, individual physicians, and health care services in general. 

We identified 5 roles with respect to health and well-being among the authors of the types of spontaneously generated narratives considered in this review: a sufferer, an addict, a patient, a carer, and a suicide victim. 

Out of 86 studies considered, only 4 reported the demographic characteristics. 

A wide range of methods were used to perform SA. 

Most common choices included support vector machines, naïve Bayesian learning, decision trees, logistic regression, and adaptive boosting. 

In contrast with general trends in SA research, only 1 study used deep learning. 

The performance lags behind the state of the art achieved in other domains when measured by F-score, which was found to be below 60% on average. 

In the context of SA, the domain of health and well-being was found to be resource poor: few domain-specific corpora and lexica are shared publicly for research purposes. 

Conclusions: 

SA results in the area of health and well-being lag behind those in other domains. 

It is yet unclear if this is because of the intrinsic differences between the domains and their respective sublanguages, the size of training datasets, the lack of domain-specific sentiment lexica, or the choice of algorithms.

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https://medinform.jmir.org/2020/1/e16023

Arabic Sentiment Analysis: A Systematic Literature Review

 Abstract

With the recently grown attention from different research communities for opinion mining, there is an evolving body of work on Arabic Sentiment Analysis (ASA). 

This paper introduces a systematic review of the existing literature relevant to ASA. 

The main goals of the review are to support research, to propose further areas for future studies in ASA, and to smoothen the progress of other researchers' search for related studies. 

The findings of the review propose a taxonomy for sentiment classification methods. 

Furthermore, the limitations of existing approaches are highlighted in the preprocessing step, feature generation, and sentiment classification methods. 

Some likely trends for future research with ASA are suggested in both practical and theoretical aspects.

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https://www.hindawi.com/journals/acisc/2020/7403128/

Online Reviews Over Sentiment Analysis using Machine Learning: A Systematic Review

Abstract

One of the key territories of NLP is Sentiment Analysis, the capacity to comprehend emotional tones in speech and text. 

This Systematic Literature Review has focused on papers between 2015 to 2020, taken from trusted and credible database such as IEEE Xplore, Science Direct and Springer. A total of 70 papers have been chosen for this review. 

This SLR approach is followed to get an effective insight on various work being done in this research field using Machine learning techniques: supervised or unsupervised. Different research questions have been looked up and discussed. 

The result shows that most of the work have used SVM for classification techniques and accuracy as the performance metrics. 

Also most of the dataset are yielded from e-commerce sites for product reviews, reviews in form of tweets from twitter and in various other fields like hospitality reviews, movie reviews and other social networking sites opinions.

Key words: SVM, Machine Learning, SLR

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