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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http://www.jcreview.com/?mno=97284

A Systematic Mapping Review: Sentiment Analysis In Higher Education Context

 

Abstract

Sentiment analysis has become increasingly a popular research area; it allows analyzing and understanding the students' opinions toward their institution to make more effective and better-targeted decisions. 

The objective of this study is to apply a systematic mapping review to investigate the current state-of-the-art on the application of sentiment analysis in the domain of higher education, and to identify the most common and successful techniques /methods that have been used. 

The current study was established through a systematic mapping review; therefore, the study's protocol was defined firstly, such as research questions, the search strategy, studies selection, information extraction, and how the study findings will be reported. 

Based on the criteria of the study, 16 related studies are selected and classified according to their application domains, platforms, techniques, and methods that have been used in the studies. 

The findings of the study showed that the adoption of the emerging area of data mining sentiment analysis in educational systems has a great potential in improving the quality of higher education institutions and evaluating the teaching process as well as teachers' performance. 

The main contribution of this study is the new classifications of many studies in higher education that may help in providing nearly a full image of the application of SA techniques, the related tools in higher education and the related areas. 

The study also found that applying specific SA techniques in the higher education field could offer the best means of solving certain learning problems and can be useful in developing teaching strategies and providing the required tools that institutions will be able to use for the purposes of continuous improvement.

https://www.archives.palarch.nl/index.php/jae/article/view/4779

Designing Positive Behavior Change Experiences: A Systematic Review and Sentiment Analysis based on Online User Reviews of Fitness and Nutrition Mobile Applications

 

Abstract

While mobile devices have become ubiquitous, illnesses derived from poor lifestyle habits are on the rise.

However, our understanding of design mechanisms that induce healthier behavior change through mobile devices is still limited.

Using the BCT Taxonomy, and online user reviews as an indicator of experience satisfaction, we make a three-folded contribution to designing interactive systems for behavior change:

(i) a systematic review of applications for physical activity and healthier eating habits, coding BCTs;

(ii) sentiment analysis performed on 20492 review sentences of these apps; and

(iii) design implications regarding the implementation features for each BCT cluster, considering the highest-scored features in terms of sentiment analysis.

Positive expressions referred to the framing/reframing technique.

Contrarily, negative expressions were mostly related to reward and threat.

Findings from this study can be used to benchmark interactions between users and behavior change interfaces, and provide design insights to support positive user experiences.

https://dl.acm.org/doi/abs/10.1145/3428361.3428403

A Systematic Review on Implicit and Explicit Aspect Extraction in Sentiment Analysis

 

Aspect-based sentiment analysis (ABSA) is currently among the most vigorous areas in natural language processing (NLP). 

Individuals, private and government institutions are increasingly using media sources for decision making. 

In the last decade, aspect extraction has been the most essential phase of sentiment analysis (SA) to conduct an abridged sentiment classification. 

However, previous studies on sentiment analysis mostly focused on explicit aspects extraction with limited work on implicit aspects. 

To the best of our knowledge, this is the first systematic review that covers implicit, explicit, and the combination of both implicit and explicit aspect extractions. 

Therefore, this systematic review has been conducted to, 1) identify techniques used for extracting implicit, explicit, or both implicit and explicit aspects; 2) analyze the various evaluation metrics, data domains, and languages involved in the implicit and explicit aspect extraction in sentiment analysis from years 2008 to 2019; 3) identify the key challenges associated with the techniques based on the result of a comprehensive comparative analysis; and finally, 4) highlight the feasible opportunities for future research directions. 

This review can be used to assist novice and prominent researchers to understand the concept of both implicit and explicit aspect extractions in aspect-based sentiment analysis domain.

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https://ieeexplore.ieee.org/document/9234464

A Systematic Review of Levels, Methods and Tasks in Sentiment Analysis

 

Abstract

With faster progression of internet and increasing number of users on social media, opinions, reviews, views, ratings, feedback are generated by users on the web are also increasing very rapidly. 

These contents generated by the social media express the sentiment about the products, people and events. 

Huge amount of sentiment data generated by social media which are in unstructured format. 

It becomes mandatory to analysis such kind of data by using techniques of text mining and sentiment analysis. 

Many challenges have been faced by researchers in sentiment analysis. 

Analysis and detection of sentiment polarity becomes difficult due to these challenges. 

Natural language processing and text analysis techniques are applied to analyze the sentiments, opinions and reviews; alongwithidentification and extraction of subjective information from text. 

This paper presents the current state of sentiment analysis and detailed review of the existing work done by the researchers till yet. 

The various research directions in field of sentiment analysis are also identified in this paper.

https://novyimir.net/gallery/nmrj%202645%20f.pdf

Sentiment analysis and its applications in fighting COVID-19 and infectious diseases: A systematic review

Abstract

The COVID-19 pandemic caused by the novel coronavirus SARS-CoV-2 occurred unexpectedly in China in December 2019. 

Tens of millions of confirmed cases and more than hundreds of thousands of confirmed deaths are reported worldwide according to the World Health Organisation. 

News about the virus is spreading all over social media websites. 

Consequently, these social media outlets are experiencing and presenting different views, opinions and emotions during various outbreak-related incidents. 

For computer scientists and researchers, big data are valuable assets for understanding people's sentiments regarding current events, especially those related to the pandemic. 

Therefore, analysing these sentiments will yield remarkable findings. 

To the best of our knowledge, previous related studies have focused on one kind of infectious disease. 

No previous study has examined multiple diseases via sentiment analysis. 

Accordingly, this research aimed to review and analyse articles about the occurrence of different types of infectious diseases, such as epidemics, pandemics, viruses or outbreaks, during the last 10 years, understand the application of sentiment analysis and obtain the most important literature findings.

 Articles on related topics were systematically searched in five major databases, namely, ScienceDirect, PubMed, Web of Science, IEEE Xplore and Scopus, from 1 January 2010 to 30 June 2020. 

These indices were considered sufficiently extensive and reliable to cover our scope of the literature. 

Articles were selected based on our inclusion and exclusion criteria for the systematic review, with a total of n = 28 articles selected. 

All these articles were formed into a coherent taxonomy to describe the corresponding current standpoints in the literature in accordance with four main categories: lexicon-based models, machine learning-based models, hybrid-based models and individuals. 

The obtained articles were categorised into motivations related to disease mitigation, data analysis and challenges faced by researchers with respect to data, social media platforms and community. 

Other aspects, such as the protocol being followed by the systematic review and demographic statistics of the literature distribution, were included in the review. 

Interesting patterns were observed in the literature, and the identified articles were grouped accordingly. 

This study emphasised the current standpoint and opportunities for research in this area and promoted additional efforts towards the understanding of this research field.

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https://www.sciencedirect.com/science/article/abs/pii/S0957417420308988

A Concept-based Sentiment Analysis Approach for Arabic

.

Abstract

Concept-Based Sentiment Analysis (CBSA) methods are considered to be more advanced and more accurate when it compared to ordinary Sentiment Analysis methods, because it has the ability of detecting the emotions that conveyed by multi-word expressions concepts in language. 

This paper presented a CBSA system for Arabic language which utilizes both of machine learning approaches and concept-based sentiment lexicon. 

For extracting concepts from Arabic, a rule-based concept extraction algorithm called semantic parser is proposed. 

Different types of feature extraction and representation techniques are experimented among the building prosses of the sentiment analysis model for the presented Arabic CBSA system. 

A comprehensive and comparative experiments using different types of classification methods and classifier fusion models, together with different combinations of our proposed feature sets, are used to evaluate and test the presented CBSA system. 

The experiment results showed that the best performance for the sentiment analysis model is achieved by combined Support Vector Machine-Logistic Regression (SVM-LR) model where it obtained a F-score value of 93.23% using the Concept-Based-Features+Lexicon-Based-Features+Word2vec-Features (CBF+LEX+W2V) features combinations.

.

A systematic literature review on machine learning applications for consumer sentiment analysis using online reviews

 

Abstract

Consumer sentiment analysis is a recent fad for social media related applications such as healthcare, crime, finance, travel, and academics. 

Disentangling consumer perception to gain insight into the desired objective and reviews is significant. 

With the advancement of technology, a massive amount of social web-data increasing in terms of volume, subjectivity, and heterogeneity, becomes challenging to process it manually. 

Machine learning techniques have been utilized to handle this difficulty in real-life applications. 

This paper presents the study to find out the usefulness, scope, and applicability of this alliance of Machine Learning techniques for consumer sentiment analysis on online reviews in the domain of hospitality and tourism. 

We have shown a systematic literature review to compare, analyze, explore, and understand the attempts and direction in a proper way to find research gaps to illustrating the future scope of this pairing. 

This work is contributing to the extant literature in two ways; firstly, the primary objective is to read and analyze the use of machine learning techniques for consumer sentiment analysis on online reviews in the domain of hospitality and tourism. 

Secondly, in this work, we presented a systematic approach to identify, collect observational evidence, results from the analysis, and assimilate observations of all related high-quality research to address particular research queries referring to the described research area.

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