Machine learning algorithms and techniques for sentiment analysis in scientific paper reviews: A systematic literature review

Abstract

Sentiment analysis also referred to as opinion mining, is an automated process for identifying and classifying subjective information such as sentiments from a piece of text usually comments and reviews.

 Supported by machine learning algorithms, it is possible to identify positive, neutral or negative opinions, being possible to rank or classify them in order to reach some kind of conclusion or obtain any type of information. 

Thus, this paper aims to perform a systematic literature review in order to report the state-of-the-art of machine learning techniques for sentiment analysis applied to texts of reviews, comments and evaluations of scientific papers.

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https://repositorium.sdum.uminho.pt/handle/1822/65115

Sentiment Analysis in Scandinavian Languages: Systematic Review and Evaluation

 

Abstract

Natural Language Processing has seen a tremendous boost in popularity following the widespread use of the World Wide Web, and emergence of machine learning tools. 

The specific problem of sentiment analysis has become a popular topic with the availability of user generated content, from micro-blogs and the likes. 

But these data dependent problems have seen a larger jump in popularity in the international field, compared to low-resource languages, due to the availability of language specific data. 

This thesis seeks to delve into the problem of sentiment analysis research within some of these low-resource languages, specifically those of mainland Scandinavia, which are closely related languages. 

We perform a literature review to uncover popular research topics within this language specific field, and seek to find practical and theoretical work as well as resources within this field. 

Furthermore we perform experiments adapting international tools for these low-resource languages, and compare our results to that of the research, in order to further contribute to the language specific research field

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A systematic review on opinion mining and sentiment analysis in social media

 

Abstract

This paper employed information retrieval and statistical techniques for producing systematic literature review (SLR). 

Sentiment analysis (SA) and opinion mining (OM) in social media domain were considered as a case study to produce an example of SLR. 

The produced SLR introduced the field of SA and OM and surveyed current issues in user content based mining in social media field. 

SLR retrieves and evaluates the multiple relevant research papers concerning specific research questions. 

The paper details different approaches for conducting SA and OM and provides a common framework for searching and selection procedure applied to extracting the research papers that cover comprehensively the intended research directions in the field. 

This systematic review investigates the SA and OM techniques that are found in more than 60 specialised research papers in the field of data mining with respect to social media.

Sentiment analysis in social media and its application: Systematic literature review

 

Abstract

This paper is a report of a review on sentiment analysis in social media that explored the methods, social media platform used and its application. 

Social media contain a large amount of raw data that has been uploaded by users in the form of text, videos, photos and audio. 

The data can be converted into valuable information by using sentiment analysis. 

A systematic review of studies published between 2014 to 2019 was undertaken using the following trusted and credible database including ACM, Emerald Insight, IEEE Xplore, Science Direct and Scopus. 

After the initial and in-depth screening of paper, 24 out of 77 articles have been chosen from the review process. 

The articles have been reviewed based on the aim of the study. 

The result shows most of the articles applied opinion-lexicon method to analyses text sentiment in social media, extracted data on microblogging site mainly Twitter and sentiment analysis application can be seen in world events, healthcare, politics and business.

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https://www.sciencedirect.com/science/article/pii/S187705091931885X

Systematic literature review on context-based sentiment analysis in social multimedia

 

Abstract

The opinion seeking behavior of people for good decision making has greatly enhanced the importance of social media as a platform for exchange of information. 

This trend has led to a sudden spurt of information overflow on the Web. 

The huge volume of such information has to be technically processed for segregating the relevant knowledge. Sentiment analysis is the popular method extensively used for this purpose. 

It is defined as the computational study of mining the opinions from the available content about the entity of interest. 

Existing Sentiment analysis techniques quite efficiently capture opinions from text written in syntactically correct and explicit language. 

However, while dealing with the informal data, limitation has been observed in performance of sentiment analysis techniques. 

With a view to deal with the imperfect and indirect language used by the netizens, it has become necessary to work on improvement in the existing sentiment analysis techniques. 

In this regard, the conventional sentiment analysis techniques have shown some improvement on applying the appropriate context information. 

However, still there is ample scope for further research to find the relevant “context” and applying it to a given scenario. 

This systematic literature review paper intends to explore and analyze the existing work on the context-based sentiment analysis and to report gaps and future directions in the said research area.

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https://link.springer.com/article/10.1007%2Fs11042-019-7346-5

Systematic literature review of sentiment analysis on Twitter using soft computing techniques

 

ABSTRACT

Sentiment detection and classification is the latest fad for social analytics on Web. 

With the array of practical applications in healthcare, finance, media, consumer markets, and government, distilling the voice of public to gain insight to target information and reviews is non-trivial. 

With a marked increase in the size, subjectivity, and diversity of social web-data, the vagueness, uncertainty and imprecision within the information has increased manifold. 

Soft computing techniques have been used to handle this fuzziness in practical applications. 

This work is a study to understand the feasibility, scope and relevance of this alliance of using Soft computing techniques for sentiment analysis on Twitter. 

We present a systematic literature review to collate, explore, understand and analyze the efforts and trends in a well-structured manner to identify research gaps defining the future prospects of this coupling. 

The contribution of this paper is significant because firstly the primary focus is to study and evaluate the use of soft computing techniques for sentiment analysis on Twitter and secondly as compared to the previous reviews we adopt a systematic approach to identify, gather empirical evidence, interpret results, critically analyze, and integrate the findings of all relevant high-quality studies to address specific research questions pertaining to the defined research domain.

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