Big data and sentiment analysis: A comprehensive and systematic literature review

 

Abstract

Sentiment analysis can extract information from many text sources such as reviews, news, and blogs; then it classifies them based on their polarity. 

Moreover, big data is produced via mobile networks and social media. 

Applications of sentiment analysis on big data are used as a way of classifying the opinions into diverse sentiment. 

Accordingly, performing sentiment analysis on big data can be helpful for a business to take useful commercial insights from text-oriented content. 

However, there are very few comprehensive investigations and profound argument in this context. 

The goal of this paper is to provide a comprehensive and systematic investigation of the state-of-the-art techniques and highlight the directions for future research. 

In this paper, we used systematic literature review method and in the first step, we obtained 15 351 articles; then, based on different filters, 48 related articles were attained. 

We have selected 23 articles based on the year of publication, the relevance of the journal, the completeness of the text, the nonrepeatability of the title, and the page number. 

Also, we have categorized big data and sentiment analysis into two classifications: centralized and distributed platforms. 

Furthermore, the disadvantages and advantages of the investigated techniques are studied and their key issues are emphasized. 

Consequently, this study shows that a better analysis of textual big data in terms of sentiment increases efficiency, flexibility, and intelligence. 

By providing comparative information and analyzing the current developments in this area, this paper will directly support academics and practicing professionals for better handling of big data in the field of sentiment analysis. 

This study sheds some new light on using sentiment analysis and big data for public opinion estimation and prediction.

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https://onlinelibrary.wiley.com/doi/abs/10.1002/cpe.5671

Sentiment Analysis of Textual Content in Social Networks From Hand-Crafted to Deep Learning-Based Models [Problem Statement]

 

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...

 Most of the existing systems on sentiment analysis rely heavily on a rich set of sentiment resources (such as text corpora with manually annotated sentiment polarity, sentiment lexicons, and word embeddings). However, the distribution of sentiment resources is very imbalanced among languages. Thus, building a sentiment analysis system in low-resource languages requires tremendous human effort to construct such resources, which is a time-consuming and expensive task.

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Although opinions are the most common shared content in online social networks and forums, people also tend to share their emotions which are the keys to their feelings and thoughts. Emotion analysis is the task of determining the attitude towards a target or topic. The attitude can be the polarity (positive or negative), or an emotional state such as joy, anger or sadness [101–103].

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1.2 Objectives

In this thesis, we aim to develop methods to automatically analyse textual content shared on social networks and identify people’ opinions, emotions and feelings at different level of analysis and in different languages. As such, we introduce the following set of goals:

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• To develop efficient sentiment analysis systems based on new features that can be used with traditional Machine Learning (Machine Learning) methods. Towards this objective, we aim to use pre-built resources such as sentiment lexicons and word embeddings to extract new features.

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• To move sentiment analysis beyond sentence-level and polarity-based analysis. Towards this objective, we are interested in:

– Proposing ensemble systems that combine classical Machine Learning models with Deep Learning to analyse emotions expressed on Twitter.

– Developing a Deep Learning based model to solve the problem of multi-label emotions classification.

– Utilising Deep Learning based models to analyse opinions at the aspect level.

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• To move sentiment analysis beyond a single language.

We aim to utilise the concept of transfer learning to develop a system that can transfer sentiment knowledge from high resources languages to low resources languages. The final goal of this objective is to obtain a universal sentiment analysis system that works with low resource languages and does not require machine translation.

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• To test the developed systems on real cases of analysis

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• The idea of this objective is to test our developed systems in real applications and different domains. Hence, towards this objective, we define the following sub-goals:

– To collect tweets of local people, visitors and official brand destination offices from different tourist destinations and analyse the opinions shared in these tweets.

– To combine the aspect-based sentiment analysis with multi-criteria decision aid systems to improve the decision-making process.

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https://deim.urv.cat/~itaka/itaka2/PDF/acabats/PhD_Thesis/TESI_Mohammed_Jabreel.pdf

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Sentiment and position-taking analysis of parliamentary debates: A systematic literature review

 

Abstract

Parliamentary and legislative debate transcripts provide access to information concerning the opinions, positions and policy preferences of elected politicians. 

They attract attention from researchers from a wide variety of backgrounds, from political and social sciences to computer science. 

As a result, the problem of automatic sentiment and position-taking analysis has been tackled from different perspectives, using varying approaches and methods, and with relatively little collaboration or cross-pollination of ideas. 

The existing research is scattered across publications from various fields and venues. 

In this article we present the results of a systematic literature review of 61 studies, all of which address the automatic analysis of the sentiment and opinions expressed and positions taken by speakers in parliamentary (and other legislative) debates. 

In this review, we discuss the available research with regard to the aims and objectives of the researchers who work on these problems, the automatic analysis tasks they undertake, and the approaches and methods they use. 

We conclude by summarizing their findings, discussing the challenges of applying computational analysis to parliamentary debates, and suggesting possible avenues for further research.

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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