scholarly journals Arabic Sentiment Analysis: A Systematic Literature Review

2020 ◽  
Vol 2020 ◽  
pp. 1-21 ◽  
Author(s):  
Abdullatif Ghallab ◽  
Abdulqader Mohsen ◽  
Yousef Ali
Keyword(s):  
Systematic Review ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Sentiment Classification ◽  
Future Research ◽  
Classification Methods ◽  
Feature Generation ◽  
Future Studies ◽  
Arabic Sentiment Analysis ◽  
Support Research

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.

Polarity Classification of Arabic Sentiments

2016 ◽  
Vol 11 (3) ◽  
pp. 32-49 ◽  
Author(s):  
Mohammed N. Al-Kabi ◽  
Heider A. Wahsheh ◽  
Izzat M. Alsmadi
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Operating Characteristic ◽  
Opinion Mining ◽  
Online Social Network ◽  
The Social ◽  
Polarity Classification ◽  
Arabic Sentiment Analysis ◽  
Modern Standard

Sentiment Analysis/Opinion Mining is associated with social media and usually aims to automatically identify the polarities of different points of views of the users of the social media about different aspects of life. The polarity of a sentiment reflects the point view of its author about a certain issue. This study aims to present a new method to identify the polarity of Arabic reviews and comments whether they are written in Modern Standard Arabic (MSA), or one of the Arabic Dialects, and/or include Emoticons. The proposed method is called Detection of Arabic Sentiment Analysis Polarity (DASAP). A modest dataset of Arabic comments, posts, and reviews is collected from Online social network websites (i.e. Facebook, Blogs, YouTube, and Twitter). This dataset is used to evaluate the effectiveness of the proposed method (DASAP). Receiver Operating Characteristic (ROC) prediction quality measurements are used to evaluate the effectiveness of DASAP based on the collected dataset.

Sentiment Analysis of Comparative Sentences for Chinese Document

2012 ◽  
Vol 157-158 ◽  
pp. 1079-1082
Author(s):  
Guo Shi Wu ◽  
Xiao Yin Wu ◽  
Jing Jing Wei
Keyword(s):  
Sentiment Analysis ◽  
Opinion Mining ◽  
Sentiment Classification ◽  
The Third ◽  
Sentence Patterns ◽  
Simple Sentences ◽  
Document Level ◽  
Level Analysis

One of the most widely-studied sub-problems of opinion mining is sentiment classification, which includes three study levels: word, sentence and document. At the third level, most of the existing methods ignore comparative sentences which have particular sentence patterns and may lower the precision of the document-level analysis. This paper studies sentiment analysis of comparative sentences. The aim is to determine whether opinions expressed in a comparative sentence are positive or negative. Experiments of comparing with document-level sentiment analysis based on simple sentences shows the effectiveness of the proposed method.

scholarly journals KNN classifier based approach for multi-class sentiment analysis of twitter data

2018 ◽  
Vol 7 (3) ◽  
pp. 1372
Author(s):  
Soudamini Hota ◽  
Sudhir Pathak
Keyword(s):  
Sentiment Analysis ◽  
Opinion Mining ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Online News ◽  
Classification Algorithm ◽  
Sentiment Classification ◽  
Supervised Machine Learning ◽  
K Nearest Neighbor ◽  
News Reports

‘Sentiment’ literally means ‘Emotions’. Sentiment analysis, synonymous to opinion mining, is a type of data mining that refers to the analy-sis of data obtained from microblogging sites, social media updates, online news reports, user reviews etc., in order to study the sentiments of the people towards an event, organization, product, brand, person etc. In this work, sentiment classification is done into multiple classes. The proposed methodology based on KNN classification algorithm shows an improvement over one of the existing methodologies which is based on SVM classification algorithm. The data used for analysis has been taken from Twitter, this being the most popular microblogging site. The source data has been extracted from Twitter using Python’s Tweepy. N-Gram modeling technique has been used for feature extraction and the supervised machine learning algorithm k-nearest neighbor has been used for sentiment classification. The performance of proposed and existing techniques is compared in terms of accuracy, precision and recall. It is analyzed and concluded that the proposed technique performs better in terms of all the standard evaluation parameters. 

Machine Learning Approaches for Sentiment Analysis

2014 ◽  
pp. 193-208 ◽  
Author(s):  
Basant Agarwal ◽  
Namita Mittal
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Machine Learning Algorithms ◽  
Sentiment Classification ◽  
Learning Approaches ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Knowledge Based ◽  
Semantic Orientation

Opinion Mining or Sentiment Analysis is the study that analyzes people's opinions or sentiments from the text towards entities such as products and services. It has always been important to know what other people think. With the rapid growth of availability and popularity of online review sites, blogs', forums', and social networking sites' necessity of analysing and understanding these reviews has arisen. The main approaches for sentiment analysis can be categorized into semantic orientation-based approaches, knowledge-based, and machine-learning algorithms. This chapter surveys the machine learning approaches applied to sentiment analysis-based applications. The main emphasis of this chapter is to discuss the research involved in applying machine learning methods mostly for sentiment classification at document level. Machine learning-based approaches work in the following phases, which are discussed in detail in this chapter for sentiment classification: (1) feature extraction, (2) feature weighting schemes, (3) feature selection, and (4) machine-learning methods. This chapter also discusses the standard free benchmark datasets and evaluation methods for sentiment analysis. The authors conclude the chapter with a comparative study of some state-of-the-art methods for sentiment analysis and some possible future research directions in opinion mining and sentiment analysis.

Machine Learning Approaches for Sentiment Analysis

Big Data ◽  
2016 ◽  
pp. 1917-1933
Author(s):  
Basant Agarwal ◽  
Namita Mittal
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Machine Learning Algorithms ◽  
Sentiment Classification ◽  
Learning Approaches ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Knowledge Based ◽  
Semantic Orientation

Opinion Mining or Sentiment Analysis is the study that analyzes people's opinions or sentiments from the text towards entities such as products and services. It has always been important to know what other people think. With the rapid growth of availability and popularity of online review sites, blogs', forums', and social networking sites' necessity of analysing and understanding these reviews has arisen. The main approaches for sentiment analysis can be categorized into semantic orientation-based approaches, knowledge-based, and machine-learning algorithms. This chapter surveys the machine learning approaches applied to sentiment analysis-based applications. The main emphasis of this chapter is to discuss the research involved in applying machine learning methods mostly for sentiment classification at document level. Machine learning-based approaches work in the following phases, which are discussed in detail in this chapter for sentiment classification: (1) feature extraction, (2) feature weighting schemes, (3) feature selection, and (4) machine-learning methods. This chapter also discusses the standard free benchmark datasets and evaluation methods for sentiment analysis. The authors conclude the chapter with a comparative study of some state-of-the-art methods for sentiment analysis and some possible future research directions in opinion mining and sentiment analysis.

scholarly journals Motivational Determinants of Sexting

2019 ◽  
Vol 28 (3) ◽  
pp. 461-482
Author(s):  
Kristina Sesar ◽  
Arta Dodaj ◽  
Nataša Šimić
Keyword(s):  
Systematic Review ◽  
Young Adults ◽  
Theoretical Model ◽  
Empirical Research ◽  
Electronic Media ◽  
Individual Characteristics ◽  
Future Research ◽  
Future Studies ◽  
Sexually Explicit ◽  
Contextual Characteristics

Sexting, defined as the exchange of sexually explicit contents among adolescents and young adults using electronic media, has become a popular topic of interest in the wider public. This paper proposed a theoretical model to explain motivational determinants of sexting behaviour. We reviewed sexting literature to show how multiple empirical confirmed motivational factors can be modelled within our framework. By analysing empirical research, we posited a set of individual characteristics and contextual characteristics that explain sexting behaviour. We offered a systematic review of motivational determinants relevant to capture sexting behaviour and guidelines for future research in this area. We intended by this theory to organize prior research into a more theoretically satisfying approach to study sexting and to encourage researchers to expand the model and use it in future studies.

scholarly journals High Performing Sentiment Analysis based on Fast Fourier Transform over Temporal Intuitionistic Fuzzy Value

2021 ◽  
Author(s):  
Basanti Pal Nandi ◽  
Amita Jain ◽  
Devendra Kumar Tayal ◽  
Poonam Ahuja Narang
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Sentiment Analysis ◽  
Frequency Domain ◽  
Fuzzy Set ◽  
Opinion Mining ◽  
New Technology ◽  
Intuitionistic Fuzzy Set ◽  
Sentiment Classification ◽  
Intuitionistic Fuzzy

Abstract Sentiment analysis or opinion mining has an extensive area in the field of research. Today we consider the huge amount of structured and unstructured data available in the web for a particular subject to get an opinion. The surplus data handling termed as big data requires some new technology to deal with. This paper considers the requirement of sentiment analysis of such huge data for fast processing. Based on Fast Fourier Transform on Temporal Intuitionistic fuzzy set generated from text, this algorithm (FFT-TIFS) expedites the sentiment classification. Fourier analysis converts a signal from its time domain to its representation in frequency domain. Such frequency domain algorithm on Temporal Intuitionistic fuzzy set is used in Sentiment analysis for the first time. This algorithm is useful for short twitter text, document level as well as sentence level binary sentiment classification. It is tested on aclImdb, Polarity, MR, Sentiment140 and CR dataset which gives an average of 80% accuracy. The proposed method shows significant improvement in required time complexity where the method achieves 17 times faster processing in comparison to sequential Fuzzy C Means(FCM) method and again it is at least 7 times faster than distributed FCM method present in literature. The method presented in this paper has a novel approach towards fastest processing time and suitability of various sizes of the text sentiment analysis.

scholarly journals TUJUH BELAS TAHUN STUDI AKUNTANSI ZAKAT DI INDONESIA: SEBUAH REVIU SISTEMATIK DAN SARAN UNTUK RISET DI MASA MENDATANG

2019 ◽  
Vol 19 (1) ◽  
pp. 87
Author(s):  
Hafiez Sofyani
Keyword(s):  
Systematic Review ◽  
Research Design ◽  
Future Research ◽  
Research Paradigm ◽  
Future Studies ◽  
Accounting Research ◽  
Research Paradigms ◽  
Management Organization ◽  
Published Research

<p><em>Roughly 17 years have passed since the first article on zakat accounting was written by Harahap and Yusuf (2002). Until 2019 today, the average accounting zakat articles published in accredited journals ranking 2 and 3 are only 1.53 per year. This indicates that attention to the issue of accounting for zakat by accounting academics is low. Various problems related to zakat accounting research are presented in this paper, starting from the shallowness of the study, there was no connection between one research with other research, and monotonous topics which have no follow-up on research design that should be more in-depth. Many papers also did not really present practical and theoretical contributions. Various research paradigms which are actually very valuable to be employed in zakat accounting research have also not been explored further. Departing from the various problems, this paper tries to provide direction for future research regarding zakat accounting issue. This paper is a systematic review of published research as well as developments in academic and practices issues in the field related to zakat accounting and Zakat Management Organization (OPZ). Review results offers three points of improvement for future studies, namely the proposal of several contemporary issues/topics that need to be investigated, the research paradigm recommended to be applied, and theories or concepts that need to be tested related to research on zakat accounting.</em></p>

scholarly journals A systematic review of automated journalism scholarship: guidelines and suggestions for future research

2021 ◽  
Vol 1 ◽  
pp. 4
Author(s):  
Samuel Danzon-Chambaud
Keyword(s):  
Field Theory ◽  
Systematic Review ◽  
Future Research ◽  
Media Industry ◽  
Future Studies ◽  
Journalism Studies ◽  
The Media ◽  
English Speaking ◽  
Algorithmic Techniques ◽  
Selection Of

Background: The use of advanced algorithmic techniques is increasingly changing the nature of work for highly trained professionals. In the media industry, one of the technical advancements that often comes under the spotlight is automated journalism, a solution generally understood as the auto generation of journalistic stories through software and algorithms, without any human input except for the initial programming. Methods: In order to conduct a systematic review of existing empirical research on automated journalism, I analysed a range of variables that can account for the semantical, chronological and geographical features of a selection of academic articles as well as their research methods, theoretical backgrounds and fields of inquiry. I then engaged with and critically assessed the meta-data that I obtained to provide researchers with a good understanding of the main debates dominating the field. Results: My findings suggest that the expression “automated journalism” should be called into question, that more attention should be devoted to non-English speaking scholarship, that the collective and individual impacts of the technology on media practitioners should be better documented and that well-established sociological theories such as institutionalism and Bourdieu’s field theory could constitute two adequate frameworks to study automated journalism practices. Conclusions: This systematic literature therefore provides researchers with an overview of the main challenges and debates that are occurring within the field of automated journalism studies. Future studies should, in particular, make use of institutionalism and field theory to explore how automated journalism is impacting the work of media practitioners, which could help unearth common patterns across media organisations.

scholarly journals Detection of Sentiment Analysis in Social Media using Deep Learning

2020 ◽  
Vol 9 (1) ◽  
pp. 636-643
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Activation Function ◽  
Sentiment Classification ◽  
Network Layers ◽  
Sentiment Dictionary ◽  
Lstm Network ◽  
The Military

With the rapid climb of web page from social media, such studies as online opinion mining or sentiment analysis of text have started receiving attention from government, industry, and academic sectors. In recent years, sentiment analysis has not only emerged under knowledge fusion within the big data era, but has also become a well-liked research topic within the area of AI and machine learning. This study used the Military life PTT board of Taiwan’s largest online forum because the source of its experimental data. the aim of this study was to construct a sentiment analysis framework and processes for social media so as to propose a self-developed military sentiment dictionary for improving sentiment classification and analyze the performance of various deep learning models with various parameter calibration combinations. The experimental results show that the accuracy and F1-measure of the model that mixes existing sentiment dictionaries and therefore the self-developed military sentiment dictionary are better than the results from using existing sentiment dictionaries only. Furthermore, the prediction model trained using the activation function, Tanh, and when the amount of Bi-LSTM network layers is 2, the accuracy and F1-measure have a good better performance for sentiment classification.

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