Sentiment Analysis in Education Domain: A Systematic Literature Review

2018 ◽  
pp. 285-297 ◽  
Author(s):  
Karen Mite-Baidal ◽  
Carlota Delgado-Vera ◽  
Evelyn Solís-Avilés ◽  
Ana Herrera Espinoza ◽  
Jenny Ortiz-Zambrano ◽  
...  
Keyword(s):  
Literature Review ◽  
Sentiment Analysis ◽  
Systematic Literature Review

scholarly journals Arabic Aspect-Based Sentiment Analysis: A Systematic Literature Review

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Ruba Obiedat ◽  
Duha Al-Darras ◽  
Esra Alzaghoul ◽  
Osama Harfoushi
Keyword(s):  
Literature Review ◽  
Sentiment Analysis ◽  
Systematic Literature Review

scholarly journals Sentiment Analysis using SVM: A Systematic Literature Review

2018 ◽  
Vol 9 (2) ◽  
Author(s):  
Munir Ahmad ◽  
Shabib Aftab ◽  
Muhammad Salman ◽  
Noureen Hameed
Keyword(s):  
Literature Review ◽  
Sentiment Analysis ◽  
Systematic Literature Review

scholarly journals Approaches to Cross-Domain Sentiment Analysis: A Systematic Literature Review

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 16173-16192 ◽  
Author(s):  
Tareq Al-Moslmi ◽  
Nazlia Omar ◽  
Salwani Abdullah ◽  
Mohammed Albared
Keyword(s):  
Literature Review ◽  
Sentiment Analysis ◽  
Systematic Literature Review ◽  
Cross Domain

scholarly journals Multilingual Sentiment Analysis: A Systematic Literature Review

2021 ◽  
Vol 29 (1) ◽  
Author(s):  
Nur Atiqah Sia Abdullah ◽  
Nur Ida Aniza Rusli
Keyword(s):  
Literature Review ◽  
Sentiment Analysis ◽  
Systematic Literature Review ◽  
Online Community ◽  
Language Analysis ◽  
Evaluation Models ◽  
Topic Clustering ◽  
The Common ◽  
N Gram ◽  
Processing Techniques

With the explosive growth of social media, the online community can freely express their opinions without disclosing their identities. People with hidden agendas can easily post fake opinions to discredit target products, services, politicians, or organizations. With these big data, monitoring opinions and distilling their sentiments remain a formidable task because of the proliferation of diverse sites with a large volume of opinions that are portrayed in multilingual. Therefore, this paper aims to provide a systematic literature review on multilingual sentiment analysis, which summarises the common languages supported in multilingual sentiment analysis, pre-processing techniques, existing sentiment analysis approaches, and evaluation models that have been used for multilingual sentiment analysis. By following the systematic literature review, the findings revealed, most of the models supported two languages, and English is seen as the most used language in sentiment analysis studies. None of the reviewed literature has catered the combination of languages for English, Chinese, Malay, and Hindi language on multilingual sentiment analysis. The common pre-processing techniques for the multilingual domain are tokenization, normalization, capitalization, N-gram, and machine translation. Meanwhile, the sentiment analysis classification techniques for multilingual sentiment are hybrid sentiment analysis, which includes localized language analysis, unsupervised topic clustering, and then followed by multilingual sentiment analysis. In terms of evaluation, most of the studies used precision, recall, and accuracy as the benchmark for the results.

Systematic literature review of sentiment analysis in the Spanish language

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sonia Osorio Angel ◽  
Adriana Peña Pérez Negrón ◽  
Aurora Espinoza-Valdez
Keyword(s):  
Literature Review ◽  
Sentiment Analysis ◽  
Systematic Literature Review ◽  
Design Methodology ◽  
Spanish Language ◽  
Point Of View ◽  
Classification Systems ◽  
Content Type ◽  
Linguistic Resources ◽  
Hybrid Approaches

PurposeMost studies on Sentiment Analysis are performed in English. However, as the third most spoken language on the Internet, Sentiment Analysis for Spanish presents its challenges from a semantic and syntactic point of view. This review presents a scope of the recent advances in this area.Design/methodology/approachA systematic literature review on Sentiment Analysis for the Spanish language was conducted on recognized databases by the research community.FindingsResults show classification systems through three different approaches: Lexicon based, Machine Learning based and hybrid approaches. Additionally, different linguistic resources as Lexicon or corpus explicitly developed for the Spanish language were found.Originality/valueThis study provides academics and professionals, a review of advances in Sentiment Analysis for the Spanish language. Most reviews on Sentiment Analysis are for English, and other languages such as Chinese or Arabic, but no updated reviews were found for Spanish.

scholarly journals Lexicon Based Sentiment Analysis in Indonesia Languages : A Systematic Literature Review

2021 ◽  
Vol 1 (1) ◽  
pp. 363-367
Author(s):  
Yuli Fauziah ◽  
Bambang Yuwono ◽  
Agus Sasmito Aribowo
Keyword(s):  
Literature Review ◽  
Sentiment Analysis ◽  
Systematic Literature Review ◽  
Classification Accuracy ◽  
Main Question ◽  
Maximum Accuracy ◽  
Stop Word ◽  
Positive Sentiment ◽  
Negative Sentiment

This systematic literature review aims to determine the trend of lexicon based sentiment analysis research in Indonesian Language in the last two years. The focus of the study is on the understanding of preprocessing used in lexicon-based sentiment analysis studies in the last two years, the lexicon used in these studies, and classification accuracy. The main question in this SLR : what techniques of lexicon based sentiment analysis will provide the highest accuracy. The most widely used preprocessing methods in previous research are tokenization, case conversion, stemming, remove punctuation, remove stop word, remove or replace emoji and emoticons, and normalization or slangword conversion. The sentiment labeling process in previous studies calculated based on the comparison of the number of negative sentiment keywords with positive sentiment keywords in one sentence. The maximum accuracy from previous study is 90%. The most widely used lexicon is NRC and Inset which is a lexicon dictionary in Indonesian. Knowledge of this can be used to propose a better model for lexicon based sentiment analysis in Indonesian Languages.

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