Sentiment analysis for Malay language: Systematic literature review

Abstract

Recent research and developments in Sentiment Analysis (SA) have simplified sentiment detection and classification from textual content. 

The related domains for these studies are diverse and comprise fields such as tourism, costumer review, finance, software engineering, speech conversation, social media content, news and so on. 

SA research and developments field have been done on various languages such as Chinese and English language. 

However, SA research on other languages such as Malay language is still scarce. 

Thus, there is a need for constructing SA research specifically for Malay language. 

To understand trends and to support practitioners and researchers with comprehension information with regard to SA for Malay language, this study exhibit to review published articles on SA for Malay language. 

From five online databases including ACM, Emerald insight, IEEE Xplore, Science Direct, and Scopus, 2433 scientific articles were obtained. 

Moreover, through the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) Statement, 10 articles have been chosen for the review process. 

Those articles have been reviewed depend on a few categories consisting of the aim of the study, SA classification techniques, as well as the domain and source of content. 

As a result, the conducted systematic literature review shed some light about the starting point to research in term of SA for Malay language.

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https://ieeexplore.ieee.org/abstract/document/8567139

A journey of Indian languages over sentiment analysis: a systematic review

 

.

Abstract

In recent years, due to the availability of voluminous data on web for Indian languages, it has become an important task to analyze this data to retrieve useful information. 

Because of the growth of Indian language content, it is beneficial to utilize this explosion of data for the purpose of sentiment analysis. 

This research depicts a systematic review in the field of sentiment analysis in general and Indian languages specifically. 

The current status of Indian languages in sentiment analysis is classified according to the Indian language families. 

The periodical evolution of Indian languages in the field of sentiment analysis, sources of selected publications on the basis of their relevance are also described. 

Further, taxonomy of Indian languages in sentiment analysis based on techniques, domains, sentiment levels and classes has been presented. 

This research work will assist researchers in finding the available resources such as annotated datasets, pre-processing linguistic and lexical resources in Indian languages for sentiment analysis and will also support in selecting the most suitable sentiment analysis technique in a specific domain along with relevant future research directions. 

In case of resource-poor Indian languages with morphological variations, one encounters problems of performing sentiment analysis due to unavailability of annotated resources, linguistic and lexical tools. 

Therefore, to provide efficient performance using existing sentiment analysis techniques, the aforementioned issues should be addressed effectively.

.

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