Sentiment analysis system for movie review in Bahasa Indonesia using naive bayes classifier method

Nurdiansyah, Yanuar; Bukhori, Saiful; Hidayat, Rahmad

doi:10.1088/1742-6596/1008/1/012011

Cited by 34 publications

(26 citation statements)

References 1 publication

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“…Sentiment analysis. There has been sentiment analysis for Indonesian domains/data sources including presidential elections (Ibrahim et al, 2015), stock prices (Cakra and Trisedya, 2015), Twitter (Koto and Rahmaningtyas, 2017), and movie reviews (Nurdiansyah et al, 2018). Most previous work, however, has used non-public and low-resource datasets.…”

Section: Semantic Tasksmentioning

confidence: 99%

IndoLEM and IndoBERT: A Benchmark Dataset and Pre-trained Language Model for Indonesian NLP

Koto¹,

Rahimi²,

Lau³

et al. 2020

Proceedings of the 28th International Conference on Computational Linguistics

133

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Although the Indonesian language is spoken by almost 200 million people and the 10th mostspoken language in the world, 1 it is under-represented in NLP research. Previous work on Indonesian has been hampered by a lack of annotated datasets, a sparsity of language resources, and a lack of resource standardization. In this work, we release the INDOLEM dataset comprising seven tasks for the Indonesian language, spanning morpho-syntax, semantics, and discourse. We additionally release INDOBERT, a new pre-trained language model for Indonesian, and evaluate it over INDOLEM, in addition to benchmarking it against existing resources. Our experiments show that INDOBERT achieves state-of-the-art performance over most of the tasks in INDOLEM.

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Section: Semantic Tasksmentioning

confidence: 99%

IndoLEM and IndoBERT: A Benchmark Dataset and Pre-trained Language Model for Indonesian NLP

Koto¹,

Rahimi²,

Lau³

et al. 2020

Proceedings of the 28th International Conference on Computational Linguistics

133

View full text Add to dashboard Cite

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“…There are two methods namely the Chi-Square test and the Gini Index which are frequently used. DIA and CMFS are also used in some of the situations (Nurdiansyah, 2018).…”

Section: Modified Max Entropy Classifiermentioning

confidence: 99%

A SERVQUAL based E-Commerce platform for Indian customers

2021

cibg

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The customer reviews and ratings are now part of the purchasing process on E-Commerce platforms. It helps online businesses from knowing the goods but also influences companies to rectify the lacunas in buying process and to give better services to the customers. Nevertheless, poor reviews are a great deal to enhance the quality of the goods and services. That means a lot for the improvement of the products is needed. Most of the time bad reviews and ratings are outcomes of the problems faced by the customer after the delivery of the products. The customer expects a repair facility after the warranty period of the products. Often customers expect the repair facility like a service engineer to be close to their home. The key issue is that there is no shared forum to provide all of the product services. Online businesses must consider the effect of the psychology of the customers on their company. The paper recommends SERVQUAL for improvements in Indian E-Commerce like pre-purchase product comparisons, repair facility to every product after warrantee, and a helpdesk with a real-time conversation facility to resolve the issues in services for any brands in the existing E-Commerce structure based on customer's reviews and ratings only.

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“…For supervised document-level opinion mining, Al-Amrani et al (2017) presented a comparison of five supervised classification algorithms to identify the optimal classifiers. Nurdiansyah et al (2018) conducted opinion classification on movie reviews based on a naïve Bayes classifier. Troussas et al (2019) analysed sentiments of Twitter data sets and proved that the use of ensembles of multiple base classifiers can improve the accuracy of sentiment analysis.…”

Section: Related Workmentioning

confidence: 99%

Multidimensional mining of public opinion in emergency events

Zhou

Jing

2020

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Purpose The suddenness, urgency and social publicity of emergency events lead to great impacts on public life. The deep analysis of emergency events can provide detailed and comprehensive information for the public to get trends of events timely. With the development of social media, users prefer to express opinions on emergency events online. Thus, massive public opinion information of emergencies has been generated. Hence, this paper aims to conduct multidimensional mining on emergency events based on user-generated contents, so as to obtain finer-grained results. Design/methodology/approach This paper conducted public opinion analysis via fine-grained mining. Specifically, public opinion about an emergency event was collected as experimental data. Secondly, opinion mining was conducted to get users’ opinion polarities. Meanwhile, users’ information was analysed to identify impacts of users’ characteristics on public opinion. Findings The experimental results indicate that public opinion is mainly negative in emergencies. Meanwhile, users in developed regions are more active in expressing opinions. In addition, male users, especially male users with high influence, are more rational in public opinion expression. Originality/value To the best of the authors’ knowledge, this is the first research to identify public opinion in emergency events from multiple dimensions, which can get in-detail differences of users’ online expression.

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Sentiment analysis system for movie review in Bahasa Indonesia using naive bayes classifier method

Cited by 34 publications

References 1 publication

IndoLEM and IndoBERT: A Benchmark Dataset and Pre-trained Language Model for Indonesian NLP

IndoLEM and IndoBERT: A Benchmark Dataset and Pre-trained Language Model for Indonesian NLP

A SERVQUAL based E-Commerce platform for Indian customers

Multidimensional mining of public opinion in emergency events

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