Towards A Sentiment Analyzer for Low-Resource Languages

Indriani, Dian and Nasution, Arbi Haza and Monika, Winda and Nasution, Salhazan (2022) Towards A Sentiment Analyzer for Low-Resource Languages. In: International conference on smart computing and cyber security : strategic foresight, security challenges and innovation SMARTCYBER 2020, July 7 - 8, 2020, Online.

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Twitter is one of the top in uenced social media which has a million number of active users. It is commonly used for microblogging that allows users to share messages, ideas, thoughts and many more. Thus, millions interaction such as short messages or tweets are owing around among the twitter users discussing various topics that has been happening world-wide. This research aims to analyse a sentiment of the users towards a particular trending topic that has been actively and massively discussed at that time.We chose a hashtag #kpujangancurang that was the trending topic during the Indonesia presidential election in 2019. We use the hashtag to obtain a set of data from Twitter to analyse and investigate further the positive or the negative sentiment of the users from their tweets. This research utilizes rapid miner tool to generate the twitter data and comparing Naive Bayes, K-Nearest Neighbor, Decision Tree, and Multi-Layer Perceptron classi�cation methods to classify the sentiment of the twitter data. There are overall 200 labeled data in this experiment. Overall, Naive Bayes and Multi-Layer Perceptron classi �cation outperformed the other two methods on 11 experiments with di�erent size of training-testing data split. The two classi�ers are potential to be used in creating sentiment analyzer for low-resource languages with small corpus.

Item Type: Conference or Workshop Item (Paper)
Subjects: T Technology > T Technology (General)
Divisions: > Teknik Informatika
> Teknik Informatika
Depositing User: Monika Winda Monika
Date Deposited: 19 May 2023 09:04
Last Modified: 19 May 2023 09:04

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