Towards A Sentiment Analyzer for Low-Resource Languages

TL;DR

This paper investigates sentiment analysis on low-resource languages using Twitter data, comparing machine learning classifiers to identify effective methods for small datasets, with potential applications in low-resource language sentiment analysis.

Contribution

It introduces a sentiment analysis approach for low-resource languages using Twitter data and compares multiple classifiers to find effective methods with small datasets.

Findings

Naive Bayes and Multi-Layer Perceptron outperform other classifiers

Effective sentiment classification achieved with small datasets

Potential for low-resource language sentiment analysis

Abstract

Twitter is one of the top influenced 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 flowing 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 \textit{\#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 classification 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 classification outperformed the other two methods on 11 experiments with different size of training-testing data split. The two classifiers are potential to be used in creating sentiment analyzer for low-resource languages with small corpus.