Supervised Entity Tagger for Indonesian Labor Strike Tweets using Oversampling Technique and Low Resource Features

Purwarianti, Ayu; Madlberger, Lisa; Ibrahim, M. H.

doi:10.12928/telkomnika.v14i4.3876

Cited by 5 publications

(3 citation statements)

References 8 publications

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“…It was based on a dissertation. One publication (Purwarianti et al 2016), which one of the founders of Prewave has co-authored, introduced an information extraction system, which likely was foundational to the company. It retrieves event information from tweets on upcoming labor strikes in Indonesia to aid in their anticipation and detection.…”

Section: Civil Unrest As Economic Riskmentioning

confidence: 99%

Future Protest Made Risky: Examining Social Media Based Civil Unrest Prediction Research and Products

Grill

2021

Comput Supported Coop Work

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Social media has both been hailed for enabling social movements and critiqued for its affordances as a surveillance infrastructure. In this work, I focus on the latter by analyzing research, products, and discourses around the recent history of civil unrest prediction based on social media data and other public data sources, thereby giving insights into current and often opaque protest surveillance and forecasting practices. Technologies to monitor individuals and groups online have been developed for instance to predict US protests following the election of President Trump in 2016 and labor strikes across global supply chains. These works are part of an emerging computer science research field focused on “civil unrest prediction” dedicated to forecasting protests across the globe (e.g., Indonesia, Brazil, and Australia). Foremost I focus on scholarly literature as my unit of analysis, but also other artifacts discussing or detailing applications for companies, organizations or governments are examined. I provide a conceptualization of civil unrest prediction technology by illustrating data sources, features and methods used, and how prediction and detection are necessarily entangled. Then I show how various kinds of unrest activity are framed as risks to be fixed or averted for various actors with differing interests such as the military, law enforcement, and various industries. Finally, I critically unpack justifications and ascribed benefits of the technology and point to how the perspectives of protestors are almost completely absent. My analysis shows a critical need for regulation centering activists and workers, and reflection within academia, particularly in the fields of computer and data science, on the ethics and politics of protest research and ensuing technological applications.

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Section: Civil Unrest As Economic Riskmentioning

confidence: 99%

Future Protest Made Risky: Examining Social Media Based Civil Unrest Prediction Research and Products

Grill

2021

Comput Supported Coop Work

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“…Social media as a huge data source, provides an opportunity for researchers to perform data mining. Relevant studies have been introduced, including sentiment analysis [5][6][7], named entity recognition [8][9][10], automatic summarization [11][12][13], and user profile clustering [14].…”

Section: Introductionmentioning

confidence: 99%

Personality Modelling of Indonesian Twitter Users with XGBoost Based on the Five Factor Model

Suhartono¹

2021

IJIES

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Over the past few years, there has been an increasing number of researches on automated personality prediction. One of the approaches include analysing personality based on the user's choice of words on social media. Even though research on personality prediction have been done in several different languages, advancements in personality prediction for Bahasa Indonesia, the Indonesian language, has been stagnant. This is due to scarcity of data and the lack of psycholinguistic dictionaries for the language. This is unfortunate as Indonesians are among one of the most active social media consumers, considering more than 2% of worldwide tweets come from Indonesians. We address these issues in this study through the modelling of our own personality prediction model based on an Indonesian dataset that we have produced. We employed the model using the XGBoost machine learning algorithm, trained on 250 user data that we have collected and annotated manually. The resulting model was able to gain decent performance for the Agreeableness and Openness personality trait, achieving AUROC of 0.71 and 0.63, while the Conscientiousness, Extraversion, and Neuroticism personality traits were harder to distinguish with an AUROC of 0.5, 0.59, and 0.48 respectively.

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“…Thus, there are already several researches for Indonesian NE tagger [1,2,3,4,5,6]. Most researches on Indonesian NER employed traditional machine learning algorithms such as association rule [1], ensemble learning [4], and support vector machine (SVM) [2,3,5]. The problem of these researches is the features.…”

Section: Introductionmentioning

confidence: 99%

Investigating Bi-LSTM and CRF with POS Tag Embedding for Indonesian Named Entity Tagger

Hoesen¹,

Purwarianti

2018

2018 International Conference on Asian Language Processing (IALP)

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Researches on Indonesian named entity (NE) tagger have been conducted since years ago. However, most did not use deep learning and instead employed traditional machine learning algorithms such as association rule, support vector machine, random forest, naïve bayes, etc. In those researches, word lists as gazetteers or clue words were provided to enhance the accuracy. Here, we attempt to employ deep learning in our Indonesian NE tagger. We use long short-term memory (LSTM) as the topology since it is the state-of-the-art of NE tagger. By using LSTM, we do not need a word list in order to enhance the accuracy. Basically, there are two main things that we investigate. The first is the output layer of the network: Softmax vs conditional random field (CRF). The second is the usage of part of speech (POS) tag embedding input layer. Using 8400 sentences as the training data and 97 sentences as the evaluation data, we find that using POS tag embedding as additional input improves the performance of our Indonesian NE tagger. As for the comparison between Softmax and CRF, we find that both architectures have a weakness in classifying an NE tag.

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Supervised Entity Tagger for Indonesian Labor Strike Tweets using Oversampling Technique and Low Resource Features

Abstract: We propose an entity tagger for Indonesian tweets sent during labor strike events using supervised learning methods.

Cited by 5 publications

References 8 publications

Future Protest Made Risky: Examining Social Media Based Civil Unrest Prediction Research and Products

Future Protest Made Risky: Examining Social Media Based Civil Unrest Prediction Research and Products

Personality Modelling of Indonesian Twitter Users with XGBoost Based on the Five Factor Model

Investigating Bi-LSTM and CRF with POS Tag Embedding for Indonesian Named Entity Tagger

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