Sarcasm Detection on Indonesian Twitter Feeds

Rahayu, Dwi; Kuntur, Soveatin; Hayatin, Nur

doi:10.11591/eecsi.v5.1724

Cited by 5 publications

(8 citation statements)

References 5 publications

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“…These additional features helped in improving the accuracy of their model by about 4%. [4] tackled the sarcasm detection technique on Indonesia sentences by combining punctuation and interjection feature extraction methods with two different weighting methods and two classification algorithms. They used a pre-processing and stemming algorithm that is designed for the Indonesian language.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Other languages like Indonesian are still falling behind in improvement compared to English. This fact can be seen by the lack of publications on Indonesian sarcasm detection studies [4].…”

Section: Introductionmentioning

confidence: 99%

“…The majority of the studies, especially the ones in Indonesian, use machine learning methods such as support vector machine, naive bayes, or k-nearest neighbors [4], [7], [8]. These methods usually take a lot of time on hand engineering features of the data to further increase the accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Indonesian Sarcasm Detection Using Convolutional Neural Network

2020

IJETER

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In sentiment analysis, sarcasm is considered to be one of the most difficult problems to solve. Sarcasm or verbal irony is a linguistic phenomenon where the wording of a speaker signifies the opposite of the literal meaning of what they are saying. On social media, Indonesians tend to use sarcastic remarks to criticize something and this behavior can pose a problem when a sentiment analysis is performed where the analysis yields false-positive results. To solve this potential problem, the detection of sarcasm is necessary. A convolutional neural network model is used in this paper to handle the automated identification of sarcasm in Indonesian. Although it is mainly used in computer vision, this model has shown promising results on specific kinds of sentiment analysis and classification problems compared to other neural network models. Using this model can increase detection accuracy compared to discrete models. There have been many studies conducted on sarcasm detection, although the majority is in English. Other languages such as Indonesian are slow in improvement compared to English. This fact can be seen by the lack of publications on Indonesian sarcasm detection studies. This study is conducted to contribute to other Indonesian sarcasm detection studies.

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Section: Literature Reviewmentioning

confidence: 99%

“…Other languages like Indonesian are still falling behind in improvement compared to English. This fact can be seen by the lack of publications on Indonesian sarcasm detection studies [4].…”

Section: Introductionmentioning

confidence: 99%

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Indonesian Sarcasm Detection Using Convolutional Neural Network

2020

IJETER

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“…In a sentiment, some expressions contradict what they mean. The different meanings and expressions are called sarcasm [5]. The existence of sarcasm in tweets is challenging to detect automatically by a system, even by humans, because of textual data in tonal and genital instructions such as speech tone pressure, eye friction, hand movements, and whether it cannot be detected [6].…”

Section: Introductionmentioning

confidence: 99%

Sarcasm Detection Engine for Twitter Sentiment Analysis using Textual and Emoji Feature

Wiguna

Hudiyanti

Rausanfita

et al. 2021

Jurnal Ilmu Komputer dan Informasi

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Twitter is a social media platform that is used to express sentiments about events, topics, individuals, and groups. Sentiments in Tweets can be classified as positive or negative expressions. However, in sentiment, there is an expression that is actually the opposite of what is mean to be, and this is called sarcasm. The existence of sarcasm in a Tweet is difficult to detect automatically by a system even by humans. In this research, we propose a weighting scheme based on inconsistency between sentimen of tweet contain in Indonesian and the usage of emoji. With the weighting scheme for the detection of sarcasm, it can be used to find out a sentiment about a event, topic, individual, group, or product's review. The proposed method is by calculating the distance between the textual feature polarity score obtained from the Convolutional Neural Network and the emoji polarity score in a Tweet. This method is used to find the boundary value between Tweets that contain sarcasm or not. The experimental results of the model developed, obtained f1-score 87.5%, precision 90.5% and recall 84.8%. By using the textual features and emoji models, it can detect sarcasm in a Tweet.

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“…Istilah big data ini merujuk pada data yang memiliki volume yang besar, cepat, bernilai, beragam dan kompleks, contohnya adalah data dari media sosial [1]. Twitter merupakan media sosial (medsos) yang datanya sudah banyak digunakan untuk beberapa kajian sosial seperti opini publik, nilain interaksi sosial, ketertarikan publik, sentimen politik, identifikasi sarkas bahkan mobilitas penduduk [2, 3,4,5,6,7]. Hal ini dikarenakan twitter memiliki kemudahan untuk membaca, menulis dan mengumpulkan data yang mengandung informasi temporal dan spasial [8].…”

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Pemanfaatan Data Twitter Dalam Penanggulangan Bencana Banjir dan Longsor

Fariz

Suhardono

Verdiana³

2021

CogITo Smart Journal

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Big data seperti data twitter sudah cukup banyak digunakan dalam kajian ilmu sosial, tetapi penggunaannya dalam kajian bencana masih jarang di Indonesia. Sehingga tujuan dari penelitian ini adalah mencari tahu informasi apa saja yang dapat diidentifikasi melalui data media sosial twitter. Data yang digunakan dalam penelitian ini adalah data kualitatif dari angket dan data twitter dari sistem big data bernama Drone Emprit Academic (DEA). Data twitter yang bergeotag juga divisualisasikan dalam bentuk peta untuk membantu proses analisis deskripsi yang merupakan analisis utama dalam penelitian ini. Hasil dari penelitian ini menunjukan bahwa di twitter bencana banjir lebih mendapat perhatian ketimbang bencana longsor. Pada penyelenggaraan penanggulan bencana, kami berpendapat bahwa data twitter dapat digunakan pada semua fase. Data twitter dalam penanggulangan bencana terutama bencana hidrometereologis memiliki kelemahan seperti hanya bisa digunakan pada kota besar di Pulau Jawa. Kelemahan lainnya adalah informasi dari data twitter belum dipastikan apakah mewakili karakteristik demografi yang sama dengan kondisi dilapangan serta tingkat kevalidan informasi yang belum bisa dipertanggungjawabkan.Kata kunci— Twitter, Media sosial, Bencana, Banjir, Longsor

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Sarcasm Detection on Indonesian Twitter Feeds

Cited by 5 publications

References 5 publications

Indonesian Sarcasm Detection Using Convolutional Neural Network

Indonesian Sarcasm Detection Using Convolutional Neural Network

Sarcasm Detection Engine for Twitter Sentiment Analysis using Textual and Emoji Feature

Pemanfaatan Data Twitter Dalam Penanggulangan Bencana Banjir dan Longsor

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