Sentiment Analysis and Topic Modelling of The COVID-19 Vaccine in Indonesia on Twitter Social Media Using Word Embedding

Agustiningsih, Kartikasari Kusuma; Utami, Ema; Alsyaibani, Omar Muhamammad Altoumi

doi:10.26555/jiteki.v8i1.23009

Cited by 3 publications

(5 citation statements)

References 21 publications

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“…Machine Learning classifiers deployed, trained, and tested in this work were Logistic regression [30], Support Vector Machine (SVM) [31], K-nearest neighbors (KNN) [32], Decision Tree [33], Stochastic Gradient Descent (SGD) [34], and Multinomial Naive Bayes [35]. In the ensemble learning category, several models were applied to do the same task such as Voting Classifiers [36], Random Forest [37], Bagging Meta-Estimator [38], AdaBoost [39], XGBoost [40], Gradient Boosting [41], and Light Gradient Boosting Machine (LightGBM) [42]. In the deep learning category, different large language models (LLMs) were used such as Bert [43 & 44], RoBERTa [45 & 46], GPT [47 & 48], and GPT2 [49].…”

Section: The Proposed Approachmentioning

confidence: 99%

Detection and prediction of Future Mental disorder from Social Media Data using Machine Learning, Ensemble Learning, and Large Language Models.

Abdullah,

Negied

2024

IEEE Access

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Social media platforms are used widely by all people to express their feelings, opinions, and emotional states. Billions of people worldwide use them daily to share what they think and feel in their posts. Amongst all social media available platforms, Facebook only contains around three billion personal accounts. In this work Reddit dataset is used to automatically detect mental illness from social media posts. This study is not only limited to early detection of already existing mental illness or disorder like depression and anxiety from social posts, but also and most importantly the study is extended to predict successfully potential mental illness that would happen in future. This study deploys Nineteen different models to study the capability of them in detecting and predicting mental disorders from social media posts. Some of the deployed models are classical machine learning classifiers, some are ensemble learning models, and the rest are large language models (LLMs). Six machine learning classifiers were used in this work for the automatic detection and prediction of mental illness and logistic regression proved to be the best amongst other classifiers in this task. Nine Ensemble methods were also used and examined. Amongst the Nine ensemble learning models VC2, Light GBM, Bagging estimator, and XGBoost proved to be superior in this task. Four large language models were also used and examined for the same task. RoBERTa and OpenAI GPT proved to outperform the rest of models in this task. All those models were built, trained, tested, and compared with previous work in literature to get the best possible results. The study covers the main four mental disorders which are ADHD, Anxiety, Bipolar, and Depression. The work proposed in this paper succeeded in outperforming the results in literature in terms of number of addressed mental disorders, number of models used and tested, and dataset size used to validate results. The proposed work also outperformed the only attempt in literature that addressed all mental disorders in results of detection and prediction noticeably. This work achieved the detection of already existing mental disorders F1-score of 0.80 from clinical data and of 0.52 from non-clinical data, and it achieved a prediction of future mental disorder F1-score of 0.43 from non-clinical data.

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Section: The Proposed Approachmentioning

confidence: 99%

Detection and prediction of Future Mental disorder from Social Media Data using Machine Learning, Ensemble Learning, and Large Language Models.

Abdullah,

Negied

2024

IEEE Access

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“…Pre-Trained word embedding is a vectorization process to convert words into vectors where the vector represents words that have been changed before [9], [18], [21], [22]. This process is needed because it aims to convert words into vectors so that they can be processed by the model [23]. The dimension of the resulting vector can be determined.…”

Section: Pre-trained Word Embeddingmentioning

confidence: 99%

“…The maximum number of words in this study is 123 and the dimensions used have a dimension length of 300. This study uses the FastText framework because it is more efficient and faster than Glove and Word2vec [18], [23]. FastText is a library owned and managed by fecabook that functions to represent words efficiently and support the text classification process [23].…”

Section: Pre-trained Word Embeddingmentioning

confidence: 99%

“…This study uses the FastText framework because it is more efficient and faster than Glove and Word2vec [18], [23]. FastText is a library owned and managed by fecabook that functions to represent words efficiently and support the text classification process [23]. After this process is carried out, 26253 words are found in the wiki vocab and 42760 new words are found in the dataset.…”

Section: Pre-trained Word Embeddingmentioning

confidence: 99%

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Comparative Analysis of Long Short-Term Memory Architecture for Text Classification

Fajar Abdillah,

Kusnawi

2023

Ilk. J. Ilm.

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Text classification which is a part of NLP is a grouping of objects in the form of text based on certain characteristics that show similarities between one document and another. One of methods used in text classification is LSTM. The performance of the LSTM method itself is influenced by several things such as datasets, architecture, and tools used to classify text. On this occasion, researchers analyse the effect of the number of layers in the LSTM architecture on the performance generated by the LSTM method. This research uses IMDB movie reviews data with a total of 50,000 data. The data consists of positive, negative data and there is data that does not yet have a label. IMDB Movie Reviews data go through several stages as follows: Data collection, data pre-processing, conversion to numerical format, text embedding using the pre-trained word embedding model: Fastext, train and test classification model using LSTM, finally validate and test the model so that the results are obtained from the stages of this research. The results of this study show that the one-layer LSTM architecture has the best accuracy compared to two-layer and three-layer LSTM with training accuracy and testing accuracy of one-layer LSTM which are 0.856 and 0.867. While the training accuracy and testing accuracy on two-layer LSTM are 0.846 and 0.854, the training accuracy and testing accuracy on three layers are 0.848 and 864.

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“…Marec et al [ 117 ] used concepts of sentiment analysis to detect public sentiments about specific COVID-19 vaccines, such as AstraZeneca/Oxford, Pfizer/BioNTech, and Moderna. The works of Nezhad et al [ 118 ], Agustiningsih et al [ 119 ], and Ponmani et al [ 120 ] presented the results of performing sentiment analysis of Tweets about COVID-19 vaccines from Iran, Indonesia, and India, respectively. In addition to this, the sentiment analysis of relevant Tweets during the COVID-19 pandemic was performed to detect the sentiments of people towards remote work [ 121 ], online education [ 122 ], social distancing [ 123 ], wearing masks [ 124 ], and vaccine boosters [ 125 ].…”

Section: Literature Reviewmentioning

confidence: 99%

MonkeyPox2022Tweets: A Large-Scale Twitter Dataset on the 2022 Monkeypox Outbreak, Findings from Analysis of Tweets, and Open Research Questions

Thakur

2022

Infectious Disease Reports

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The mining of Tweets to develop datasets on recent issues, global challenges, pandemics, virus outbreaks, emerging technologies, and trending matters has been of significant interest to the scientific community in the recent past, as such datasets serve as a rich data resource for the investigation of different research questions. Furthermore, the virus outbreaks of the past, such as COVID-19, Ebola, Zika virus, and flu, just to name a few, were associated with various works related to the analysis of the multimodal components of Tweets to infer the different characteristics of conversations on Twitter related to these respective outbreaks. The ongoing outbreak of the monkeypox virus, declared a Global Public Health Emergency (GPHE) by the World Health Organization (WHO), has resulted in a surge of conversations about this outbreak on Twitter, which is resulting in the generation of tremendous amounts of Big Data. There has been no prior work in this field thus far that has focused on mining such conversations to develop a Twitter dataset. Furthermore, no prior work has focused on performing a comprehensive analysis of Tweets about this ongoing outbreak. To address these challenges, this work makes three scientific contributions to this field. First, it presents an open-access dataset of 556,427 Tweets about monkeypox that have been posted on Twitter since the first detected case of this outbreak. A comparative study is also presented that compares this dataset with 36 prior works in this field that focused on the development of Twitter datasets to further uphold the novelty, relevance, and usefulness of this dataset. Second, the paper reports the results of a comprehensive analysis of the Tweets of this dataset. This analysis presents several novel findings; for instance, out of all the 34 languages supported by Twitter, English has been the most used language to post Tweets about monkeypox, about 40,000 Tweets related to monkeypox were posted on the day WHO declared monkeypox as a GPHE, a total of 5470 distinct hashtags have been used on Twitter about this outbreak out of which #monkeypox is the most used hashtag, and Twitter for iPhone has been the leading source of Tweets about the outbreak. The sentiment analysis of the Tweets was also performed, and the results show that despite a lot of discussions, debate, opinions, information, and misinformation, on Twitter on various topics in this regard, such as monkeypox and the LGBTQI+ community, monkeypox and COVID-19, vaccines for monkeypox, etc., “neutral” sentiment was present in most of the Tweets. It was followed by “negative” and “positive” sentiments, respectively. Finally, to support research and development in this field, the paper presents a list of 50 open research questions related to the outbreak in the areas of Big Data, Data Mining, Natural Language Processing, and Machine Learning that may be investigated based on this dataset.

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Sentiment Analysis and Topic Modelling of The COVID-19 Vaccine in Indonesia on Twitter Social Media Using Word Embedding

Cited by 3 publications

References 21 publications

Detection and prediction of Future Mental disorder from Social Media Data using Machine Learning, Ensemble Learning, and Large Language Models.

Detection and prediction of Future Mental disorder from Social Media Data using Machine Learning, Ensemble Learning, and Large Language Models.

Comparative Analysis of Long Short-Term Memory Architecture for Text Classification

MonkeyPox2022Tweets: A Large-Scale Twitter Dataset on the 2022 Monkeypox Outbreak, Findings from Analysis of Tweets, and Open Research Questions

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