A Semi-Supervised Approach to Sentiment Analysis of Tweets during the 2022 Philippine Presidential Election

Macrohon, Julio Jerison E.; Villavicencio, Charlyn Nayve; Inbaraj, X. Alphonse; Jeng, Jyh-Horng

doi:10.3390/info13100484

Cited by 16 publications

(13 citation statements)

References 11 publications

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“…The data cleaning and preprocessing technique employed natural language processing (NLP) to attain classification outcomes with high accuracy, as this processing method is crucial for the computer's understanding of data [45]. In this phase, various libraries, including Google Colab, nltk, pandas, spacy, and the Indonesian Sastrawi library were utilized for preprocessing.…”

Section: Preprocessingmentioning

confidence: 99%

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A Hybrid Sampling Approach for Improving the Classification of Imbalanced Data Using ROS and NCL Methods

2023

IJIES

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This research presents a novel hybrid sampling technique, implemented at the data level, to effectively address imbalanced and noisy data in classification processes. The proposed technique expertly combines two established methods, namely, the random over sampling (ROS) and neighbourhood cleaning rule (NCL) approaches, to tackle imbalance and noise issues, respectively. The study carried out an empirical evaluation of the proposed approach using crowdsourced text data that primarily emphasized the triple bottom line (TBL) dimension of a smart social, economic, and environmental city. The study used the long short-term memory (LSTM), convolutional neural networks (CNN), and CNN-LSTM classification models to validate the efficacy of the proposed hybrid sampling technique and compare its performance with other existing approaches, including ROS oversampling, NCL undersampling, synthetic minority over sampling & tomek links (SMOTE-Tomek), and synthetic minority oversampling and edited nearest neighbours (SMOTE-ENN) hybrid sampling. The results are impressive, with the ROS-NCL hybrid sampling technique achieving high accuracy rates across all three classification models, at 97.71%, 98.01%, and 98.11%, respectively. This approach provides a robust and effective solution for handling impure data and holds great promise in identifying complex data patterns in real-world classification problems.

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Section: Preprocessingmentioning

confidence: 99%

“…For instance, "Saya" and "saya" were considered the same. This stage aimed to reduce the differences between lowercase, uppercase, and capital letters when vectoring [45]. (3) Stop-word removal eliminated meaningless words and was carried out using the stopwords() library provided by NLTK through the Sastrawi tool.…”

Section: Preprocessingmentioning

confidence: 99%

A Hybrid Sampling Approach for Improving the Classification of Imbalanced Data Using ROS and NCL Methods

2023

IJIES

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“…Additionally, up to 250 replies from each tweet were queried which resulted in a total number of 8,362,555 replies. Moreover, Macrohon et al [14] proposed a semi-supervised sentiment analysis using multinomial Naive Bayes of tweets during the 2022 Philippine Presidential Election. A total of 150,792 raw tweets were collected from Twitter API.…”

Section: A Political Discourse In the Internetmentioning

confidence: 99%

“…The complex and monographically rich nature of the Nepali language makes Natural Language Processing (NLP) tasks particularly challenging for this language [12]. Unlike languages such as English, which has ample resources and a plethora of NLP studies [13], [14], research in the field of low-resource languages such as Nepali is scarce [15], [16]. There have been some works involving sentimental analysis in Nepali [12], [17], [18].…”

Section: Introductionmentioning

confidence: 99%

Multi-Aspect Annotation and Analysis of Nepali Tweets on Anti-Establishment Election Discourse

Rauniyar,

Poudel,

Shiwakoti

et al. 2023

IEEE Access

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In today's social media-dominated landscape, digital platforms wield substantial influence over public opinion, particularly during crucial political events such as electoral processes. These platforms become hubs for diverse discussions, encompassing topics, reforms, and desired changes. Notably, in times of government dissatisfaction, they serve as arenas for anti-establishment discourse, highlighting the need to analyze public sentiment in these conversations. However, the analysis of such discourse is notably scarce, even in high-resource languages, and entirely non-existent in the context of the Nepali language. To address this critical gap, we present Nepal Anti Establishment discourse Tweets (NAET), a novel dataset comprising 4,445 multi-aspect annotated Nepali tweets, facilitating a comprehensive understanding of political conversations. Our contributions encompass evaluating tweet relevance, sentiment, and satire, while also exploring the presence of hate speech, identifying its targets, and distinguishing directed and non-directed expressions. Additionally, we investigate hope speech, an underexplored aspect crucial in the context of antiestablishment discourse, as it reflects the aspirations and expectations from new political figures and parties. Furthermore, we set NLP-based baselines for all these tasks. To ensure a holistic analysis, we also employ topic modeling, a powerful technique that helps us identify and understand the prevalent themes and patterns emerging from the discourse. Our research thus presents a comprehensive and multi-faceted perspective on anti-establishment election discourse in a low-resource language setting. The dataset is publicly available, facilitating in-depth analysis of political tweets in Nepali discourse and further advancing NLP research for the Nepali language through labeled data and baselines for various NLP tasks. The dataset for this work is made available at https://github.com/rkritesh210/NAET.

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“…This study used a semi-supervised approach in machine learning, which involved the use of the supervised machine learning models and augmenting the said model, especially in some circumstances when data are weak or where vast quantities of data are unlabeled [ 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

A Semi-Supervised Machine Learning Approach in Predicting High-Risk Pregnancies in the Philippines

et al. 2022

Self Cite

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Early risk tagging is crucial in maternal health, especially because it threatens both the mother and the long-term development of the baby. By tagging high-risk pregnancies, mothers would be given extra care before, during, and after pregnancies, thus reducing the risk of complications. In the Philippines, where the fertility rate is high, especially among the youth, awareness of risks can significantly contribute to the overall outcome of the pregnancy and, to an extent, the Maternal mortality rate. Although supervised machine learning models have ubiquity as predictors, there is a gap when data are weak or scarce. Using limited collected data from the municipality of Daraga in Albay, the study first compared multiple supervised machine learning algorithms to analyze and accurately predict high-risk pregnancies. Through hyperparameter tuning, supervised learning algorithms such as Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors, Naïve Bayes, and Multilayer Perceptron were evaluated by using 10-fold cross validation to obtain the best parameters with the best scores. The results show that Decision Tree bested other algorithms and attained a test score of 93.70%. To address the gap, a semi-supervised approach using a Self-Training model was applied to the modified Decision Tree, which was then used as the base estimator with a 30% unlabeled dataset and achieved a 97.01% accuracy rate which outweighs similar studies.

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A Semi-Supervised Approach to Sentiment Analysis of Tweets during the 2022 Philippine Presidential Election

Cited by 16 publications

References 11 publications

A Hybrid Sampling Approach for Improving the Classification of Imbalanced Data Using ROS and NCL Methods

A Hybrid Sampling Approach for Improving the Classification of Imbalanced Data Using ROS and NCL Methods

Multi-Aspect Annotation and Analysis of Nepali Tweets on Anti-Establishment Election Discourse

A Semi-Supervised Machine Learning Approach in Predicting High-Risk Pregnancies in the Philippines

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