Experimentation Of Various Preprocessing Pipelines For Sentiment Analysis On Twitter Data About New Indonesia’s Capital City Using SVM And CNN

Pebiana, Siska; Hidayati, Nuraisa Novia; Afra, Dian Isnaeni Nurul; Nurfadhilah, Elvira; Prafitia, Harnum Annisa; Prihantoro, Junanto; Fajri, Radhiyatul; Uliniansyah, M. Teduh; Santosa, Agung; Aini, Lyla Ruslana; Sahreza, Yosi; Subekti, Aulia Haritsuddin Karisma Muhammad; Pinem, Josua Geovani; Alfin, Muhammad Reza; Septadi, Agung; Shaleha, Siti; Wibowanto, Gembong Satrio; Jarin, Asril; Gunarso,; Latief, Andi Djalal; Riza, Hammam

doi:10.1109/o-cocosda202257103.2022.9997982

Cited by 7 publications

(2 citation statements)

References 21 publications

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“…We conducted a series of preprocessing phases [7] to generate a dataset (biodiversity_for_modelling.csv) for sentiment classification modeling. The initial stage encompassed converting all text to lowercase to ensure uniformity and eliminate potential stemming discrepancies from capitalization.…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Twitter dataset on public sentiments towards biodiversity policy in Indonesia

Uliniansyah,

Budi,

Nurfadhilah

et al. 2024

Data in Brief

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Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Twitter dataset on public sentiments towards biodiversity policy in Indonesia

Uliniansyah,

Budi,

Nurfadhilah

et al. 2024

Data in Brief

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“…The pre-processing steps include the normalization of slang language, converting text to lowercase, removing numbers, punctuation marks, single characters, and symbols, and eliminating stop words with adverbial sentiment filters, and these steps are similar to our previous study [13]. Slang language normalization aims to standardize informal expressions or jargon in the text.…”

Section: Preprocessing Datamentioning

confidence: 99%

Improving Aspect-Based Sentiment Analysis for Hotel Reviews with Latent Dirichlet Allocation and Machine Learning Algorithms

Hidayati

2023

regist. j. ilm. teknol. sist. inf.

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The rapid expansion of online platforms has resulted in a deluge of user-generated content, emphasizing the need for sentiment analysis to gauge public opinion. Aspect-based sentiment analysis is now essential for uncovering intricate opinions within product reviews, social media posts, and online texts. Despite their potential, the complexity of human emotions and diverse language nuances pose significant challenges. Our study focuses on the importance and trends of sentiment and aspect-based sentiment analysis in automated review analysis, with a primary focus on Indonesian-language hotel reviews. Our research underscores the need for nuanced tools to unravel multifaceted sentiments. We propose an automation framework that utilizes Latent Dirichlet Allocation (LDA) for feature extraction. We evaluate LDA's performance, enhance it through filtration, and enrich it by integrating it with Word2Vec and Doc2Vec. Our methodology encompasses various machine learning algorithms, including Logistic Regression (LR), Stochastic Gradient Descent (SGD), Support Vector Machine (SVM), Random Forest (RF), and Light Gradient Boosting Machine (LGBM). Empirical results reveal that the optimal combination involves LDA bigram and Word2Vec, alongside the LGBM classifier, yielding an average F1 score of 86.6 across ten aspects. This contribution advances automated aspect-based sentiment analysis, offering concrete implications for e-commerce, marketing, and customer service. Our insights inform precise marketing strategies and enhance customer experiences, underscoring the research's relevance in the digital landscape.

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