Aspect Based Sentiment Analysis With Combination Feature Extraction LDA and Word2vec

Sudiro, Rizka Vio Octriany Inggit; Prasetiyowati, Sri Suryani; Sibaroni, Yuliant

doi:10.1109/icoict52021.2021.9527506

Cited by 2 publications

(3 citation statements)

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“…For femaledaily.com product reviews, which cover diverse topics including cost, packaging, and scent, a study in the Indonesian language also utilized word embedding as an additional feature extraction technique. Combining LDA with Word2Vec CBOW and Skip-gram models yields the most accurate results, as confirmed by an SVM classifier [9]. In these studies, LDA categorizes reviews by topic, which are considered as aspects.…”

Section: Introductionmentioning

confidence: 67%

“…In the Skip-gram mode, word vectors are generated by employing contextual word vectors as input through a neural network learning procedure [16]. Previous research [9] has demonstrated that the Skip-gram method is more accurate than CBOW when combined with a linear parameter for SVM in the Skip-gram approach. Therefore, in this study, we opted for the Skip-gram method due to its ability to consider words and their contexts independently, contributing to its accuracy and effectiveness.…”

Section: Train Word2vec Modelmentioning

confidence: 99%

“…, a generative probabilistic model, is employed to uncover latent topics in a collection of documents. Previous research has utilized LDA as an extraction characteristic, as discussed in the relevant section[6][7][8][9]. According to a study, LDA outperforms TFIDF-…”

mentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 67%

Section: Train Word2vec Modelmentioning

confidence: 99%