Aspect-based Opinion Mining for Code-Mixed Restaurant Reviews in Indonesia

Suciati, Andi; Budi, Indra

doi:10.1109/ialp48816.2019.9037689

Cited by 10 publications

(11 citation statements)

References 16 publications

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“…(2) Machine learning approaches Another type is using machine learning approaches to determine the sentiment polarity of the specific aspect by analyzing the opinion words or contextual contents. There are several traditional machine learning approaches to detect sentiment polarities: Decision Tree (DT) (Suciati and Budi, 2019), Logistic Regression (LR) (Nguyen and Shirai, 2015), Maximum Entropy (ME) (Hercig et al, 2016), SVM (Al-Smadi et al, 2018), Naïve Bayesian Model (NBM) (Afzaal et al, 2019b), LDA (Xu et al, 2013), and CRF (Miao et al, 2021). Deep learning approaches have also been used to determine the sentiment polarity of the specific aspect, including the ones based on Multiple Attention Mechanism Network (MAMN) (Wang et al, 2021), CNN (Wu et al, 2021;Ye et al, 2021), RNN (Chen et al, 2017), LSTM (Song et al, 2019;Liu et al, 2021;Lv et al, 2021), GRU (Ali et al, 2021), and MN (Zhang et al, 2020;Chen et al, 2021).…”

Section: Polarity Detectionmentioning

confidence: 99%

Review of sentiment analysis: An emotional product development view

Yan

2022

Front. Eng. Manag.

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Conveying consumers’ specific emotions in new products, referred to as emotional product development or emotional design, is strategically crucial for manufacturers. Given that sentiment analysis (SA) can extract and analyze people’s opinions, sentiments, attitudes, and perceptions regarding different products/services, SA-based emotional design may provide manufacturers with real-time, direct, and rapid decision support. Despite its considerable advancements and numerous survey and review articles, SA is seldom considered in emotional design. This study is among the first efforts to conduct a thorough review of SA from the view of emotional design. The comprehensive review of aspect-level SA reveals the following: 1) All studies focus on extracting product features by mixing technical product features and consumers’ emotional perceptions. Consequently, such studies cannot capture the relationships between technical and emotional attributes and thus cannot convey specific emotions to the new products. 2) Most studies use the English language in SA, but other languages have recently received more interest in SA. Furthermore, after conceptualizing emotion as Kansei and introducing emotional product development and Kansei Engineering, a review of the data-driven emotional design is then conducted. A few efforts start to study emotional design with the help of SA. However, these studies only focus on either analyzing consumers’ preferences on product features or extracting emotional opinions from online reviews, thus cannot realize data-driven emotional product development. Finally, some research opportunities are provided. This study opens a broad door to aspect-level SA and its integration with emotional product development.

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Section: Polarity Detectionmentioning

confidence: 99%

Review of sentiment analysis: An emotional product development view

Yan

2022

Front. Eng. Manag.

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“…The algorithm that achieved the highest score was obtained by Logistic Regression (LR) and Decision Tree (DT) [22]. Research by Miranda et al (2019) used Bayes classification to process Indonesian text data.…”

Section: Related Workmentioning

confidence: 99%

Effect of Random Splitting and Cross Validation for Indonesian Opinion Mining using Machine Learning Approach

Purba¹,

Ermatita²,

Abdiansah³

et al. 2022

IJACSA

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Opinion mining has been a prominent topic of research in Indonesia, however there are still many unanswered questions. The majority of past research has been on machine learning methods and models. A comparison of the effects of random splitting and cross-validation on processing performance is required. Text data is in Indonesian. The goal of this project is to use a machine learning model to conduct opinion mining on Indonesian text data using a random splitting and cross validation approach. This research consists of five stages: data collection, pre-processing, feature extraction, training & testing, and evaluation. Based on the experimental results, the TF-IDF feature is better than the Count-Vectorizer (CV) for Indonesian text. The best accuracy results are obtained by using TF-IDF as a feature and Support Vector Machine (SVM) as a classifier with cross validation implementation. The best accuracy reaches 81%. From the experimental results, it can also be seen that the implementation of cross validation can improve accuracy compared to the implementation of random splitting.

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“…After the annotation process, the next stage is data preprocessing. This stage adapted the research from [17] and consists few steps, which are: 1) Emoticon Processing: In this step, emoticon characters, such as: :( was changed into "sad", and :) into "happy". This was conducted to avoid losing the information about the emoticon.…”

Section: Data Preprocessingmentioning

confidence: 99%

“…For illustration, "I"ve visted the place, that wasn"t too crowd" was corrected into "i have visted the place, that was not too crowd". We used the abreviation dictionary that id selfdeveloped by [17], and contains abreviations from indonesian and english.…”

Section: Data Preprocessingmentioning

confidence: 99%

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Aspect-Based Sentiment Analysis and Emotion Detection for Code-Mixed Review

Suciati

Budi²

2020

IJACSA

Self Cite

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Review can affect customer decision making because by reading it, people manage to know whether the review is positive, or negative. However, positive, negative, and neutral, without considering the emotion will be not enough because emotion can strengthen the sentiment result. This study explains about the comparison of machine learning and deep learning in sentiment as well as emotion classification with multilabel classification. In machine learning comparison, the problem transformation that we used are Binary Relevance (BR), Classifier Chain (CC), and Label Powerset (LP), with Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Extra Tree Classifier (ET) as algorithms of machine learning. The features we compared are n-gram language model (unigram, bigram, unigram-bigram). For deep learning, algorithms that we applied are Gated Recurrent Unit (GRU) and Bidirectional Long Short-Term Memory (BiLSTM), using selfdeveloped word embedding. The comparison results show RF dominates with 88.4% and 89.54% F1 scores with CC method for food aspect, and LP for price, respectively. For service and ambience aspects, ET leads with 92.65% and 87.1% with LP and CC methods, respectively. On the other hand, in deep learning comparison, GRU and BiLSTM obtained similar F1-score for food aspect, 88.16%. On price aspect, GRU leads with 83.01%. However, for service and ambience, BiLSTM achieved higher F1score, 89.03% and 84.78%.

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Aspect-based Opinion Mining for Code-Mixed Restaurant Reviews in Indonesia

Cited by 10 publications

References 16 publications

Review of sentiment analysis: An emotional product development view

Review of sentiment analysis: An emotional product development view

Effect of Random Splitting and Cross Validation for Indonesian Opinion Mining using Machine Learning Approach

Aspect-Based Sentiment Analysis and Emotion Detection for Code-Mixed Review

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