Aspect Based Sentiment Analysis for Restaurant Reviews Using Hybrid ELMoWikipedia and Hybrid Expanded Opinion Lexicon-SentiCircle

Nurifan, Farza; Sarno, Riyanarto; Sungkono, Kelly Rossa

doi:10.22266/ijies2019.1231.05

Cited by 20 publications

(17 citation statements)

References 26 publications

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“…These aspect categories are taken from the review of the aspect rating data on the TripAdvisor application. As has been explained about the definition of aspect categories [6] and the aspect category variables [14] so we used several keywords for four aspect categories in this research from Wikipedia. Table 1 below shows the aspect categories and variables that used in this research.…”

Section: Aspect Category Abstractmentioning

confidence: 99%

Aspect-Based Sentiment Analysis for Sentence Types with Implicit Aspect and Explicit Opinion in Restaurant Review Using Grammatical Rules, Hybrid Approach, and SentiCircle

Suhariyanto¹,

Abdullah²,

Sarno³

et al. 2021

IJIES

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Sentiment analysis can provide rough recommendations in the form of sentiment from a collection of reviews or can provide recommendations in more detail about sentiment in a particular aspect called aspect-based sentiment analysis (ABSA). Sentiment analysis based on many aspects has been carried out but its accuracy is still being developed. In previous research, most research was carried out on explicit and implicit aspects and opinions and was carried out in simple sentences. The purpose of this research is to analyze the sentiment of restaurant reviews using the rule grammar method to extract implicit aspects -explicit opinions in four sentence models, namely simple (Si-AIOE), compound (Co-AIOE), complex (Ce-AIOE), and compound-complex (CoCe-AIOE). The ABSA method is proposed using the development of a grammatical rule extraction method to extract explicit and implicit aspect words and opinion words as the basis for sentence extraction. Rules making is done to take explicit and implicit aspect words and opinion words in Si-AIOE, Co-AIOE, Ce-AIOE, and CoCe-AIOE so that the comparison of the evaluation values can be known. This research uses the Semeval 2015 dataset on Restaurant reviews from the Tripadvisor Website which has been annotated as sentence data for ABSA. The aspect categorization process is then used to categorize aspects into 4 aspect categories, namely Ambience, Food, Service, and Price using hybrid approach. The hybrid approach is combined using Elmo-Wikipedia, grammatical rule extraction, WordNet, TF-ICF, and semantic similarity methods. The results of the aspect extraction obtained value of precision, recall, and f1-measure of 0.80, 0.84, and 0.82, respectively. Meanwhile, the ABSA process uses SentiCircle to classify sentiments into two, namely positive and negative. The results of the ABSA showed that the performance of proposed method achieve for precision, recall, and f1-measure were 0.84, 0.89, and 0.87, respectively.

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Section: Aspect Category Abstractmentioning

confidence: 99%

Aspect-Based Sentiment Analysis for Sentence Types with Implicit Aspect and Explicit Opinion in Restaurant Review Using Grammatical Rules, Hybrid Approach, and SentiCircle

Suhariyanto¹,

Abdullah²,

Sarno³

et al. 2021

IJIES

Self Cite

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“…The output of the architecture are three neurons representing negative, neutral, and positive as written in Eqs. (4) and (5).…”

Section: Concept Extraction For Image Sentiment Analysismentioning

confidence: 99%

“…Social media analysis can be developed for many applications, such as health care, finance, voting intentions, security and defense, disaster response, NLP-based modeling, NLP-based social media visualization, entertainment, and media monitoring [1]. Studies in social media have been developed on various modalities such as text [2][3][4][5], image [6], video [7], and network graph [8].…”

Section: Introductionmentioning

confidence: 99%

Multiview Sentiment Analysis with Image-Text-Concept Features of Indonesian Social Media Posts

Setiawan¹

2021

IJIES

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Social media development makes it possible for everyone to express their opinions and information through text, speech, video, or images. Multiview sentiment analysis in current studies generally combines two modalities, text and image. It seeks to classify social media posts into two or more polarities, such as positive, neutral, or negative. To improve the performance of multiview Sentiment Analysis, we added another modality, which is concepts derived from text and image. Our proposed model integrates three views into a fusion with an ensemble approach by a metaclassifier. We performed text classification with Deep Convolutional Neural Networks. The input feature is Word2Vec for text representation in order to preserve semantic meaning. Additionally, we analyzed concepts from texts with SenticNet 5 as a knowledge base model and extracted concepts from images using the DeepSentiBank model. We obtained 2089 Adjective Noun Pairs and classified it with Multi-Layer Perceptron. Then we combined predicted probabilities from each classifier for Image, Text, and Concept by Ensemble Learning. A meta-classifier was implemented to predict the final sentiment from a fusion of Image-Text-Concept features. The fusion for multiview sentiment analysis works well and could achieve the best accuracy of 70% by applying the ensemble approach with Logistic Regression as the meta-classifier.

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“…Until recently, a few studies [15], [19] attempted to extract aspect categories directly from review text in a single step using language rule methods. The intermediate task of aspect term extraction is required for most models [16], [20], [21] However, it is hard to use these models on new datasets since manual tuning of various thresholds is required [15], [22]. Another problem of recent models is that they need to find some synonyms for each aspect, in which the result depends on the selection of these synonyms words.…”

Section: Background Of Studymentioning

confidence: 99%

A Similarity Score Model for Aspect Category Detection

Madhoushi¹,

Hamdan²,

Zainudin³

2021

IJACSA

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Aspect-based Sentiment Analysis (ABSA) aims to extract significant aspects of an item or product from reviews and predict the sentiment of each aspect. Previous similarity methods tend to extract aspect categories at the word level by combining Language Models (LM) in their models. A drawback for the LM model is its dependence on a large amount of labelled data for a specific domain to function well. This work proposes a mechanism to address labelled data dependency by a one-step approach experimenting to decide the best combinatory architectures of recurrent-based LM and the best semantic similarity measures for fostering a new aspect category detection model. The proposed model addresses drawbacks of previous aspect category detection models in an implicit manner. The datasets of this study, S1 and S2, are from standard SemEval online competition. The proposed model outperforms the previous baseline models in terms of the F1-score of aspect category detection. This study finds more relevant aspect categories by creating a more stable and robust model. The F1 score of our best model for aspect category detection is 79.03% in the restaurant domain for the S1 dataset. In dataset S2, the F1score is 72.65% in the laptop domain and 75.11% in the restaurant domain.

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Aspect Based Sentiment Analysis for Restaurant Reviews Using Hybrid ELMoWikipedia and Hybrid Expanded Opinion Lexicon-SentiCircle

Cited by 20 publications

References 26 publications

Aspect-Based Sentiment Analysis for Sentence Types with Implicit Aspect and Explicit Opinion in Restaurant Review Using Grammatical Rules, Hybrid Approach, and SentiCircle

Aspect-Based Sentiment Analysis for Sentence Types with Implicit Aspect and Explicit Opinion in Restaurant Review Using Grammatical Rules, Hybrid Approach, and SentiCircle

Multiview Sentiment Analysis with Image-Text-Concept Features of Indonesian Social Media Posts

A Similarity Score Model for Aspect Category Detection

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