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

Suhariyanto, Suhariyanto; Abdullah, Rachmad; Sarno, Riyanarto; Fatichah, Chastine

doi:10.22266/ijies2021.1031.17

Cited by 5 publications

(7 citation statements)

References 18 publications

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“…Reviews with an ID of 1:0 can be extracted in the aspect term "room" and the opinion terms "clean", "extremely dated", and "worn out". Based on the results done by the expert, the two reviews can still be extracted properly using the Rachmad rule algorithm [6], Suhariyanto [5], and the proposed text extraction method. However, Rachmad [6], Suhariyanto [5] have not been able to properly extract the 2:0 ID review, where both of them produce the aspect term "food" and the opinion term "cheap".…”

Section: Attention-based Sentence Extraction Resultsmentioning

confidence: 99%

“…Text extraction functions to extracts the information that related to the sentence types, word types, and the relationship between words in the sentences. Several methods have also been generated for the text extraction stage using a machine learning algorithm, which is carried out using a rule set to obtain explicit and implicit aspects, and opinion terms candidates too [5,6,14,15]. Text extraction for a review of implicit aspects, requires in-depth attention regarding the structure of the words in the sentence.…”

Section: Text Extractionmentioning

confidence: 99%

“…Previous studies [5,6] has worked on cases of implicit aspect reviews related to sentence types. These studies has succeeded in showing the characteristics of implicit aspects based on the presence or absence of nouns in the review which can be identified by the aspect category keywords.…”

Section: Introductionmentioning

confidence: 99%

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Attention-based Sentence Extraction for Aspect-based Sentiment Analysis with Implicit Aspect Cases in Hotel Review Using Machine Learning Algorithm, Semantic Similarity, and BERT

2023

IJIES

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The development of the aspect-based sentiment analysis (ABSA) method to work on the case of implicit hotel reviews in depth has not been done much. The problem of extracting aspect and opinion words based on syntaxis and semantics is not only influenced by different of sentence structure types but can also be influenced by word sense disambiguation (WSD) level. So, it needs deep attention to solve these problems. For example, the review "You can't say its cheap because food is cheaper in Chinatown.", where "food is cheaper in Chinatown" is still widely extracted as target terms because there are explicit element of aspect and opinion. In fact, it requires in-depth attention to be able to extract and capture the implicit element "can't say its cheap" as a target term. However, there has been not many research that discusses the details of the ABSA process related to this case. Therefore, we propose an attention-based sentence extraction method for ABSA with implicit aspect cases in hotel review. The method purpose is to improve the ABSA accuracy for hotel reviews based on the cases that have not been solved. First, we develop a pre-processing method to the make the data ready to be processed. Then, we build a set rule-based algorithm to get the word types and the relationship of each word in the sentence. These rules function to identify and mark the candidates of aspect and opinion terms based on the review sentence structure types (simple, compound, complex, compound-complex) and to identify and mark the factors that influence the WSD level (conjunction, punctuation, contrast, intensification) in each sentence. The candidates result of aspect and opinion terms are used as input for the aspect categorization process. The aspect categorization process is carried out using machine learning algorithm, implicit aspect corpus, BERT embedding, and semantic similarity to obtain the aspect categories of each review. Furthermore, the ABSA process is carried out using the BERT sentiment analysis method. Finally, the evaluation process for aspect categorization and ABSA are done with the good result. The evaluation result of aspect categorization obtains 91.31% for accuracy, 91.81% for precision, 89.43% for recall, and 90.61% for f1-measure. Meanwhile, the evaluation result of ABSA obtains 98.10% for accuracy, 98.11% for precision, 96.98% for recall, and 97.54% for f1-measure.

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Section: Attention-based Sentence Extraction Resultsmentioning

confidence: 99%

Section: Text Extractionmentioning

confidence: 99%

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Attention-based Sentence Extraction for Aspect-based Sentiment Analysis with Implicit Aspect Cases in Hotel Review Using Machine Learning Algorithm, Semantic Similarity, and BERT

2023

IJIES

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“…Suhariyanto et al (2) analyze the sentiment of the movie reviews on RottenTomatoes using SentiWordnet. Bagus et alDewi et al and Reza et al (3,4) conduct an experiment on aspect based sentiment analysis on hotel reviews using topic modelling and machine learning methods. In aspect-based sentiment analysis, it is generally divided into three sub-processes.…”

Section: Introductionmentioning

confidence: 99%

Aspect Based Sentiment Analysis and Opinion Mining on Twitter Data Set Using Linguistic Rules

Ahmad¹,

Shaikh²,

Tanwani³

2023

IJST

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Objectives: To propose the most effective machine learning technique for aspect based sentiment analysis of consumer feedback reviews by correlating people's emotions of different aspects of a product or service with word importance, linguistic rules, tagging, dependency relations and filtering to enhance the classification accuracy and to explore the different machine learning approaches in order to determine their significance and to spark interest in this field of study. Methods: The dataset has been taken from the most popular networking site twitter using twitter API. A total of 33,036 tweets were retrieved for this study from16 July 2022 to 16 Sept. 2022, but were only able to include 7570 tweets after data cleaning and then proposes a model named SVMS (SVM using Spacy) for sentiment Analysis. To measure the performance of a particular model, numerous analyses are conducted on the dataset and then the effectiveness of proposed model is compared with 4 basic classifiers LR, NB, RF and SVM. The parameters used in this study are accuracy, precision, recall, and F1 score. Findings: It is found that the Proposed SVMS model shows better results than all other algorithms by having 96% accuracy which is 1% faster than LR, 2% faster than NB, 5% faster than RF, and 4% faster than SVM, 96% precision, 98% recall and 97% F1 score. SVMS is recommended in this research context for the classification of the text and has a great significance for future researchers in sentences and text classification. Novelty: In this paper the performance of sentiment analysis classifiers is evaluated. In addition to general opinions, the work examines specific aspects such as product, quality, service, price, and ease of use. Comparing the proposed model with the existing models, it has been found that it outperformed the other models in most of the aspects.

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“…Word similarity process is a word extraction process to get the word similarity of aspect term and category. The most frequently used word similarity is semantic similarity [13,14,15]. Semantic similarity is token-dependent, which is generated to be used as input, namely terms.…”

Section: Introductionmentioning

confidence: 99%

Aspect-Based Sentiment Analysis for Hotel Review Using LDA, Semantic Similarity, and BERT

2022

IJIES

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Hotel review is frequently used as a main input in sentiment analysis process. It aims at helping travellers easily find more accurate information about hotel aspects in selecting hotels for their journeys. Based on the review datasets, hotel service organizers may evaluate guests' responses towards the services provided by the hotels. Hotel organizers, in turn, may also know the hotel aspects which need improvement for the next experiences. The common problems are because the processed data do not focus on small scale so that wrong selection of terms from review document frequently appears. The problem that often arises is that the amount of data that is processed is not limited to a small scale, so there are often errors in taking terms from a review document. Meanwhile, these terms are the main input source used for the assessment of aspect categorization and aspect-based sentiment analysis. So, we need aspect categorization and aspect-based sentiment analysis methods that can work automatically on a large scale with good accuracy results. In this study, first, the results of the pre-processing were processed using TF-ICF to obtain terms from reviews based on aspect keyword variables in each hotel aspect category. Next, LDA was used to get the hidden topic of each term. The aim was to obtain better terms accuracy results. Then, the aspect categorization process was carried out using BERT embedding and semantic similarity with the aim of obtaining more significant differences in similarity results in each aspect category so that the determination of aspect categories from a review could be more accurate. The results of the aspect extraction obtained an evaluation of the aspect categorization for each precision 0.86, recall 0.92, and f1-measure 0.89. Furthermore, BERT sentiment analysis method is used in the aspect-based sentiment analysis process. Finally, the evaluation result of aspect-based sentiment analysis obtained for each precision, recall, and f-1 measure are 0.96, 0.98, and 0.97.

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

Cited by 5 publications

References 18 publications

Attention-based Sentence Extraction for Aspect-based Sentiment Analysis with Implicit Aspect Cases in Hotel Review Using Machine Learning Algorithm, Semantic Similarity, and BERT

Attention-based Sentence Extraction for Aspect-based Sentiment Analysis with Implicit Aspect Cases in Hotel Review Using Machine Learning Algorithm, Semantic Similarity, and BERT

Aspect Based Sentiment Analysis and Opinion Mining on Twitter Data Set Using Linguistic Rules

Aspect-Based Sentiment Analysis for Hotel Review Using LDA, Semantic Similarity, and BERT

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