Ontology-Driven Sentiment Analysis of Product and Service Aspects

Schouten, Kim; Frăsincar, Flavius

doi:10.1007/978-3-319-93417-4_39

Cited by 25 publications

(48 citation statements)

References 13 publications

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“…To predict the presence of a relation with the aspect, we label a word as "Yes" if it is related to the aspect, and as "No", if it is not related to the aspect. We determine the existence of a relation by using the Stanford CoreNLP Dependency Parser [9], and the ontology described by Schouten and Frasincar [13] that connects aspects to words. A word is related to the aspect in the opinion if there exists a dependency between them, or if they are connected according to the ontology.…”

Section: Diagnostic Classificationmentioning

confidence: 99%

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Explaining a neural attention model for aspect-based sentiment classification using diagnostic classification

Meijer

Frăsincar

Truşcǎ

2021

Proceedings of the 36th Annual ACM Symposium on Applied Computing

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Many high performance machine learning models for Aspect-Based Sentiment Classification (ABSC) produce black box models, and therefore barely explain how they classify a certain sentiment value towards an aspect. In this paper, we propose explanation models, that inspect the internal dynamics of a state-of-the-art neural attention model, the LCR-Rot-hop, by using a technique called Diagnostic Classification. Our diagnostic classifier is a simple neural network, which evaluates whether the internal layers of the LCR-Rot-hop model encode useful word information for classification, i.e., the part of speech, the sentiment value, the presence of aspect relation, and the aspect-related sentiment value of words. We conclude that the lower layers in the LCR-Rot-hop model encode the part of speech and the sentiment value, whereas the higher layers represent the presence of a relation with the aspect and the aspect-related sentiment value of words.

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Section: Diagnostic Classificationmentioning

confidence: 99%

“…Table 3 represents the classes' frequencies in the data sets. We specify the sentiment value of a word by using the ontology described by Schouten and Frasincar [13], and NLTK SentiWordNet [3]. The ontology classifies a word positive (negative), if the word has a superclass Positive (Negative).…”

Section: Diagnostic Classificationmentioning

confidence: 99%

Explaining a neural attention model for aspect-based sentiment classification using diagnostic classification

Meijer

Frăsincar

Truşcǎ

2021

Proceedings of the 36th Annual ACM Symposium on Applied Computing

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“…Ontology. The ontology is based upon [19] and consists of three classes. First, SentimentMention contains the expressions of sentiment.…”

Section: Haabsa++mentioning

confidence: 99%

Adversarial Training for a Hybrid Approach to Aspect-Based Sentiment Analysis

Hochstenbach¹,

Frăsincar²,

Truşcǎ³

2021

Preprint

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The increasing popularity of the Web has subsequently increased the abundance of reviews on products and services. Mining these reviews for expressed sentiment is beneficial for both companies and consumers, as quality can be improved based on this information. In this paper, we consider the state-of-the-art HAABSA++ algorithm for aspect-based sentiment analysis tasked with identifying the sentiment expressed towards a given aspect in review sentences. Specifically, we train the neural network part of this algorithm using an adversarial network, a novel machine learning training method where a generator network tries to fool the classifier network by generating highly realistic new samples, as such increasing robustness. This method, as of yet never in its classical form applied to aspect-based sentiment analysis, is found to be able to considerably improve the out-of-sample accuracy of HAABSA++: for the SemEval 2015 dataset, accuracy was increased from 81.7% to 82.5%, and for the SemEval 2016 task, accuracy increased from 84.4% to 87.3%.

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“…Many researchers merged commonsense knowledge, the semantics of features with ontology to improve the accuracy of aspect extraction and sentiment classification. Schouten, Frasincar & de Jong (2017) and Schouten & Frasincar (2018) proposed ontologyenhanced aspect-based sentiment analysis. Schouten, Frasincar & de Jong (2017) concentrated on a knowledge-driven solution with the aim to complement standard machine learning (ML) method.…”

Section: Introductionmentioning

confidence: 99%

“…For both tasks the libsvm classifier is used. Schouten & Frasincar (2018) prepared ontology with three classes like SentimentMention, AspectMention, and Sentiment Value. The ontology is generated using an onto-clean methodology.…”

Section: Introductionmentioning

confidence: 99%

A supervised scheme for aspect extraction in sentiment analysis using the hybrid feature set of word dependency relations and lemmas

Bhamare

Prabhu

2021

PeerJ Computer Science

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Due to the massive progression of the Web, people post their reviews for any product, movies and places they visit on social media. The reviews available on social media are helpful to customers as well as the product owners to evaluate their products based on different reviews. Analyzing structured data is easy as compared to unstructured data. The reviews are available in an unstructured format. Aspect-Based Sentiment Analysis mines the aspects of a product from the reviews and further determines sentiment for each aspect. In this work, two methods for aspect extraction are proposed. The datasets used for this work are SemEval restaurant review dataset, Yelp and Kaggle datasets. In the first method a multivariate filter-based approach for feature selection is proposed. This method support to select significant features and reduces redundancy among selected features. It shows improvement in F1-score compared to a method that uses only relevant features selected using Term Frequency weight. In another method, selective dependency relations are used to extract features. This is done using Stanford NLP parser. The results gained using features extracted by selective dependency rules are better as compared to features extracted by using all dependency rules. In the hybrid approach, both lemma features and selective dependency relation based features are extracted. Using the hybrid feature set, 94.78% accuracy and 85.24% F1-score is achieved in the aspect category prediction task.

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Ontology-Driven Sentiment Analysis of Product and Service Aspects

Cited by 25 publications

References 13 publications

Explaining a neural attention model for aspect-based sentiment classification using diagnostic classification

Explaining a neural attention model for aspect-based sentiment classification using diagnostic classification

Adversarial Training for a Hybrid Approach to Aspect-Based Sentiment Analysis

A supervised scheme for aspect extraction in sentiment analysis using the hybrid feature set of word dependency relations and lemmas

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