Concept-Level Sentiment Analysis with Dependency-Based Semantic Parsing: A Novel Approach

Agarwal, Basant; Poria, Soujanya; Mittal, Namita; Gelbukh, Alexander; Hussain, Amir

doi:10.1007/s12559-014-9316-6

Cited by 119 publications

(38 citation statements)

References 30 publications

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“…Zhuang et al [19], Ma et al [11] and Agarwal et al [2] performed ML approaches that require largely trained datasets to perform with accuracy. ML approaches deliver significant results for feature extraction task when the training data sets are manually annotated by a human expert.…”

Section: Related Workmentioning

confidence: 99%

“…For example, Association Rule Mining algorithms (ARM) primarily rely on natural language processing techniques to identify nouns and noun phrases representing features [7,8,10,17]. Machine Learning approaches (ML) rely on a large set of training data to learn the features from a set of reviews [2,11,19]. Semantic Knowledge-Based approaches (SKB) are based on extracting features from reviews by utilising an ontology that contains a conceptualised knowledge background of the domain [1,14,18].…”

Section: Introductionmentioning

confidence: 99%

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A New Approach to Ontology-Based Semantic Modelling for Opinion Mining

Alfrjani

Osman

Cosma

2016

2016 UKSim-AMSS 18th International Conference on Computer Modelling and Simulation (UKSim)

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-With the fast growth of World Wide Web 2.0, a great number of opinions about a variety of products have been published in blogs, forums, and social networks. Opinion mining tools are needed to enable users to efficiently process a large number of reviews found online, in order to determine the underlying opinions. This paper presents a new methodology for semantic modelling of the domain knowledge for opinion mining. In particular, the new methodology focuses on modelling the domain knowledge in such a way that it can be translated to a formal ontology, which can then be automatically enriched with ground facts obtained from public Linked Open Data resources. The methodology also considers procedures to link between the formal ontology and Natural Language Processing. Our approach successfully enriches the ontology with the relevant ground facts. This ontology can then be used to perform a variety of data mining tasks including sentiment analysis and information retrieval.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A New Approach to Ontology-Based Semantic Modelling for Opinion Mining

Alfrjani

Osman

Cosma

2016

2016 UKSim-AMSS 18th International Conference on Computer Modelling and Simulation (UKSim)

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“…Agarwal et al used several hand-crafted dependency rules to extract syntactic n-grams from sentences [18]. For example, from the dependency graph of the sentence "the movie sounds interesting," they extracted such n-grams as " sounds movie," "sounds interesting," "sounds movie the," "sounds," "movie," etc.…”

Section: Sentiment Analysis With Dependency Treesmentioning

confidence: 99%

Automatic Classification of Online Doctor Reviews: Evaluation of Text Classifier Algorithms (Preprint)

Rivas¹,

Montazeri²,

Le³

et al. 2018

Preprint

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Background: An increasing number of doctor reviews are being generated by patients on the internet. These reviews address a diverse set of topics (features), including wait time, office staff, doctor's skills and bedside manner. Most previous work on automatic analysis of online customer reviews assumes that (a) product features are described unambiguously by a small number of keywords, e.g., "battery" for phones, and (b) the opinion for each feature has positive or negative sentiment. However, in the domain of doctor reviews, this setting is too restrictive: a feature such as "visit duration" for doctor reviews may be expressed in many ways, and does not necessarily have positive/negative sentiment.

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“…The performance of the machine learning technique depends on the effectiveness of the selected method for feature extraction. Among the most used methods are bag of words [13], TF-IDF [14], -grams (unigrams, bigrams, and trigrams) [11,15], features based on POS tagging [16], and features based on dependency rules [17].…”

Section: Related Workmentioning

confidence: 99%

Sentiment Analysis in Spanish for Improvement of Products and Services: A Deep Learning Approach

Paredes-Valverde

Colomo‐Palacios

Salas-Zárate

et al. 2017

Scientific Programming

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Sentiment analysis is an important area that allows knowing public opinion of the users about several aspects. This information helps organizations to know customer satisfaction. Social networks such as Twitter are important information channels because information in real time can be obtained and processed from them. In this sense, we propose a deep-learning-based approach that allows companies and organizations to detect opportunities for improving the quality of their products or services through sentiment analysis. This approach is based on convolutional neural network (CNN) and word2vec. To determine the effectiveness of this approach for classifying tweets, we conducted experiments with different sizes of a Twitter corpus composed of 100000 tweets. We obtained encouraging results with a precision of 88.7%, a recall of 88.7%, and an -measure of 88.7% considering the complete dataset.

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Concept-Level Sentiment Analysis with Dependency-Based Semantic Parsing: A Novel Approach

Cited by 119 publications

References 30 publications

A New Approach to Ontology-Based Semantic Modelling for Opinion Mining

A New Approach to Ontology-Based Semantic Modelling for Opinion Mining

Automatic Classification of Online Doctor Reviews: Evaluation of Text Classifier Algorithms (Preprint)

Sentiment Analysis in Spanish for Improvement of Products and Services: A Deep Learning Approach

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