Ontology-Guided Approach to Feature-Based Opinion Mining

Peñalver-Martínez, Isidro; Valencia‐García, Rafael; García‐Sánchez, Francisco

doi:10.1007/978-3-642-22327-3_20

Cited by 24 publications

(11 citation statements)

References 13 publications

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“…An an aspect level classification consists of the representation of sentiments as triplets {user, feature, sentiment}. In this sense, ontologies have been proved to be an effective method to extract the aspects for an specific domain [11][12][13][14][15][16].…”

Section: Opinion Miningmentioning

confidence: 99%

Evaluating Information-Retrieval Models and Machine-Learning Classifiers for Measuring the Social Perception towards Infectious Diseases

et al. 2019

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Recent outbreaks of infectious diseases remind us the importance of early-detection systems improvement. Infodemiology is a novel research field that analyzes online information regarding public health that aims to complement traditional surveillance methods. However, the large volume of information requires the development of algorithms that handle natural language efficiently. In the bibliography, it is possible to find different techniques to carry out these infodemiology studies. However, as far as our knowledge, there are no comprehensive studies that compare the accuracy of these techniques. Consequently, we conducted an infodemiology-based study to extract positive or negative utterances related to infectious diseases so that future syndromic surveillance systems can be improved. The contribution of this paper is two-fold. On the one hand, we use Twitter to compile and label a balanced corpus of infectious diseases with 6164 utterances written in Spanish and collected from Central America. On the other hand, we compare two statistical-models: word-grams and char-grams. The experimentation involved the analysis of different gram sizes, different partitions of the corpus, and two machine-learning classifiers: Random-Forest and Sequential Minimal Optimization. The results reach a 90.80% of accuracy applying the char-grams model with five-char-gram sequences. As a final contribution, the compiled corpus is released.

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Section: Opinion Miningmentioning

confidence: 99%

Evaluating Information-Retrieval Models and Machine-Learning Classifiers for Measuring the Social Perception towards Infectious Diseases

et al. 2019

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“…Bhattattacharya et al [2] employed IMDb's structured data to categorize documents, and Yu et al [30] built an aspect hierarchy using product specifications and reviews. Wang et al [29] and Peñalver-Martínez et al [23] also employed product specifications to summarize product features. Product reviews and specifications were jointly modeled using topic models by Duan et al [4] to improve product search and by Park et al [22] to generate augmented specifications with useful information.…”

Section: Related Workmentioning

confidence: 99%

Retrieval of Relevant Opinion Sentences for New Products

Park

Kim

Zhai

et al. 2015

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval

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With the rapid development of Internet and E-commerce, abundant product reviews have been written by consumers who bought the products. These reviews are very useful for consumers to optimize their purchasing decisions. However, since the reviews are all written by consumers who have bought and used a product, there are generally very few or even no reviews available for a new product or an unpopular product. We study the novel problem of retrieving relevant opinion sentences from the reviews of other products using specifications of a new or unpopular product as query. Our key idea is to leverage product specifications to assess product similarity between the query product and other products and extract relevant opinion sentences from the similar products where a consumer may find useful discussions. Then, we provide ranked opinion sentences for the query product that has no user-generated reviews. We first propose a popular summarization method and its modified version to solve the problem. Then, we propose our novel probabilistic methods. Experiment results show that the proposed methods can effectively retrieve useful opinion sentences for products that have no reviews.

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“…In recent years, several pieces of research have been conducted in order to improve sentiment classification. Many approaches [4–11] have proposed methods for the sentiment classification of English reviews.…”

Section: Related Workmentioning

confidence: 99%

“…The experiments, the movie-review data and the multidomain sentiment dataset show that the approach attains comparable or better performance rates than existing hardly supervised sentiment classification methods despite using no labelled documents. In Peñalver Martínez et al [8] the authors propose an innovative methodology for opinion mining that brings together traditional natural language processing techniques with sentiment analysis processes and Semantic Web technologies. The aim of this work is to improve feature-based opinion mining by employing ontologies in the selection of features and to provide a new method for sentiment analysis based on vector analysis.…”

Section: Related Workmentioning

confidence: 99%

A study on LIWC categories for opinion mining in Spanish reviews

Salas-Zárate

López-López

Valencia‐García

et al. 2014

Journal of Information Science

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With the exponential growth of social media, that is, blogs and social networks, organizations and individual persons are increasingly using the number of reviews of these media for decision-making about a product or service. Opinion mining detects whether the emotions of an opinion expressed by a user on Web platforms in natural language are positive or negative. This paper presents extensive experiments to study the effectiveness of the classification of Spanish opinions in five categories: highly positive, highly negative, positive, negative and neutral, using the combination of the psychological and linguistic features of LIWC (Linguistic Inquiry and Word Count). LIWC is a text analysis software that enables the extraction of different psychological and linguistic features from natural language text. For this study, two corpora have been used, one about movies and one about technological products. Furthermore, we conducted a comparative assessment of the performance of various classification techniques, J48, SMO and BayesNet, using precision, recall and F-measure metrics. The findings revealed that the positive and negative categories provide better results than the other categories. Finally, experiments on both corpora indicated that SMO produces better results than BayesNet and J48 algorithms, obtaining an F-measure of 90.4 and 87.2% in each domain.

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Ontology-Guided Approach to Feature-Based Opinion Mining

Cited by 24 publications

References 13 publications

Evaluating Information-Retrieval Models and Machine-Learning Classifiers for Measuring the Social Perception towards Infectious Diseases

Evaluating Information-Retrieval Models and Machine-Learning Classifiers for Measuring the Social Perception towards Infectious Diseases

Retrieval of Relevant Opinion Sentences for New Products

A study on LIWC categories for opinion mining in Spanish reviews

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