The Experts below are selected from a list of 11220 Experts worldwide ranked by ideXlab platform

Jose Luis Sanchezcervantes - One of the best experts on this subject based on the ideXlab platform.

  • feature based Opinion Mining through ontologies
    Expert Systems With Applications, 2014
    Co-Authors: Isidro Penalvermartinez, Francisco Garciasanchez, Rafael Valenciagarcia, Miguel Angel Rodriguezgarcia, Valentin Moreno, Anabel Fraga, Jose Luis Sanchezcervantes
    Abstract:

    Abstract The idiosyncrasy of the Web has, in the last few years, been altered by Web 2.0 technologies and applications and the advent of the so-called Social Web. While users were merely information consumers in the traditional Web, they play a much more active role in the Social Web since they are now also data providers. The mass involved in the process of creating Web content has led many public and private organizations to focus their attention on analyzing this content in order to ascertain the general public’s Opinions as regards a number of topics. Given the current Web size and growth rate, automated techniques are essential if practical and scalable solutions are to be obtained. Opinion Mining is a highly active research field that comprises natural language processing, computational linguistics and text analysis techniques with the aim of extracting various kinds of added-value and informational elements from users’ Opinions. However, current Opinion Mining approaches are hampered by a number of drawbacks such as the absence of semantic relations between concepts in feature search processes or the lack of advanced mathematical methods in sentiment analysis processes. In this paper we propose an innovative Opinion Mining methodology that takes advantage of new Semantic Web-guided solutions to enhance the results obtained with traditional natural language processing techniques and sentiment analysis processes. The main goals of the proposed methodology are: (1) to improve feature-based Opinion Mining by using ontologies at the feature selection stage, and (2) to provide a new vector analysis-based method for sentiment analysis. The methodology has been implemented and thoroughly tested in a real-world movie review-themed scenario, yielding very promising results when compared with other conventional approaches.

Martin Ester - One of the best experts on this subject based on the ideXlab platform.

  • aspect based Opinion Mining from product reviews
    International ACM SIGIR Conference on Research and Development in Information Retrieval, 2012
    Co-Authors: Samaneh Moghaddam, Martin Ester
    Abstract:

    "What other people think" has always been an important piece of information for most of us during the decision-making process. Today people tend to make their Opinions available to other people via the Internet. As a result, the Web has become an excellent source of consumer Opinions. There are now numerous Web resources containing such Opinions, e.g., product reviews forums, discussion groups, and blogs. But, it is really difficult for a customer to read all of the reviews and make an informed decision on whether to purchase the product. It is also difficult for the manufacturer of the product to keep track and manage customer Opinions. Also, focusing on just user ratings (stars) is not a sufficient source of information for a user or the manufacturer to make decisions. Therefore, Mining online reviews (Opinion Mining) has emerged as an interesting new research direction. Extracting aspects and the corresponding ratings is an important challenge in Opinion Mining. An aspect is an attribute or component of a product, e.g. 'zoom' for a digital camera. A rating is an intended interpretation of the user satisfaction in terms of numerical values. Reviewers usually express the rating of an aspect by a set of sentiments, e.g. 'great zoom'. In this tutorial we cover Opinion Mining in online product reviews with the focus on aspect-based Opinion Mining. This problem is a key task in the area of Opinion Mining and has attracted a lot of researchers in the information retrieval community recently. Several Opinion related information retrieval tasks can benefit from the results of aspect-based Opinion Mining and therefore it is considered as a fundamental problem. This tutorial covers not only general Opinion Mining and retrieval tasks, but also state-of-the-art methods, challenges, applications, and also future research directions of aspect-based Opinion Mining.

  • SIGIR - Aspect-based Opinion Mining from product reviews
    Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval - SIGIR '12, 2012
    Co-Authors: Samaneh Moghaddam, Martin Ester
    Abstract:

    "What other people think" has always been an important piece of information for most of us during the decision-making process. Today people tend to make their Opinions available to other people via the Internet. As a result, the Web has become an excellent source of consumer Opinions. There are now numerous Web resources containing such Opinions, e.g., product reviews forums, discussion groups, and blogs. But, it is really difficult for a customer to read all of the reviews and make an informed decision on whether to purchase the product. It is also difficult for the manufacturer of the product to keep track and manage customer Opinions. Also, focusing on just user ratings (stars) is not a sufficient source of information for a user or the manufacturer to make decisions. Therefore, Mining online reviews (Opinion Mining) has emerged as an interesting new research direction. Extracting aspects and the corresponding ratings is an important challenge in Opinion Mining. An aspect is an attribute or component of a product, e.g. 'zoom' for a digital camera. A rating is an intended interpretation of the user satisfaction in terms of numerical values. Reviewers usually express the rating of an aspect by a set of sentiments, e.g. 'great zoom'. In this tutorial we cover Opinion Mining in online product reviews with the focus on aspect-based Opinion Mining. This problem is a key task in the area of Opinion Mining and has attracted a lot of researchers in the information retrieval community recently. Several Opinion related information retrieval tasks can benefit from the results of aspect-based Opinion Mining and therefore it is considered as a fundamental problem. This tutorial covers not only general Opinion Mining and retrieval tasks, but also state-of-the-art methods, challenges, applications, and also future research directions of aspect-based Opinion Mining.

Isidro Penalvermartinez - One of the best experts on this subject based on the ideXlab platform.

  • feature based Opinion Mining through ontologies
    Expert Systems With Applications, 2014
    Co-Authors: Isidro Penalvermartinez, Francisco Garciasanchez, Rafael Valenciagarcia, Miguel Angel Rodriguezgarcia, Valentin Moreno, Anabel Fraga, Jose Luis Sanchezcervantes
    Abstract:

    Abstract The idiosyncrasy of the Web has, in the last few years, been altered by Web 2.0 technologies and applications and the advent of the so-called Social Web. While users were merely information consumers in the traditional Web, they play a much more active role in the Social Web since they are now also data providers. The mass involved in the process of creating Web content has led many public and private organizations to focus their attention on analyzing this content in order to ascertain the general public’s Opinions as regards a number of topics. Given the current Web size and growth rate, automated techniques are essential if practical and scalable solutions are to be obtained. Opinion Mining is a highly active research field that comprises natural language processing, computational linguistics and text analysis techniques with the aim of extracting various kinds of added-value and informational elements from users’ Opinions. However, current Opinion Mining approaches are hampered by a number of drawbacks such as the absence of semantic relations between concepts in feature search processes or the lack of advanced mathematical methods in sentiment analysis processes. In this paper we propose an innovative Opinion Mining methodology that takes advantage of new Semantic Web-guided solutions to enhance the results obtained with traditional natural language processing techniques and sentiment analysis processes. The main goals of the proposed methodology are: (1) to improve feature-based Opinion Mining by using ontologies at the feature selection stage, and (2) to provide a new vector analysis-based method for sentiment analysis. The methodology has been implemented and thoroughly tested in a real-world movie review-themed scenario, yielding very promising results when compared with other conventional approaches.

Chunping Li - One of the best experts on this subject based on the ideXlab platform.

  • KSEM - Ontology Based Opinion Mining for Movie Reviews
    Knowledge Science Engineering and Management, 2009
    Co-Authors: Lili Zhao, Chunping Li
    Abstract:

    Ontology itself is an explicitly defined reference model of application domains with the purpose of improving information consistency and knowledge sharing. It describes the semantics of a domain in both human-understandable and computer-processable way. Motivated by its success in the area of Information Extraction (IE), we propose an ontology-based approach for Opinion Mining. In general, Opinion Mining is quite context-sensitive, and, at a coarser granularity, quite domain dependent. This paper introduces a fine-grain approach for Opinion Mining, which uses the ontology structure as an essential part of the feature extraction process, by taking account the relations between concepts. The experiment result shows the benefits of exploiting ontology structure to Opinion Mining.

  • Ontology based Opinion Mining for movie reviews
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2009
    Co-Authors: Lili Zhao, Chunping Li
    Abstract:

    Ontology itself is an explicitly defined reference model of application domains with the purpose of improving information consistency and knowledge sharing. It describes the semantics of a domain in both human-understandable and computer-processable way. Motivated by its success in the area of Information Extraction (IE), we propose an ontology-based approach for Opinion Mining. In general, Opinion Mining is quite context-sensitive, and, at a coarser granularity, quite domain dependent. This paper introduces a fine-grain approach for Opinion Mining, which uses the ontology structure as an essential part of the feature extraction process, by taking account the relations between concepts. The experiment result shows the benefits of exploiting ontology structure to Opinion Mining.

Ahmed Al-asmar - One of the best experts on this subject based on the ideXlab platform.

  • Ontology Based Arabic Opinion Mining
    Journal of Information & Knowledge Management, 2017
    Co-Authors: Alaa El-halees, Ahmed Al-asmar
    Abstract:

    In Arabic language, studies in the area of Opinion Mining are still limited compared to that being carried out in other languages. In this paper, we highlight the problem for Arabic Opinion Mining techniques when analysing reviews having different features with different Opinion strengths. The traditional works of Opinion Mining consider all features extracted from the reviews to be equally important, so they fail to determine the correct Opinion of the review and make the review's sentiment classification less accurate. This research presents a technique based on an ontology that uses feature level classification to classify Arabic user-generated reviews by identifying the relevant features from the review based on the degree of these features in the ontology tree. Then, we exploit the important features extracted to determine the overall polarity of the review. Moreover, summarisation for each feature is done to determine which feature has satisfied or dissatisfied customers. To evaluate our work, we use public datasets which are hotels and books datasets. We used [Formula: see text]-measure metrics to assess the performance and compare the results with other supervised and unsupervised techniques. Also, subjective evaluation is used in our method to demonstrate the effectiveness of feature and Opinion extraction process and summarisation. We show that our method improves the performance compared with other Opinion Mining classification approaches, obtaining 78.83% [Formula: see text]-measure in hotels domain and 79.18% in books domain. Furthermore, the subjective evaluation shows the effectiveness of our method by getting an average [Formula: see text]-measure of 84.62% in hotels dataset and 86.31% in books dataset.

  • Ontology Based Arabic Opinion Mining
    Journal of Information & Knowledge Management, 2017
    Co-Authors: Alaa El-halees, Ahmed Al-asmar
    Abstract:

    In Arabic language, studies in the area of Opinion Mining are still limited compared to that being carried out in other languages. In this paper, we highlight the problem for Arabic Opinion Mining techniques when analysing reviews having different features with different Opinion strengths. The traditional works of Opinion Mining consider all features extracted from the reviews to be equally important, so they fail to determine the correct Opinion of the review and make the review's sentiment classification less accurate. This research presents a technique based on an ontology that uses feature level classification to classify Arabic user-generated reviews by identifying the relevant features from the review based on the degree of these features in the ontology tree. Then, we exploit the important features extracted to determine the overall polarity of the review. Moreover, summarisation for each feature is done to determine which feature has satisfied or dissatisfied customers. To evaluate our work, we use public datasets which are hotels and books datasets. We used f-measure metrics to assess the performance and compare the results with other supervised and unsupervised techniques. Also, subjective evaluation is used in our method to demonstrate the effectiveness of feature and Opinion extraction process and summarisation. We show that our method improves the performance compared with other Opinion Mining classification approaches, obtaining 78.83% f-measure in hotels domain and 79.18% in books domain. Furthermore, the subjective evaluation shows the effectiveness of our method by getting an average f-measure of 84.62% in hotels dataset and 86.31% in books dataset.