19,875 research outputs found

    A New Hybrid Approach to Sentiment Classification

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    With the advancement of the World Wide Web, opinion sharing online has gained a lot of popularity. These opinions are utilized for decision making, market analysis, as well as other applications. The need to harness these opinions, and the motivation behind this need has led to the development and subsequent advancement of the field of Sentiment Analysis. Various issues have arisen from these, such as difficulty in locating these opinions in a body of text, as well as determining the sentiment/polarity of these opinions. To tackle the issue of opinion polarity determination, a number of classification approaches have been developed. These approaches have focused on opinion classification at various levels, such as document, sentence and aspect levels. Most document level approaches treat documents as a bag of words during the classification process, and hence classify them as a whole. The problem with this is that there could be a mixture of opinions directed towards various aspects, within a document. It is therefore imperative to utilize a classification approach which takes into account these constituent opinions. This is the focus of classification approaches which work at the aspect level. Another important factor in the issue of sentiment/polarity classification is the choice of the classification approach. This can be machine learning, lexical/lexicon-based, and more recently, hybrid. The machine learning approaches have the benefits of carrying out classification with high accuracies, and efficiently handling large feature sets, which makes them a favourite choice where high accuracies are desired. They however also have the drawback of difficulty in adaptability, due to the domain dependency of sentiment words. The pure lexicon-based approaches do not achieve the accuracy of the machine learning approaches, but are said to offer more explainable results and take into consideration the information in lexicons. In this work, we present a novel hybrid approach, which incorporates information from lexicons in a machine learning classifier, and takes as features various linguistic knowledge sources. Our novel hybrid approach utilizes transitive dependencies to incorporate the opinions expressed towards different aspects of a document in determining the polarity classification of the whole document. The domain dependency of sentiment words is also addressed through the use of composite features and a domain specific lexicon created in this work. It was found that the use of transitive dependencies in an aspect-focused classification is a promising area, which has the potential of improving aspect based classification once the aspects have been properly determined. It was also found that although using composite features does not necessarily improve the classification accuracy, it gives rise to context rich classifiers, and the domain specific lexicon generated performed on par with the widely used generic lexicon, SentiWordNet

    What attracts vehicle consumers’ buying:A Saaty scale-based VIKOR (SSC-VIKOR) approach from after-sales textual perspective?

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    Purpose: The increasingly booming e-commerce development has stimulated vehicle consumers to express individual reviews through online forum. The purpose of this paper is to probe into the vehicle consumer consumption behavior and make recommendations for potential consumers from textual comments viewpoint. Design/methodology/approach: A big data analytic-based approach is designed to discover vehicle consumer consumption behavior from online perspective. To reduce subjectivity of expert-based approaches, a parallel Naïve Bayes approach is designed to analyze the sentiment analysis, and the Saaty scale-based (SSC) scoring rule is employed to obtain specific sentimental value of attribute class, contributing to the multi-grade sentiment classification. To achieve the intelligent recommendation for potential vehicle customers, a novel SSC-VIKOR approach is developed to prioritize vehicle brand candidates from a big data analytical viewpoint. Findings: The big data analytics argue that “cost-effectiveness” characteristic is the most important factor that vehicle consumers care, and the data mining results enable automakers to better understand consumer consumption behavior. Research limitations/implications: The case study illustrates the effectiveness of the integrated method, contributing to much more precise operations management on marketing strategy, quality improvement and intelligent recommendation. Originality/value: Researches of consumer consumption behavior are usually based on survey-based methods, and mostly previous studies about comments analysis focus on binary analysis. The hybrid SSC-VIKOR approach is developed to fill the gap from the big data perspective

    Introduction to the special issue on cross-language algorithms and applications

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    With the increasingly global nature of our everyday interactions, the need for multilingual technologies to support efficient and efective information access and communication cannot be overemphasized. Computational modeling of language has been the focus of Natural Language Processing, a subdiscipline of Artificial Intelligence. One of the current challenges for this discipline is to design methodologies and algorithms that are cross-language in order to create multilingual technologies rapidly. The goal of this JAIR special issue on Cross-Language Algorithms and Applications (CLAA) is to present leading research in this area, with emphasis on developing unifying themes that could lead to the development of the science of multi- and cross-lingualism. In this introduction, we provide the reader with the motivation for this special issue and summarize the contributions of the papers that have been included. The selected papers cover a broad range of cross-lingual technologies including machine translation, domain and language adaptation for sentiment analysis, cross-language lexical resources, dependency parsing, information retrieval and knowledge representation. We anticipate that this special issue will serve as an invaluable resource for researchers interested in topics of cross-lingual natural language processing.Postprint (published version

    Transfer Learning for Speech and Language Processing

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    Transfer learning is a vital technique that generalizes models trained for one setting or task to other settings or tasks. For example in speech recognition, an acoustic model trained for one language can be used to recognize speech in another language, with little or no re-training data. Transfer learning is closely related to multi-task learning (cross-lingual vs. multilingual), and is traditionally studied in the name of `model adaptation'. Recent advance in deep learning shows that transfer learning becomes much easier and more effective with high-level abstract features learned by deep models, and the `transfer' can be conducted not only between data distributions and data types, but also between model structures (e.g., shallow nets and deep nets) or even model types (e.g., Bayesian models and neural models). This review paper summarizes some recent prominent research towards this direction, particularly for speech and language processing. We also report some results from our group and highlight the potential of this very interesting research field.Comment: 13 pages, APSIPA 201
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