Authors: Piryani, Rajesh | Gupta, Vedika | Singh, Vivek Kumar
Article Type: Research Article
Abstract: This paper describes an integrated aspect level opinion summary generation system for movie reviews. The system, named as Movie Prism, analyses each movie review, locates aspect term in it, identifies opinion about those aspects and then generates a visual aspect based opinion summary of the movie in question. At present, the movie reviews and other related information is being automatically fetched from IMDb for all the movies released during the years 2010 to 2014. The system has an integrated crawler for this purpose. Further, ontology for the movie domain is created for better aspect identification. We have evaluated the system …on three annotated movie review datasets. The system obtains good accuracy. Overall the designed system is capable of producing visual aspect level opinion summaries from unstructured textual reviews, without any need of training and results have a reasonable degree of accuracy. Show more
Keywords: Aspect level sentiment analysis, linguistic approach, natural language processing (NLP), ontology
DOI: 10.3233/JIFS-169272
Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 5, pp. 3297-3311, 2017
Authors: Piryani, Rajesh | Gupta, Vedika | Singh, Vivek Kumar | Pinto, David
Article Type: Research Article
Abstract: Books are an important source of knowledge to disseminate information. Researchers and academicians write books to propagate their innovative research or teachings amongst academic as well as non-academic audience. The number of books written every year is increasing rapidly. According to International Publisher Association (IPA) annual report 2015–2016, around 150 million different books were published worldwide in 2014–2015. Many e-commerce websites are also involved in selling books. A recent addition to book publishing world is e-books, which have really made it very simple to publish. While, availability of large number of books is good for readers, at the same time …it is challenging to find a good book, particularly in scholarly settings. Researchers in the area of Scientometrics have attempted to view assessment of goodness of a scholarly book by measuring citations that a book receive. However, citations alone are not a true measure of a book’s impact. Many a times people use the knowledge in a book without actually citing it. Also use of books in classroom settings or for general reading often is not reflected in terms of citations. Therefore, it is important to obtain users’s opinion about a book from other forms of data. Fortunately, we have now some data of this sort available in form of reviews, downloads and social media mentions etc. Amazon and Goodreads, both of which provide the readers’ views about a book, are two good examples. This paper presents an exploratory research work on using these non-traditional data about books to assess impact of a book. A set of Scopus-indexed computer science books with good citations as well as some other popular books in computer science domain are used for analysis. The reviews of books have been crawled in an automated fashion from Amazon and Goodreads. Thereafter sentiment analysis is carried out the text of reviews. Results of sentiment analysis are compared and correlated with traditional impact assessment metrics. The experimental analysis does not show a coherent relationship between citation and online reviews. Also, majority of the online reviews are found to be positive for large number of books in the dataset. As a related exercise, the Scopus citation data and Google scholar citation data for books are also compared. A high value of correlation is observed in these two. Overall the exploratory analysis provides a useful insight into the problem of book impact assessment. Show more
Keywords: Altmetrics, book impact, citation impact, review mining, sentiment analysis
DOI: 10.3233/JIFS-169494
Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 5, pp. 3101-3110, 2018
Authors: Gupta, Vedika | Singh, Vivek Kumar | Ghose, Udayan | Mukhija, Pankaj
Article Type: Research Article
Abstract: This paper tries to map the research work carried out in the field of Big Data through a detailed analysis of scholarly articles published on the theme during 2010-16, as indexed in Scopus. We have collected and analyzed all relevant publications on Big Data, as indexed in Scopus, through a quantitative as well as textual characterization. The analysis attempts to dwell into parameters like research productivity, growth of research and citations, thematic trends, top publication sources and emerging topics in this field. The analytical study also investigates country-wise publications output and impact in terms of average citations per paper, country-level …collaboration patterns, authorship and leading contributors (countries, institutions) etc. The scholarly publication data is also subjected to a detailed textual analysis method to identify key themes in Big Data research, disciplinary variations and thematic trends and patterns. The results produce interesting inferences. Quantitative measures show that there has been a tremendous increase in number of publications related to Big Data during last few years. Research work in Big Data, though primarily considered a sub-discipline of Computer Science, is now carried out by researchers in many disciplines. Thematic analysis of publications in Big Data show that it’s a discipline involving research interest from fields as diverse as Medicine to Social Sciences. The paper also identifies major keywords now associated with Big Data research such as Cloud Computing, Deep Learning, Social Media and Data Analytics. This helps in a thorough understanding and visualization of the Big Data research area. Show more
Keywords: Big data, big data analytics, data science, scientometrics
DOI: 10.3233/JIFS-179016
Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4659-4675, 2019
Authors: Gupta, Vedika | Singh, Vivek Kumar | Mukhija, Pankaj | Ghose, Udayan
Article Type: Research Article
Abstract: E-commerce websites provide an easy platform for users to put forth their viewpoints on different topics-ranging from a news item to any product in the market. Such online content encourages authors to express opinions on various aspects of an entity. Aspect based sentiment analysis deals with analyzing this textual content to look for the aspect in question. After locating the aspects, corresponding sentiment bearing words are looked for. This paper describes an integrated system that generates the opinionated aspect based graphical and extractive summaries from a large set of mobile reviews. The system focuses on three tasks (a) identification of …aspects in given field, (b) computation of sentiment polarity of each aspect, and (c) generates opinionated aspect based graphical and extractive summaries. The system has been evaluated on three mobile-reviews dataset and obtains better precision and recall than baseline approach. The system generates summaries from reviews without any training. Show more
Keywords: Aspect-based sentiment analysis, extractive summary, sentiment summarization
DOI: 10.3233/JIFS-179021
Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4721-4730, 2019