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20. Information Extraction from PDF Sources Based on Rule-Based System Using Integrated Formats

Author

Ahmad, Riaz, Afzal, Muhammad Tanvir, Qadir, Muhammad Abdul, Diniz Junqueira Barbosa, Simone, Series editor, Chen, Phoebe, Series editor, Du, Xiaoyong, Series editor, Filipe, Joaquim, Series editor, Kara, Orhun, Series editor, Kotenko, Igor, Series editor, Liu, Ting, Series editor, Sivalingam, Krishna M., Series editor, Washio, Takashi, Series editor, Sack, Harald, editor, Dietze, Stefan, editor, Tordai, Anna, editor, and Lange, Christoph, editor

Published
2016
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21. Evaluation of Polarity Features for Sentiment Analysis on Product Reviews

Author

Zafar, Lubna, Afzal, Muhammad Tanvir, Zareen, Syed Jawad, Zafar, Lubna, Afzal, Muhammad Tanvir, and Zareen, Syed Jawad

Abstract

Customers express their opinions about products online, which influence potential buyers. This feedback is valuable for manufacturers to enhance their products. Sentiment analysis, which categorizes sentiments as positive, negative, or neutral, is crucial but challenging. Despite recent advancements, several research gaps persist. Firstly, prior studies have explored polarity features independently or in partial combinations, lacking comprehensive evaluation across annotated datasets. Secondly, most methods classify sentiments as merely positive or negative, overlooking nuances like intensity levels (e.g., strong positive, weak negative). Lastly, existing approaches often employ diverse classifiers on disparate datasets, lacking standardized comparison. To address these gaps, this research examines adjective, adverb, and verb polarity features both independently and in various combinations (Adjective-Adverb, Adjective-Verb, Adverb-Verb, and Adjective-Adverb-Verb). The findings demonstrate that adjectives can accurately classify sentiments into seven intensity levels. Notably, the Naïve Bayes classifier achieves high precision (0.984) when utilizing adjectives alone and (0.981) when combined with adverbs, outperforming other classifiers across six evaluated combinations.

Published
2023

27. Finding Relatedness between Research Papers Using Similarity and Dissimilarity Scores

Author

Mahmood, Qamar, Qadir, Muhammad Abdul, Afzal, Muhammad Tanvir, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Kobsa, Alfred, editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Weikum, Gerhard, editor, Li, Feifei, editor, Li, Guoliang, editor, Hwang, Seung-won, editor, Yao, Bin, editor, and Zhang, Zhenjie, editor

Published
2014
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49. Extending co-citation using sections of research articles

Author

KHAN, Arjumand Yar, KHATTAK, Abdul Shahid, and AFZAL, Muhammad Tanvir

Subjects
Recommending relevant papers,co-citations,citation proximity analysis,in-text citation frequencies and patterns,citation analysis, GeneralLiterature_REFERENCE(e.g.,dictionaries,encyclopedias,glossaries)
Abstract

The excessive amount of digital information has made it crucial to extract the relevant information. This hinders researchers in finding documents pertaining to their research. There exist various state-of-the-art techniques, such as co-citation, bibliographic coupling, and their recent extensions like citation proximity analysis and citation order analysis, that recommend the relevant documents against the posed query. Most of these approaches are statistical in nature and thus can further be extended by incorporating some semantics to enhance the results. In this paper, we present an extension of a co-citation-based technique to identify the most relevant documents to co-cited document(s). The proposed system explores in-text citation frequencies and in-text citation patterns of co-cited documents within the different logical sections of cited-by papers. Furthermore, we have evaluated the proposed approach with the co-citation approach and citation proximity analysis (CPA) approach on a dataset acquired from CiteSeer. The outcomes revealed that most of the time the proposed approach outperformed other state-of-the-art techniques. The average correlation of the proposed approach is increased by 68 % as compared to the co-citation-based approach. In comparison with CPA approach, the average correlation of the proposed approach is increased by 39 % with respect to gold-standard rankings.

Published
2018

50. SwCS: Section-Wise Content Similarity Approach to Exploit Scientific Big Data.

Author

Irshad, Kashif, Afzal, Muhammad Tanvir, Rizvi, Sanam Shahla, Shahid, Abdul, Riaz, Rabia, and Tae-Sun Chung

Subjects
BIG data, SCIENTIFIC knowledge, KNOWLEDGE base, COMPUTER science, ACQUISITION of data
Abstract

The growing collection of scientific data in various web repositories is referred to as Scientific Big Data, as it fulfills the four “V’s” of Big Data—volume, variety, velocity, and veracity. This phenomenon has created new opportunities for startups; for instance, the extraction of pertinent research papers from enormous knowledge repositories using certain innovative methods has become an important task for researchers and entrepreneurs. Traditionally, the content of the papers are compared to list the relevant papers from a repository. The conventional method results in a long list of papers that is often impossible to interpret productively. Therefore, the need for a novel approach that intelligently utilizes the available data is imminent. Moreover, the primary element of the scientific knowledge base is a research article, which consists of various logical sections such as the Abstract, Introduction, Related Work, Methodology, Results, and Conclusion. Thus, this study utilizes these logical sections of research articles, because they hold significant potential in finding relevant papers. In this study, comprehensive experiments were performed to determine the role of the logical sections-based terms indexing method in improving the quality of results (i.e., retrieving relevant papers). Therefore, we proposed, implemented, and evaluated the logical sections-based content comparisons method to address the research objective with a standard method of indexing terms. The section-based approach outperformed the standard content-based approach in identifying relevant documents from all classified topics of computer science. Overall, the proposed approach extracted 14% more relevant results from the entire dataset. As the experimental results suggested that employing a finer content similarity technique improved the quality of results, the proposed approach has led the foundation of knowledge-based startups. [ABSTRACT FROM AUTHOR]

Published
2021
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