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Aspect based citation sentiment analysis using linguistic patterns for better comprehension of scientific knowledge

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Abstract

An almost unrestrained access to research plethora has emerged with a potential drawback: extracting relevant scientific publications is not a straightforward task anymore. The best way is to search on citation indexes, which also provide large number of pertinent papers and when a paper is focused even then it ascertains thousands of citations. In such a scenario, citation text could be a quintessential indicator in determining the importance and relevancy of paper for the researcher based on different aspects of the cited work such as technique, corpus, method, task, concept, measure, model and tool etc. This paper presents a novel approach to identify aspect level sentiments to reveal the hidden patterns from scholarly big data. The proposed methodology comprises of two levels. At first level, it extracts the aspects from the citation sentences using the pattern of opinionated phrases around the aspect. At the second level, it detects the sentiment polarity of the identified aspect considering nearby words and associates it with the corresponding aspect category based on a linguistic rule-based approach. We consider the words before, after and around the aspect using n-gram based features: ‘N-gram after’, ‘N-gram before’ and ‘N-gram around’. Our results reveal that ‘N-gram around’ feature performed better than other features and the SVM outperformed other considered classifiers for all N-gram models.

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References

  • Abirami, A. M., & Askarunisa, A. (2016). Feature based sentiment analysis for service reviews. Journal of Universal Computer Science, 22(5), 650–670.

    MathSciNet  Google Scholar 

  • Agarwal, B., & Mittal, N. (2016a). Prominent feature extraction for review analysis: An empirical study. Journal of Experimental and Theoretical Artificial Intelligence, 28(3), 485–498.

    Article  Google Scholar 

  • Agarwal, B., & Mittal, N. (2016b). Machine learning approach for sentiment analysis. In Prominent feature extraction for sentiment analysis, Socio-Affective Computing (pp. 21–45). Springer, Cham. https://doi.org/10.1007/978-3-319-25343-5_3.

    Chapter  Google Scholar 

  • Anand, D., & Naorem, D. (2016). Semi-supervised aspect based sentiment analysis for movies using review filtering. Procedia Computer Science, 84, 86–93.

    Article  Google Scholar 

  • Appel, O., Chiclana, F., Carter, J., & Fujita, H. (2016). A hybrid approach to the sentiment analysis problem at the sentence level. Knowledge-Based Systems, 108, 110–124.

    Article  Google Scholar 

  • Athar, A. (2011). Sentiment analysis of citations using sentence structure-based features. In Proceedings of the ACL 2011 student session (pp. 81–87). Association for Computational Linguistics.

  • Bar-Ilan, J., & Halevi, G. (2017). Post retraction citations in context: A case study. Scientometrics, 113(1), 547–565.

    Article  Google Scholar 

  • Bertin, M., Atanassova, I., Sugimoto, C. R., & Lariviere, V. (2016). The linguistic patterns and rhetorical structure of citation context: An approach using n-grams. Scientometrics, 109(3), 1417–1434.

    Article  Google Scholar 

  • del Pilar Salas-Zárate, M., Valencia-García, R., Ruiz-Martínez, A., & Colomo-Palacios, R. (2016). Feature-based opinion mining in financial news: An ontology-driven approach. Journal of Information Science. https://doi.org/10.1177/0165551516645528.

    Google Scholar 

  • Er, M. J., Liu, F., Wang, N., Zhang, Y., & Pratama, M. (2016). User-level twitter sentiment analysis with a hybrid approach. In International symposium on neural networks (pp. 426–433). Springer.

  • Ghosh, S., Das, D., & Chakraborty, T. (2017). Determining sentiment in citation text and analyzing its impact on the proposed ranking index. arXiv preprint arXiv:1707.01425.

  • Hernández, M., & Gómez, J. M. (2015). Sentiment, polarity and function analysis in bibliometrics: A review. In Natural language processing and cognitive science: proceedings 2014 (p. 149).

  • Hernández-Alvarez, M., & Gomez, J. M. (2016). Survey about citation context analysis: Tasks, techniques, and resources. Natural Language Engineering, 22(03), 327–349.

    Article  Google Scholar 

  • Hernández-Álvarez, M., Gómez Soriano, J., & Martínez-Barco, P. (2016). Annotated corpus for citation context analysis. Latin American Journal of Computing Faculty of Systems Engineering National Polytechnic School Quito-Ecuador, 3(1), 35–42.

    Google Scholar 

  • Ikram, M. T., Butt, N. A., & Afzal, M. T. (2016). Open source software adoption evaluation through feature level sentiment analysis using Twitter data. Turkish Journal of Electrical Engineering and Computer Sciences, 24(5), 4481–4496.

    Article  Google Scholar 

  • Jochim, C., & Schütze, H. (2012). Towards a generic and flexible citation classifier based on a faceted classification scheme. In Proceedings of the 2012 international conference on computational linguistics (pp. 1343–1358).

  • Kazi, P., Patwardhan, M., & Joglekar, P. (2016). Towards a new perspective on context based citation index of research articles. Scientometrics, 107(1), 103–121.

    Article  Google Scholar 

  • Khalid, A., Alam, F., & Ahmed, I. (2017). Extracting reference text from citation contexts. Cluster Computing, 1, 1–18.

    Google Scholar 

  • Kilicoglu, H. (2017). Biomedical text mining for research rigor and integrity: Tasks, challenges, directions. Briefings in Bioinformatics, 19(6), 1400–1414. https://doi.org/10.1093/bib/bbx057.

    Google Scholar 

  • Liu, S., Chen, C., Ding, K., Wang, B., Xu, K., & Lin, Y. (2014). Literature retrieval based on citation context. Scientometrics, 101(2), 1293–1307.

    Article  Google Scholar 

  • Lu, C., Ding, Y., & Zhang, C. (2017). Understanding the impact change of a highly cited article: A content-based citation analysis. Scientometrics, 112(2), 927–945.

    Article  Google Scholar 

  • Luna-Aveiga, H., Medina-Moreira, J., Lagos-Ortiz, K., Apolinario, O., Paredes-Valverde, M. A., del Pilar Salas-Zárate, M., et al. (2017). Sentiment polarity detection in social networks: An approach for asthma disease management. In International conference on computer science, applied mathematics and applications (pp. 141–152). Cham: Springer.

    Google Scholar 

  • Manek, A. S., Shenoy, P. D., Mohan, M. C., & Venugopal, K. R. (2016). Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier. World Wide Web, 20(2), 1–20.

    Google Scholar 

  • Penalver-Martinez, I., Garcia-Sanchez, F., Valencia-Garcia, R., Rodriguez-Garcia, M. A., Moreno, V., Fraga, A., et al. (2014). Feature-based opinion mining through ontologies. Expert Systems with Applications, 41(13), 5995–6008.

    Article  Google Scholar 

  • Quan, C., & Ren, F. (2016). Feature-level sentiment analysis by using comparative domain corpora. Enterprise Information Systems, 10(5), 505–522.

    Article  Google Scholar 

  • Rana, T. A., & Cheah, Y. N. (2016). Aspect extraction in sentiment analysis: Comparative analysis and survey. Artificial Intelligence Review, 46(4), 1–25.

    Article  Google Scholar 

  • Saggion, H., & Ronzano, F. (2016). Trainable citation-enhanced summarization of scientific articles. In Proceedings of the joint workshop on bibliometric-enhanced information retrieval and natural language processing for digital libraries (BIRNDL) (pp. 175–186).

  • Salas-Zárate, M. D. P., Medina-Moreira, J., Lagos-Ortiz, K., Luna-Aveiga, H., Rodríguez-García, M. Á., & Valencia-García, R. (2017). Sentiment analysis on tweets about diabetes: An aspect-level approach. Computational and Mathematical Methods in Medicine, 2017, 1–9. https://doi.org/10.1155/2017/5140631.

    Article  Google Scholar 

  • Tandon, N., & Jain, A. (2012). Citation context sentiment analysis for structured summarization of research papers. In 35th German conference on artificial intelligence (pp. 98–102). Springer.

  • Valenzuela, M., Ha, V., & Etzioni, O. (2015). Identifying meaningful citations. In AAAI workshop: Scholarly big data.

  • Wan, X., & Liu, F. (2014). Are all literature citations equally important? Automatic citation strength estimation and its applications. Journal of the Association for Information Science and Technology, 65(9), 1929–1938.

    Article  Google Scholar 

  • Widyantoro, D. H., & Amin, I. (2014). Citation sentence identification and classification for related work summarization. In 2014 International conference on advanced computer science and information systems (ICACSIS) (pp. 291–296). IEEE.

  • Xu, J., Zhang, Y., Wu, Y., Wang, J., Dong, X., & Xu, H. (2015). Citation sentiment analysis in clinical trial papers. In AMIA annual symposium proceedings (Vol. 2015, p. 1334). American Medical Informatics Association.

  • Zainuddin, N., Selamat, A., & Ibrahim, R. (2016). Improving twitter aspect-based sentiment analysis using hybrid approach. In Asian conference on intelligent information and database systems (pp. 151–160). Berlin: Springer.

    Chapter  Google Scholar 

  • Zainuddin, N., Selamat, A., & Ibrahim, R. (2016). Twitter feature selection and classification using support vector machine for aspect-based sentiment analysis. In International conference on industrial, engineering and other applications of applied intelligent systems (pp. 269–279). Springer.

  • Zhang, X., Chen, H., Wang, W., & Ordóñez de Pablos, P. (2016). What is the role of IT in innovation? A bibliometric analysis of research development in IT innovation. Behaviour and Information Technology, 35(12), 1130–1143.

    Article  Google Scholar 

  • Zhu, X., Turney, P., Lemire, D., & Vellino, A. (2015). Measuring academic influence: Not all citations are equal. Journal of the Association for Information Science and Technology, 66(2), 408–427.

    Article  Google Scholar 

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Authors and Affiliations

  1. Computer Science Department, Capital University of Science & Technology, Islamabad, Pakistan

    Muhammad Touseef Ikram & Muhammad Tanvir Afzal

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  1. Muhammad Touseef Ikram
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  2. Muhammad Tanvir Afzal
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Correspondence to Muhammad Touseef Ikram.

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Ikram, M.T., Afzal, M.T. Aspect based citation sentiment analysis using linguistic patterns for better comprehension of scientific knowledge. Scientometrics 119, 73–95 (2019). https://doi.org/10.1007/s11192-019-03028-9

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