Journal of Information and Knowledge (Formerly SRELS Journal of Information Management)

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Deriving Pertinent Knowledge through Sentiment Analysis and Linking with Relevant Documents


Affiliations
1 Department of Library and Information Science, Jadavpur University, Kolkata, West Bengal, India
     

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Purpose: This study aims to explore pertinent knowledge through the Sentiment Analysis technique and to link with relevant, pin-pointed documents. Design/Methodology/Approach: While information is essential ‘information overload’ is a big problem when we search for specific information. To get rid of psychological stress, mistakes in decision making or disregarding of relevant information, a methodology has been developed which may be suitable for researchers to extract pertinent knowledge from huge amount of research publications in a particular domain (‘climatology’ has been chosen for demonstration) within the shortest possible time. The study presents, how exactly relevant information can be retrieved there through sentiment analysis and through which a preliminary knowledge base can be gained. For this, ‘R’ software has been used to do the desired manipulation on the collected data. The steps involve pre-processing of introductory text, tokenization, polarity detection and analysis of text through sentiment analysis. Findings: It has been found that knowledge derived through sentiment analysis and abstract of the linked documents fairly match with each other, which validates the relevance and importance of the linked documents. Again, the impact factor of the prestigious journal having global coverage, where most of the linked documents were published also shows the importance of the linked documents/papers.

Keywords

Information Extraction, Information Overload, Pertinent Knowledge, Polarity Dataset, Sentiment Analysis, Subjective Analysis.
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About The Authors

Apala Chatterjee
Department of Library and Information Science, Jadavpur University, Kolkata, West Bengal
India

Shampa Mahato
Department of Library and Information Science, Jadavpur University, Kolkata, West Bengal
India

Sunil Kumar Chatterjee
Department of Library and Information Science, Jadavpur University, Kolkata, West Bengal
India


Notifications

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  • Boiy, E. (2009). A machine learning approach to sentiment analysis in multilingual web texts. Information Retrieval, 12(5): 526-558. https://doi.org/10.1007/s10791-008-9070-z.

  • Tripathy, A. (2015). Classification of sentimental reviews using machine learning techniques. Procedia Computer Science, 57: 821-829. https://doi.org/10.1016/j. procs.2015.07.523.

  • Collomb, A. (2013). A study and comparison of sentiment analysis methods for reputation evaluation, Computer Science.

  • Zhou, L. (2008). Ontology-supported polarity mining. Journal of the American Society for Information Science and Technology, 59(1): 98-110. https://doi.org/10.1002/ asi.20735.

  • Mantyla, M. V. (2018). The evolution of sentiment analysis - A review of research topics, venues, and top cited papers. Computer Science Review, 27: 16-32. https://doi.org/10.1016/j.cosrev.2017.10.002.

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  • Iqbal, F. (2019). A hybrid framework for sentiment analysis using genetic algorithm based feature reduction. IEEE Access, 99.

  • Turney, P. D. (2002). Thumbs up or thumbs down? Sentiment Orientation Applied to Unsupervised Classification of Reviews, Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL); p. 417- 424. https://doi.org/10.3115/1073083.1073153.

  • Hatzivassiloglou, V. (2000). Effects of Adjective Orientation and Grad Ability on Sentence Subjectivity. COLING ‘OO: Proceedings of the 18th Conference on Computational Linguistics; Vol.1, p. 299-305. https://doi. org/10.3115/990820.990864.

  • Liu, B. (2009). Handbook Chapter: Sentiment Analysis and Subjectivity, Handbook of Natural Language Processing, Marcel Dekker, Inc. New York, NY, USA.

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  • Deriving Pertinent Knowledge through Sentiment Analysis and Linking with Relevant Documents

Abstract Views: 319  |  PDF Views: 5

Authors

Apala Chatterjee
Department of Library and Information Science, Jadavpur University, Kolkata, West Bengal, India
Shampa Mahato
Department of Library and Information Science, Jadavpur University, Kolkata, West Bengal, India
Sunil Kumar Chatterjee
Department of Library and Information Science, Jadavpur University, Kolkata, West Bengal, India

Abstract


Purpose: This study aims to explore pertinent knowledge through the Sentiment Analysis technique and to link with relevant, pin-pointed documents. Design/Methodology/Approach: While information is essential ‘information overload’ is a big problem when we search for specific information. To get rid of psychological stress, mistakes in decision making or disregarding of relevant information, a methodology has been developed which may be suitable for researchers to extract pertinent knowledge from huge amount of research publications in a particular domain (‘climatology’ has been chosen for demonstration) within the shortest possible time. The study presents, how exactly relevant information can be retrieved there through sentiment analysis and through which a preliminary knowledge base can be gained. For this, ‘R’ software has been used to do the desired manipulation on the collected data. The steps involve pre-processing of introductory text, tokenization, polarity detection and analysis of text through sentiment analysis. Findings: It has been found that knowledge derived through sentiment analysis and abstract of the linked documents fairly match with each other, which validates the relevance and importance of the linked documents. Again, the impact factor of the prestigious journal having global coverage, where most of the linked documents were published also shows the importance of the linked documents/papers.

Keywords


Information Extraction, Information Overload, Pertinent Knowledge, Polarity Dataset, Sentiment Analysis, Subjective Analysis.

References