Deriving Pertinent Knowledge through Sentiment Analysis and Linking with Relevant Documents


  • Department of Library and Information Science, Jadavpur University, Kolkata, West Bengal
  • Department of Library and Information Science, Jadavpur University, Kolkata, West Bengal
  • Department of Library and Information Science, Jadavpur University, Kolkata, West Bengal



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


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.


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Thelwall, M. (2011). Sentiment in twitter events. Journal of American Society for Information Science and Technology, 62(2): 406-418.

Boiy, E. (2009). A machine learning approach to sentiment analysis in multilingual web texts. Information Retrieval, 12(5): 526-558.

Tripathy, A. (2015). Classification of sentimental reviews using machine learning techniques. Procedia Computer Science, 57: 821-829. 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. 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.

Medhat, W. (2014). Sentiment analysis algorithms and applications: A survey. A in Shams Engineering Journal, 5(4): 1093-1113.

Dey, L. (2009). Opinion Mining from Noisy Text Data, AND ‘o8: Proceedings of the Second Work Hop on Analytics for Noisy Unstructured Text Data; p. 83-90.

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.

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.

Pang, B. (2002). Thumbs up? Sentiment Classification Using Machine Learning Techniques. In: Proceedings of EMNLP; 2002.

Mullen, T. and Collier, N. (2004). Sentiment Analysis using Support Vector Machines with Diverse Information Sources, In: Dekang Lin & Dekai Wu (Eds.). Proceedings of EMNLP-2004, Barcelona, Spain; July 2004. p. 412-418.



How to Cite

Chatterjee, A., Mahato, S., & Kumar Chatterjee, S. (2021). Deriving Pertinent Knowledge through Sentiment Analysis and Linking with Relevant Documents. Journal of Information and Knowledge, 58(5), 319–331.



Received 2021-05-13
Accepted 2021-10-26
Published 2021-10-30