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Application of sentiment analysis in libraries to provide temporal information service: a case study on various facets of productivity

Abstract

With the advent of social media, people have found new ways through which they can express their views, opinions, and beliefs . This study presents an interdisciplinary nature of research where sentiment analysis is applied to the economics discipline of productivity as an experimental study to introduce new service for libraries’ users. Firstly, data were retrieved from Twitter on 20 different queries related to productivity using RapidMiner platform and then sentiment analysis was performed employing AYLIEN Text Analysis Software. A total of 6416 tweets were mined from Twitter for a period of 13 days. Further, 676 prominent hashtags had been identified where 83 hashtags were found to be associated with a geographical location in the tweets. It was observed that the United Kingdom was the most popular country which was being used as a hashtag on Twitter in relation to various facets of the productivity followed by India, the United States, China, and Nigeria. In regard to polarity, most of the tweets were found to be of neutral polarity and most of the positive tweets had a low percentage value. The information analyzed using this strategy can be repackaged as a consolidated time-based service and can be presented to libraries’ users in different formats. This study not only introduces a nascent way to cater to the information needs of today’s users, but also proposes a new way of conducting marketing in libraries using social media mining and sentiment analysis.

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Correspondence to Manika Lamba.

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Lamba, M., Madhusudhan, M. Application of sentiment analysis in libraries to provide temporal information service: a case study on various facets of productivity. Soc. Netw. Anal. Min. 8, 63 (2018). https://doi.org/10.1007/s13278-018-0541-y

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  • DOI: https://doi.org/10.1007/s13278-018-0541-y

Keywords

  • Information retrieval
  • Information repackaging
  • Sentiment analysis
  • Social media mining
  • Twitter
  • User services