Abstract
Conflict detection and management is a much-needed strategy in educational institutions nowadays. The frequency of clashes, protests, strikes and agitations is rising at an alarming level, particularly with the extensive usage of information and communication technology. This unprecedented issue has cobblestone the research problem and the objective of this study. In order to ensure detection of conflicts using a modified Naïve Bayes algorithm that would assess the sentiments and mood recognition from the tweets of the stakeholders with respect to trends leading to clashes, protests, strikes and agitations existing within the educational environment. Consequently, continuous monitoring and surveillance of the social networking platform have been made to understand the mood recognition and assessment from real-time tweets published on Twitter that would be streamed through Twitter Application Program Interface over the user-given text that would serve as an input for developing early conflict detection strategies. Moreover, Stanford parser has been used to extract keywords that would subsequently be deployed over the tweets for the mood detection module to evaluate the keywords using the preprocessed dictionary. Here, the mood detection module extracts various mood states from the given chorus that supplements the current prevailing sentiments of the stakeholders in academic institutions. This extraction would result in devising strategic formulation and implementation of remedial measures for resolving the unrest in an environment. This would culminate in a healthy educational environment that would ultimately result in overall growth, development, harmony and prosperity of the institutions.
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Data availability
Data were mainly collected from primary and secondary sources. Developed the algorithm code and extracted data by API (Application Program Interface). Data was also collected from news website and research articles.
Code availability
The software applications used for developing code are JAVA Development Environment (along with NetBeans IDE 8.2), Twitter API, Stanford Parser API (Application Program Interface).
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The research study was possible because of the authors’ dedication and research team's devotion. We are also obliged and grateful to the Indian educational institutions for providing there esteemed support and revered cooperation.
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Sengupta, S., Vaish, A. Social networking mood recognition algorithm for conflict detection and management of Indian educational institutions. Soc. Netw. Anal. Min. 10, 89 (2020). https://doi.org/10.1007/s13278-020-00701-3
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DOI: https://doi.org/10.1007/s13278-020-00701-3