Open AccessMethodology for sentiment analysis in twitter posts about mobile learning
Author(s) -
Hernán Gil-Ramírez,
Rosa María Guilleumas García
Publication year - 2021
Publication title -
south florida journal of development
Language(s) - English
Resource type - Journals
ISSN - 2675-5459
DOI - 10.46932/sfjdv2n2-125
Subject(s) - influencer marketing , computer science , field (mathematics) , sentiment analysis , process (computing) , relation (database) , data science , social network analysis , social media , world wide web , social network (sociolinguistics) , network analysis , order (exchange) , artificial intelligence , data mining , engineering , mathematics , finance , marketing , relationship marketing , pure mathematics , electrical engineering , business , economics , marketing management , operating system
Analysis of social networks has become of great interest to researchers from different areas, including educators, due to Twitter’s growing importance as a space for discussion and dissemination of knowledge and opinions. This reality demands the development of analysis processes that allow to know the topics of interest in the network, the positive or negative feelings in relation to those topics and who the network influencers are. Those objectives guided this research work and in order to achieve them, we developed a methodological proposal for sentiment analysis of tweets. This article describes the process followed, which involved 1) detecting the structure of the communication network, 2) calculating the general metrics, 3) representing the communication network, 4) identifying and analyzing the clusters, 5) calculating their metrics as well as those of the individual nodes and 6) establishing the polarity of the posts published in the network. This paper also describes the methodoly followed to identify trends and topics of interest in the hashtags and web domains included in the tweets. The proposal for analysis presented here is intended to help researchers interested in the field of social networks, to understand the complex interactions that take place in these environments and the way in which information is disseminated, valued and converted into topics of interest thanks to the network users’ actions.