What influences sentiment analysis on social networks: A case study

Autor: Giacomo Mambelli, Catia Prandi, Silvia Mirri
Přispěvatelé: Mambelli G., Prandi C., Mirri S.
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Zdroj: ISCC
2020 IEEE Symposium on Computers and Communications (ISCC)
Popis: Sentiment analysis, social networks analysis, and social media sensing are becoming important tools to extract meaningful information from text, adopted in several contexts, ranging from social interactions, touristic activities, shopping and e-commerce, to name a few. In particular, the current CoVid-19 quarantine the world is witnessing has shown the potential of such tools as a way to monitor and understand people’s mood and feelings, in a time where people are resorting, more than ever, to social networks to engage and communicate with others. Indeed, when performing social network content analysis, privacy is a major concern. On the one hand, privacy issues and international laws and acts drive such analysis (e.g., GDPR), with the aim of protecting persons’ privacy and security. On the other hand, these can limit somehow such activities. Hence, a precise and accurate identification of the strategies to adopt should be done to balance privacy issues and sentiment analysis activities. Taking into account the requirements of a Urban Innovation Action project, which is based on the active involvement of citizens, this work aims to describe limitations and potentialities of social networks monitoring and analysis to understand users’ mood about the project actions adopted in the city of Ravenna (in Italy) to improve specific districts.
Databáze: OpenAIRE