Title :
Harvesting Opinions and Emotions from Social Media Textual Resources
Author :
Chatzakou, Despoina ; Vakali, Athena
Author_Institution :
Aristotle Univ. of Thessaloniki, Thessaloniki, Greece
Abstract :
Harvesting sentiments from social media textual resources can reveal insightful information. The understanding and modeling of such resources are key requirements for accurately capturing the conveyed sentiments. Here, the authors consider multiple approaches, with an emphasis on detecting sentiments in Web 2.0 textual resources.
Keywords :
behavioural sciences computing; emotion recognition; social networking (online); text analysis; Web 2.0 textual resources; emotion harvesting; opinion harvesting; sentiment detection; social media textual resources; Adaptation models; Analytical models; Filtering; Media; Sentiment analysis; Text processing; Web 2.0; Internet/Web technologies; sentiment analysis; textual resources;
Journal_Title :
Internet Computing, IEEE
DOI :
10.1109/MIC.2015.28
