Start Date
12-13-2015
Description
The massive amount of data in social media platforms is a key source for companies to analyze customer sentiment and opinions. Many existing sentiment analysis approaches solely rely on textual contents of a sentence (e.g. words) for sentiment identification. Consequently, current sentiment analysis systems are ineffective for analyzing contents in social media because people may use non-standard language (e.g., abbreviations, misspellings, emoticons or multiple languages) in online platforms. Inspired by the attribution theory that is grounded in social psychology, we propose a sentiment analysis framework that considers the social relationships among users and contents. We conduct experiments to compare the proposed approach against the existing approaches on a dataset collected from Facebook. The results indicate that we can more accurately classify sentiment of sentences by utilizing social relationships.
Recommended Citation
Fan, Shaokun; Ilk, Noyan; and Zhang, Kunpeng, "Sentiment Analysis in Social Media Platforms: The Contribution of Social Relationships" (2015). ICIS 2015 Proceedings. 10.
https://aisel.aisnet.org/icis2015/proceedings/DecisionAnalytics/10
Sentiment Analysis in Social Media Platforms: The Contribution of Social Relationships
The massive amount of data in social media platforms is a key source for companies to analyze customer sentiment and opinions. Many existing sentiment analysis approaches solely rely on textual contents of a sentence (e.g. words) for sentiment identification. Consequently, current sentiment analysis systems are ineffective for analyzing contents in social media because people may use non-standard language (e.g., abbreviations, misspellings, emoticons or multiple languages) in online platforms. Inspired by the attribution theory that is grounded in social psychology, we propose a sentiment analysis framework that considers the social relationships among users and contents. We conduct experiments to compare the proposed approach against the existing approaches on a dataset collected from Facebook. The results indicate that we can more accurately classify sentiment of sentences by utilizing social relationships.