Multilingual Connotation Frames: A Case Study on Social Media for Targeted Sentiment Analysis and Forecast

Conference · Sun Jul 30 04:00:00 EDT 2017

DOI:https://doi.org/10.18653/v1/P17-2073· OSTI ID:1373867

Rashkin, Hannah J.; Bell, Eric B.; Choi, Yejin; Volkova, Svitlana

People around the globe respond to major real world events through social media. To study targeted public sentiments across many languages and geographic locations, we introduce multilingual connotation frames: an extension from English connotation frames of Rashkin et al. (2016) with 10 additional European languages, focusing on the implied sentiments among event participants engaged in a frame. As a case study, we present large scale analysis on targeted public sentiments using 1.2 million multilingual connotation frames extracted from Twitter. We rely on connotation frames to build models to forecast country-specific connotation dynamics – perspective change over time towards salient entities and events. Our results demonstrate that connotation dynamics can be accurately predicted up to half a week in advance.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (US)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 1373867
• Report Number(s): PNNL-SA-124156; 453040300
• Country of Publication: United States
• Language: English

Related Subjects

connotation frames
deep learning
forecasting
multilingual
sentiment analysis