Entity emotion mining in social media environment. (14th June 2019)

• Record Type: Journal Article
• Authors: Fu, Xuefeng; Luo, Xiangfeng; Guo, Yike
• Is Part Of: Concurrency and computation. Volume 31:Number 20(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-06-14
• Subjects: emotion mining -- entity‐word relations -- social media environment -- topic model; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.5336
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: With the thriving of the social media (eg, Sina Microblog and Twitter), the public is keen on expressing their opinions or views on entities as celebrities and products. Emotion mining on social media can be applied in diverse areas, such as helping government or organizations understand people's attitudes so as to make right decisions for business services or political campaigns. One kind of the mainstream approaches for emotion mining are based on topic models, while most of these existing approaches aim at analyzing entire sentiments for the whole Microblog, rather than explicitly assigning the relevant sentiments to the specific entities. In addition, topic model is mainly based on the bag‐of‐words model but ignores the semantic relations of entity‐word in the modeling process, which brings low accuracy and poor interpretability to the sentiment analysis. To overcome the aforementioned difficulties, an Entity Sentiment Topic Model (ESTM) is proposed, which carries out entity‐dependent sentiment analysis. To improve the accuracy of sentiment analysis and enhance the interpretability of the results, ESTM is integrated with relations of entity‐word and a six‐dimensional emotion lexicon as weakly supervised information. Experiments have shown promising results on sentiment classification accuracy, interpretability, and quality of coherent topics for entities.