Premium
Entity emotion mining in social media environment
Author(s) -
Fu Xuefeng,
Luo Xiangfeng,
Guo Yike
Publication year - 2019
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.5336
Subject(s) - interpretability , sentiment analysis , social media , microblogging , computer science , lexicon , thriving , mainstream , government (linguistics) , topic model , process (computing) , data science , natural language processing , information retrieval , semantics (computer science) , artificial intelligence , world wide web , psychology , linguistics , political science , philosophy , law , psychotherapist , programming language , operating system
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.