Topic-Specific Emotion Mining Model for Online Comments

Semantic Emotion-Topic Model Based Social Emotion Mining

Sentiment topic models for social emotion mining

The rapid development of social media services has facilitated the communication of opinions throughonlinenews, blogs,microblogs/tweets, instant-messages, and so forth. This article concentrates on the mining of readers’ emotions evoked by social media materials. Compared to the classical sentiment analysis from writers’ perspective, sentiment analysis of readers is sometimes more meaningful in...

A Topic Model for Building Fine-grained Domain-specific Emotion Lexicon

Emotion lexicons play a crucial role in sentiment analysis and opinion mining. In this paper, we propose a novel Emotion-aware LDA (EaLDA) model to build a domainspecific lexicon for predefined emotions that include anger, disgust, fear, joy, sadness, surprise. The model uses a minimal set of domain-independent seed words as prior knowledge to discover a domainspecific lexicon, learning a fine-...

Improving Topic Model Clustering of Newspaper Comments for Summarisation

Online newspaper articles can accumulate comments at volumes that prevent close reading. Summarisation of the comments allows interaction at a higher level and can lead to an understanding of the overall discussion. Comment summarisation requires topic clustering, comment ranking and extraction. Clustering must be robust as the subsequent extraction relies on a good set of clusters. Comment dat...

Topic Modeling and Classification of Cyberspace Papers Using Text Mining

The global cyberspace networks provide individuals with platforms to can interact, exchange ideas, share information, provide social support, conduct business, create artistic media, play games, engage in political discussions, and many more. The term cyberspace has become a conventional means to describe anything associated with the Internet and the diverse Internet culture. In fact, cyberspac...