Abstract
Analyzing massive user-generated microblogs is very crucial in many fields, attracting many researchers to study. However, it is very challenging to process such noisy and short microblogs. Most prior works only use texts to identify sentiment polarity and assume that microblogs are independent and identically distributed, which ignore microblogs are networked data. Therefore, their performance is not usually satisfactory. Inspired by two sociological theories (sentimental consistency and emotional contagion), in this paper, we propose a new method combining social context and topic context to analyze microblog sentiment. In particular, different from previous work using direct user relations, we introduce structure similarity context into social contexts and propose a method to measure structure similarity. In addition, we also introduce topic context to model the semantic relations between microblogs. Social context and topic context are combined by the Laplacian matrix of the graph built by these contexts and Laplacian regularization are added into the microblog sentiment analysis model. Experimental results on two real Twitter datasets demonstrate that our proposed model can outperform baseline methods consistently and significantly.
Highlights
It is a very challenging task to get users’ real sentiment from large collections of short user-generated social media contents
We find that the ratio of SS and SS-T is much higher than chance on both HCR and OMD in Fig 7, that is, there is a positive relation between structure similarity and sentiment labels, which paves the way for our study: how to exploit and model structure similarity into the microblog sentiment analysis system
We can see that adding topic context can improve the accuracy of microblog sentiment analysis to some extent
Summary
It is a very challenging task to get users’ real sentiment from large collections of short user-generated social media contents (e.g. microblogs). It is of great value and has a wide range of application prospects to mining users’ sentiment, such as customer relationship management, recommendation systems, and business intelligence [1,2,3]. Its performance drops sharply when it is applied to microblog sentiment analysis as it assumes that texts are independent and identically distributed (i.i.d.). Compared with long formal texts, microblogs are much shorter and have various expression style, e.g., ‘lol’ and ‘It is so coooooooool!’, which exacerbates the problem of vocabulary sparsity. Social media provides different types of metadata, such as user relations, which can be leveraged to improve the accuracy of microblog sentiment analysis
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