Affiliations: Institute of Information Systems and Applications, National Tsing Hua University, Hsinchu, Taiwan
Correspondence:
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Corresponding author: Yi-Shin Chen, Institute of Information Systems and Applications, National Tsing Hua University, Hsinchu 30013, Taiwan. Tel.: +886 03 5731211; E-mail:[email protected]
Abstract: Expanding social networks have led to a collateral growth of user generated content on the web. Micro-blogs have positioned themselves as a very common and popular channel of expression. In recent years there has been an incremental understanding that if these opinions are analyzed and interpreted correctly they can provide useful information such as understanding how people feel or react towards a specific topic. A broad version of this task attempts to determine if a given text is an expression of positive or negative opinion. More detailed alternatives classify texts into specific emotion labels. This has made it crucial to devise algorithms that efficiently identify the emotions expressed within the opinionated content.
This work proposes an unsupervised graph-based algorithm to extract emotion bearing patterns from micro-blog posts. Having the extracted patterns, a classifier is implemented to efficiently identify the emotions expressed in posts without depending on predefined emotional dictionaries, lexicons or ontologies.
The system also considers that posts maybe written in multiple languages. It then takes advantage of the pattern extraction method to successfully perform in different languages, domains and data sets. Experimental results are shown for English, Spanish and French tweets and achieve a desired accuracy, generality, adaptability and minimal supervision.
