Microblog summarization based on sentiment and aspect analysis

On Twitter, the short nature of the post forces users to remain concise while conveying the main ideas to other users. Hence, the challenge is on how to use the unstructured texts to extract information that can be valuable for organizations. We investigate the best methodology to perform microblog summarization of topics discussed on Twitter. First, we classify the microblogs related to the topic into positive, negative, or neutral sentiments, and then we extract sub-topics (i.e., topic aspects), and pick the top N ranked aspects by sentiment temperature for final summarization. We utilize known algorithms for annotation, sentiment analysis, and clustering to determine which combination yields the best results. This paper attempts to address how sentiment analysis in conjunction with aspect extraction of topics can yield more effective summarization. Evaluation results show that sentiment analysis and aspect extraction improve the overall summarization of topics compared to baseline technique.