Discovering Topical Aspects in Microblogs

Abstract We address the problem of discovering topical phrases or "aspects" from microblogging sites like Twitter, that correspond to key talking points or buzz around a particular topic or entity of interest. Inferring such topical aspects enables various applications such as trend detection and opinion mining for business analytics. However, mining high-volume microblog streams for aspects poses unique challenges due to the inherent noise, redundancy and ambiguity in users' social posts. We address these challenges by using a probabilistic model that incorporates various global and local indicators such as "uniqueness", "diversity" and "burstiness" of phrases, to infer relevant aspects. Our model is learned using an EM algorithm that uses automatically generated noisy labels, without requiring manual effort or domain knowledge. We present results on three months of Twitter data across different types of entities to validate our approach

ASPECT-BASED OPINION MINING OF PRODUCT REVIEWS IN MICROBLOGS USING MOST RELEVANT FREQUENT CLUSTERS OF TERMS

Aspect-based Opinion Mining (ABOM) systems take as input a corpus about a product and aim to mine the aspects (the features or parts) of the product and obtain the opinions of each aspect (how positive or negative the appraisal or emotions towards the aspect is). A few systems like Twitter Aspect Classifier and Twitter Summarization Framework have been proposed to perform ABOM on microblogs. However, the accuracy of these techniques are easily affected by spam posts and buzzwords. In this thesis we address this problem of removing noisy aspects in ABOM by proposing an algorithm called Microblog Aspect Miner (MAM). MAM classifies the microblog posts into subjective and objective posts, represents the frequent nouns in the subjective posts as vectors, and then clusters them to obtain relevant aspects of the product. MAM achieves a 50% improvement in accuracy in obtaining relevant aspects of products compared to previous systems