A review onquantification learning

The task of quantification consists in providing an aggregate estimation (e.g. the class distribution in a classification problem) for unseen test sets, applying a model that is trained using a training set with a different data distribution. Several real-world applications demand this kind of methods that do not require predictions for individual examples and just focus on obtaining accurate estimates at an aggregate level. During the past few years, several quantification methods have been proposed from different perspectives and with different goals. This paper presents a unified review of the main approaches with the aim of serving as an introductory tutorial for newcomers in the fiel

Sentiment Estimation on Twitter

Abstract We study the classifier quantification problem in the context of the topical opinion retrieval, that consists in estimating proportions of the sentiment categories in the result set of a topic. We propose a methodology to circumvent individual classification allowing a real-time sentiment analysis for huge volumes of data. After discussing existing approaches to quantification, the novel proposed methodology is applied to Microblogging Retrieval and provides statistically significant estimates of sentiment category proportions. Our solution modifies Hopkins and King’s approach in order to remove manual intervention, and making sentiment analysis feasible in real time. Evaluation is conduced with a test collection made up of about 3,2M tweets.