Semi-supervised Learning for Affective Common-Sense Reasoning

Big social data analysis is the area of research focusing on collecting, examining, and processing large multi-modal and multi-source datasets in order to discover patterns/correlations and extract information from the Social Web. This is usually accomplished through the use of supervised and unsupervised machine learning algorithms that learn from the available data. However, these are usually highly computationally expensive, either in the training or in the prediction phase, as they are often not able to handle current data volumes. Parallel approaches have been proposed in order to boost processing speeds, but this clearly requires technologies that support distributed computations. Extreme learning machines (ELMs) are an emerging learning paradigm, presenting an efficient unified solution to generalized feed-forward neural networks. ELM offers significant advantages such as fast learning speed, ease of implementation, and minimal human intervention. However, ELM cannot be easily parallelized, due to the presence of a pseudo-inverse calculation. Therefore, this paper aims to find a reliable method to realize a parallel implementation of ELM that can be applied to large datasets typical of Big Data problems with the employment of the most recent technology for parallel in-memory computation, i.e., Spark, designed to efficiently deal with iterative procedures that recursively perform operations over the same data. Moreover, this paper shows how to take advantage of the most recent advances in statistical learning theory (SLT) in order to address the issue of selecting ELM hyperparameters that give the best generalization performance. This involves assessing the performance of such algorithms (i.e., resampling methods and in-sample methods) by exploiting the most recent results in SLT and adapting them to the Big Data framework. The proposed approach has been tested on two affective analogical reasoning datasets. Affective analogical reasoning can be defined as the intrinsically human capacity to interpret the cognitive and affective information associated with natural language. In particular, we employed two benchmarks, each one composed by 21,743 common-sense concepts; each concept is represented according to two models of a semantic network in which common-sense concepts are linked to a hierarchy of affective domain labels. The labeled data have been split into two sets: The first 20,000 samples have been used for building the model with the ELM with the different SLT strategies, while the rest of the labeled samples, numbering 1743, have been kept apart as reference set in order to test the performance of the learned model. The splitting process has been repeated 30 times in order to obtain statistically relevant results. We ran the experiments through the use of the Google Cloud Platform, in particular, the Google Compute Engine. We employed the Google Compute Engine Platform with NM = 4 machines with two cores and 1.8 GB of RAM (machine type n1-highcpu-2) and an HDD of 30 GB equipped with Spark. Results on the affective dataset both show the effectiveness of the proposed parallel approach and underline the most suitable SLT strategies for the specific Big Data problem. In this paper we showed how to build an ELM model with a novel scalable approach and to carefully assess the performance, with the use of the most recent results from SLT, for a sentiment analysis problem. Thanks to recent technologies and methods, the computational requirements of these methods have been improved to allow for the scaling to large datasets, which are typical of Big Data applications.