Federica Bisio

An ELM-based model for affective analogical reasoning

Between the dawn of the Internet through year 2003, there were just a few dozens exabytes of information on the Web. Today, that much information is created weekly. The opportunity to capture the opinions of the general public about social events, political movements, company strategies, marketing campaigns, and product preferences has raised increasing interest both in the scientific community, for the exciting open challenges, and in the business world, for the remarkable fallouts in marketing and financial prediction. Keeping up with the ever-growing amount of unstructured information on the Web, however, is a formidable task and requires fast and efficient models for opinion mining. In this paper, we explore how the high generalization performance, low computational complexity, and fast learning speed of extreme learning machines can be exploited to perform analogical reasoning in a vector space model of affective common-sense knowledge. In particular, by enabling a fast reconfiguration of such a vector space, extreme learning machines allow the polarity associated with natural language concepts to be calculated in a more dynamic and accurate way and, hence, perform better concept-level sentiment analysis.

Sentiment Data Flow Analysis by Means of Dynamic Linguistic Patterns

Emulating the human brain is one of the core challenges of computational intelligence, which entails many key problems of artificial intelligence, including understanding human language, reasoning, and emotions. In this work, computational intelligence techniques are combined with common-sense computing and linguistics to analyze sentiment data flows, i.e., to automatically decode how humans express emotions and opinions via natural language. The increasing availability of social data is extremely beneficial for tasks such as branding, product positioning, corporate reputation management, and social media marketing. The elicitation of useful information from this huge amount of unstructured data, however, remains an open challenge. Although such data are easily accessible to humans, they are not suitable for automatic processing: machines are still unable to effectively and dynamically interpret the meaning associated with natural language text in very large, heterogeneous, noisy, and ambiguous environments such as the Web. We present a novel methodology that goes beyond mere word-level analysis of text and enables a more efficient transformation of unstructured social data into structured information, readily interpretable by machines. In particular, we describe a novel paradigm for real-time concept-level sentiment analysis that blends computational intelligence, linguistics, and common-sense computing in order to improve the accuracy of computationally expensive tasks such as polarity detection from big social data. The main novelty of the paper consists in an algorithm that assigns contextual polarity to concepts in text and flows this polarity through the dependency arcs in order to assign a final polarity label to each sentence. Analyzing how sentiment flows from concept to concept through dependency relations allows for a better understanding of the contextual role of each concept in text, to achieve a dynamic polarity inference that outperforms state-of-the-art statistical methods in terms of both accuracy and training time.

Statistical Learning Theory and ELM for Big Social Data Analysis

The science of opinion analysis based on data from social networks and other forms of mass media has garnered the interest of the scientific community and the business world. Dealing with the increasing amount of information present on the Web is a critical task and requires efficient models developed by the emerging field of sentiment analysis. To this end, current research proposes an efficient approach to support emotion recognition and polarity detection in natural language text. In this paper, we show how to exploit the most recent technological tools and advances in Statistical Learning Theory (SLT) in order to efficiently build an Extreme Learning Machine (ELM) and assess the resultant models performance when applied to big social data analysis. ELM represents a powerful learning tool, developed to overcome some issues in back-propagation networks. The main problem with ELM is in training them to work in the event of a large number of available samples, where the generalization performance has to be carefully assessed. For this reason, we propose an ELM implementation that exploits the Spark distributed in memory technology and show how to take advantage of the most recent advances in SLT in order to address the issue of selecting ELM hyperparameters that give the best generalization performance.

Sentic LDA: Improving on LDA with semantic similarity for aspect-based sentiment analysis

The advent of the Social Web has provided netizens with new tools for creating and sharing, in a time- and cost-efficient way, their contents, ideas, and opinions with virtually the millions of people connected to the World Wide Web. This huge amount of information, however, is mainly unstructured as specifically produced for human consumption and, hence, it is not directly machine-processable. In order to enable a more efficient passage from unstructured information to structured data, aspect-based opinion mining models the relations between opinion targets contained in a document and the polarity values associated with these. Because aspects are often implicit, however, spotting them and calculating their respective polarity is an extremely difficult task, which is closer to natural language understanding rather than natural language processing. To this end, Sentic LDA exploits common-sense reasoning to shift LDA clustering from a syntactic to a semantic level. Rather than looking at word co-occurrence frequencies, Sentic LDA leverages on the semantics associated with words and multi-word expressions to improve clustering and, hence, outperform state-of-the-art techniques for aspect extraction.

The CLSA Model: A Novel Framework for Concept-Level Sentiment Analysis

Hitherto, sentiment analysis has been mainly based on algorithms relying on the textual representation of online reviews and microblogging posts. Such algorithms are very good at retrieving texts, splitting them into parts, checking the spelling, and counting their words. But when it comes to interpreting sentences and extracting opinionated information, their capabilities are known to be very limited. Current approaches to sentiment analysis are mainly based on supervised techniques relying on manually labeled samples, such as movie or product reviews, where the overall positive or negative attitude was explicitly indicated. However, opinions do not occur only at document-level, nor they are limited to a single valence or target. Contrary or complementary attitudes toward the same topic or multiple topics can be present across the span of a review. In order to overcome this and many other issues related to sentiment analysis, we propose a novel framework, termed concept-level sentiment analysis (CLSA) model, which takes into account all the natural-language-processing tasks necessary for extracting opinionated information from text, namely: microtext analysis, semantic parsing, subjectivity detection, anaphora resolution, sarcasm detection, topic spotting, aspect extraction, and polarity detection.

Data intensive review mining for sentiment classification across heterogeneous domains

The automatic detection of orientation and emotions in texts is becoming increasingly important in the Web 2.0 scenario. There is a considerable need for innovative techniques and tools capable of identifying and detecting the attitude of unstructured text. The paper tackles two crucial aspects of the sentiment classification problem: first, the computational complexity of the deployed framework; second, the ability of the framework itself to operate effectively in heterogeneous commercial domains. The proposed approach adopts empirical learning to implement the sentiment-classification technology, and uses a distance-based predictive model to combine computational efficiency and modularity. A suitably designed semantic-based metric is the cognitive core that measures the distance between two user reviews, according to the sentiment they communicate. The framework ultimately nullifies the training process; at the same time, it takes advantage of a classification procedure whose computational cost increases linearly when the training corpus increases. To attain an objective measurement of the actual accuracy of the sentiment classification method, a campaign of tests involved a pair of complex, real-world scoring domains; the goal was to compare the predicted sentiment scores with actual scores provided by human assessors. Experimental results confirmed that the overall approach attained satisfactory performances in terms of both cross-domain classification accuracy and computational efficiency.

SIM-ELM

This paper moves from the affinities between two well-known learning schemes that apply randomization in the training process, namely, Extreme Learning Machines (ELMs) and the learning framework using similarity functions. These paradigms share a common approach involving data remapping and linear separators, but differ in the role of randomization within the respective learning algorithms. The paper presents an integrated approach connecting the two models, which ultimately yields a new variant of the basic ELM. The resulting learning scheme is characterized by an analytical relationship between the dimensionality of the remapped space and the learning abilities of the eventual predictor. Experimental results confirm that the new learning scheme can improve over conventional ELM in terms of the trade-off between classification accuracy and predictor complexity (i.e., the dimensionality of the remapped space).

A learning scheme based on similarity functions for affective common-sense reasoning

This paper explores the theory of learning with similarity functions in the context of common-sense reasoning and natural language processing. Based on this theory, the proposed approach (called Sim-Predictor) is characterized by the process of remapping the input space into a new space which is able to convey the similarity between the input pattern and a number of landmarks, i.e., a subset of patterns randomly extracted from the training set. The new learning scheme exhibits the interesting property of relating the dimensionality of the remapped space to the learning abilities of the eventual predictor in a formal fashion. The evaluation phase shows that Sim-Predictor compares positively with ELM and SVM, when addressing the problem of polarity detection in the sentic computing framework, a novel approach to big social data analysis based on the interpretation of the cognitive and affective information associated with natural language (affective common-sense reasoning).

Inductive bias for semi-supervised extreme learning machine

This research shows that inductive bias provides a valuable method to effectively tackle semi-supervised classification problems. In the learning theory framework, inductive bias provides a powerful tool, and allows one to shape the generalization properties of a learning machine. The paper formalizes semi-supervised learning as a supervised learning problem biased by an unsupervised reference solution. The resulting semi-supervised classification framework can apply any clustering algorithm to derive the reference function, thus ensuring maximum flexibility. In this context, the paper derives the biased version of Extreme Learning Machine (br-ELM). The experimental session involves several real world problems and proves the reliability of the semi-supervised classification scheme.

A machine learning approach for Twitter spammers detection

The ever-increasing popularity of Social Networks offers unprecedented opportunities to aggregate people and exchange information, but, at the same time, opens new modalities for cyber-crime perpetrations. The spamming phenomenon, so spread-out in emails, is now affecting microblogs, and exploits specific mechanisms of the messaging process. The paper proposes an inductive-learning method for the detection of Twitter-spammers, and applies a Random-Forest approach to a limited set of features that are extracted from traffic. Experimental results show that the proposed method outperforms existing approaches to this problem.