Isidoros Perikos

Recognizing emotions in text using ensemble of classifiers

Emotions constitute a key factor in human nature and behavior. The most common way for people to express their opinions, thoughts and communicate with each other is via written text. In this paper, we present a sentiment analysis system for automatic recognition of emotions in text, using an ensemble of classifiers. The designed ensemble classifier schema is based on the notion of combining knowledge-based and statistical machine learning classification methods aiming to benefit from their merits and minimize their drawbacks. The ensemble schema is based on three classifiers; two are statistical (a Naive Bayes and a Maximum Entropy learner) and the third one is a knowledge-based tool performing deep analysis of the natural language sentences. The knowledge-based tool analyzes the sentences text structure and dependencies and implements a keyword-based approach, where the emotional state of a sentence is derived from the emotional affinity of the sentences emotional parts. The ensemble classifier schema has been extensively evaluated on various forms of text such as, news headlines, articles and social media posts. The experimental results indicate quite satisfactory performance regarding the ability to recognize emotion presence in text and also to identify the polarity of the emotions.

Recognizing Emotion Presence in Natural Language Sentences

Emotions constitute a key factor in human communication. Human emotion can be expressed through various mediums such as speech, facial expressions, gestures and textual data. A quite common way for people to communicate with each other and with computer systems is via written text. In this paper we present an emotion detection system used to automatically recognize emotions in text. The system takes as input natural language sentences, analyzes them and determines the underlying emotion being conveyed. It implements a keyword-based approach where the emotional state of a sentence is constituted by the emotional affinity of the sentence’s emotional words. The system uses lexical resources to spot words known to have emotional content and analyses sentence structure to specify their strength. Experimental results indicate quite satisfactory performance.

Modeling ReTweet Diffusion Using Emotional Content

In this paper we present a prediction model for forecasting the depth and the width of ReTweeting using data mining techniques. The proposed model utilizes the analyzers of tweet emotional content based on Ekman emotional model, as well as the behavior of users in Twitter. In following, our model predicts the category of ReTweeting diffusion. The model was trained and validated with real data crawled by Twitter. The aim of this model is the estimation of spreading of a new post which could be retweeted by the users in a particular network. The classification model is intended as a tool for sponsors and people of marketing to specify the tweets that spread more in Twitter network.

A Web-Based Interactive System for Learning NL to FOL Conversion

In this paper, we present NLtoFOL SIP system, a web-based interactive system aimed at helping students to learn how to convert/translate natural language (NL) sentences into first-order logic (FOL) formulas. It tries to achieve it by providing (a) a structured and interactive process (SIP) for the conversion and (b) guidance and help during that process. The system provides guidance and help of various levels in an intelligent way based on the user’s responses. Also, the user interface is dynamically configured during the user interaction to reflect the steps of SIP. Different NL sentences may require the implementation of different number of SIP steps. According to our knowledge, there is no other system that tackles the problem of NL to FOL conversion in such a systematic way. Small scale evaluation has given quite satisfactory results.

An Educational System for Learning Search Algorithms and Automatically Assessing Student Performance

In this paper, first we present an educational system that assists students in learning and tutors in teaching search algorithms, an artificial intelligence topic. Learning is achieved through a wide range of learning activities. Algorithm visualizations demonstrate the operational functionality of algorithms according to the principles of active learning. So, a visualization process can stop and request from a student to specify the next step or explain the way that a decision was made by the algorithm. Similarly, interactive exercises assist students in learning to apply algorithms in a step-by-step interactive way. Students can apply an algorithm to an example case, specifying the algorithm’s steps interactively, with the system’s guidance and help, when necessary. Next, we present assessment approaches integrated in the system that aim to assist tutors in assessing the performance of students, reduce their marking task workload and provide immediate and meaningful feedback to students. Automatic assessment is achieved in four stages, which constitute a general assessment framework. First, the system calculates the similarity between the student’s and the correct answer using the edit distance metric. In the next stage, it identifies the type of the answer, based on an introduced answer categorization scheme related to completeness and accuracy of an answer, taking into account student carelessness too. Afterwards, the types of errors are identified, based on an introduced error categorization scheme. Finally, answer is automatically marked via an automated marker, based on its type, the edit distance and the type of errors made. To assess the learning effectiveness of the system an extended evaluation study was conducted in real class conditions. The experiment showed very encouraging results. Furthermore, to evaluate the performance of the assessment system, we compared the assessment mechanism against expert (human) tutors. A total of 400 students’ answers were assessed by three tutors and the results showed a very good agreement between the automatic assessment system and the tutors.

A highly motivated blended learning model oriented to computer engineering educators

This work presents a versatile blended learning model oriented to computer engineering educators in order to advance their technical skills. For the realization of this model a Hybrid Educational Platform (HEP) was developed exploiting the robustness and functionality of the Greek School Network (GSN). Findings from the evaluation of the related training activities indicate a well-promising learning framework that compensates the growing needs on information and communication technologies, thus establishing a modern and attractive education environment in the Greek Society.

Assistance and Feedback Mechanism in an Intelligent Tutoring System for Teaching Conversion of Natural Language into Logic

Logic as a knowledge representation and reasoning language is a fundamental topic of an Artificial Intelligence (AI) course and includes a number of sub-topics. One of them, which brings difficulties to students to deal with, is converting natural language (NL) sentences into first-order logic (FOL) formulas. To assist students to overcome those difficulties, we developed the NLtoFOL system and equipped it with a strong assistance and feedback mechanism. In this work, first, we present that feedback mechanism. The mechanism can provide assistance before an answer is submitted, if requested, but mainly it provides assistance after an answer is submitted. To that end, it characterizes the answer in terms of completeness and accuracy to determine the level of incorrectness, based on an answer categorization scheme, introduced in this paper. The automatically generated natural language feedback sequences grow from general to specific and can include statements on a student’s metacognitive state. Feedback is provided as natural language sentences automatically generated through a template-based natural language generation mechanism. Second, we present an extensive evaluation of the effectiveness of the assistance and feedback mechanism on students’ learning. The evaluation of feedback with students showed that full feedback sequences lead to greater learning gains than sequences consisting of only flag feedback and bottom-out hints (n = 226), and that generic, template-based feedback sequences are comparable to the utility of problem-specific hints generated by human tutors (n = 120).

Integrating User’s Emotional Behavior for Community Detection in Social Networks

The analysis of social networks is a very challenging research area. A fundamental aspect concerns the detection of user communities, i.e. the organization of vertices in clusters, with many edges joining vertices of the same cluster and comparatively few edges joining vertices of different clusters. Detecting communities is of great importance in sociology, biology as well as computer science where systems are often represented as graphs. In this paper we present a novel methodology for community detection based on users’ emotional behavior. The methodology analyzes user’s tweets in order to determine their emotional behavior in Ekman emotional scale. We define two different metrics to count the influence of produced communities. Moreover, the weighted version of a modularity community detection algorithm is utilized. Our results show that our proposed methodology creates influential enough communities.

An Innovative Educational Environment Based on Virtual Reality and Gamification for Learning Search Algorithms

Search algorithms constitute an important domain of computer science and is considered necessary for students and freshmen to get a deep and complete understanding of their operation. In this work, we present an innovative 3D virtual reality educational environment that aims to assist tutors in teaching and students in better learning the search algorithms. The educational environment utilizes innovative educational infrastructure and pedagogical approaches based on visualization of procedures and learning activities that rely on gamification to promote deeper understanding of the challenging concepts of blind and heuristic search algorithms. Algorithm visualization approaches in the virtual environment aim to help students connect abstract concepts and procedures to concrete experiences and examples which promotes robust learning. Learning activities based on the principles of gamification was designed to actively engage students and make learning more entertaining and efficient. The educational environment has been evaluated in real classroom conditions and the evaluation results indicate that the utilization of suitable learning activities in terms of students’ active engagement and can motivate students and improve learning efficiency.

Recognizing Emotions from Facial Expressions Using Neural Network

Recognizing the emotional state of a human from his/her facial gestures is a very challenging task with wide ranging applications in everyday life. In this paper, we present an emotion detection system developed to automatically recognize basic emotional states from human facial expressions. The system initially analyzes the facial image, locates and measures distinctive human facial deformations such as eyes, eyebrows and mouth and extracts the proper features. Then, a multilayer neural network is used for the classification of the facial expression to the proper emotional states. The system was evaluated on images of human faces from the JAFFE database and the results gathered indicate quite satisfactory performance.