Research Outputs
Detecting aggressiveness in tweets: A hybrid model for detecting cyberbullying in the Spanish language
In recent years, the use of social networks has increased exponentially, which has led to a significant increase in cyberbullying. Currently, in the field of Computer Science, research has been made on how to detect aggressiveness in texts, which is a prelude to detecting cyberbullying. In this field, the main work has been done for English language texts, mainly using Machine Learning (ML) approaches, Lexicon approaches to a lesser extent, and very few works using hybrid approaches. In these, Lexicons and Machine Learning algorithms are used, such as counting the number of bad words in a sentence using a Lexicon of bad words, which serves as an input feature for classification algorithms. This research aims at contributing towards detecting aggressiveness in Spanish language texts by creating different models that combine the Lexicons and ML approach. Twenty-two models that combine techniques and algorithms from both approaches are proposed, and for their application, certain hyperparameters are adjusted in the training datasets of the corpora, to obtain the best results in the test datasets. Three Spanish language corpora are used in the evaluation: Chilean, Mexican, and Chilean-Mexican corpora. The results indicate that hybrid models obtain the best results in the 3 corpora, over implemented models that do not use Lexicons. This shows that by mixing approaches, aggressiveness detection improves. Finally, a web application is developed that gives applicability to each model by classifying tweets, allowing evaluating the performance of models with external corpus and receiving feedback on the prediction of each one for future research. In addition, an API is available that can be integrated into technological tools for parental control, online plugins for writing analysis in social networks, and educational tools, among others.
The role of WordNet similarity in the affective analysis pipeline
Sentiment Analysis (SA) is a useful and important discipline in Computer Science, as it allows having a knowledge base about the opinions of people regarding a topic. This knowledge is used to improve decision-making processes. One approach to achieve this is based on the use of lexical knowledge structures. In particular, our aim is to enrich an affective lexicon by the analysis of the similarity relationship between words. The hypothesis of this work states that the similarities of the words belonging to an affective category, with respect to any other word, behave in a homogeneous way within each affective category. The experimental results show that words of a same affective category have a homogeneous similarity with an antonym, and that the similarities of these words with any of their antonyms have a low variability. The novelty of this paper is that it builds the bases of a mechanism that allows incorporating the intensity in an affective lexicon automatically.
Improving the affective analysis in texts. Automatic method to detect affective intensity in lexicons based on Plutchik’s wheel of emotions
Purpose: This paper aims to propose a method for automatically labelling an affective lexicon with intensity values by using the WordNet Similarity (WS) software package with the purpose of improving the results of an affective analysis process, which is relevant to interpreting the textual information that is available in social networks. The hypothesis states that it is possible to improve affective analysis by using a lexicon that is enriched with the intensity values obtained from similarity metrics. Encouraging results were obtained when an affective analysis based on a labelled lexicon was compared with that based on another lexicon without intensity values. Design/methodology/approach: The authors propose a method for the automatic extraction of the affective intensity values of words using the similarity metrics implemented in WS. First, the intensity values were calculated for words having an affective root in WordNet. Then, to evaluate the effectiveness of the proposal, the results of the affective analysis based on a labelled lexicon were compared to the results of an analysis with and without affective intensity values. Findings: The main contribution of this research is a method for the automatic extraction of the intensity values of affective words used to enrich a lexicon compared with the manual labelling process. The results obtained from the affective analysis with the new lexicon are encouraging, as they provide a better performance than those achieved using a lexicon without affective intensity values. Research limitations/implications: Given the restrictions for calculating the similarity between two words, the lexicon labelled with intensity values is a subset of the original lexicon, which means that a large proportion of the words in the corpus are not labelled in the new lexicon. Practical implications: The practical implications of this work include providing tools to improve the analysis of the feelings of the users of social networks. In particular, it is of interest to provide an affective lexicon that improves attempts to solve the problems of a digital society, such as the detection of cyberbullying. In this case, by achieving greater precision in the detection of emotions, it is possible to detect the roles of participants in a situation of cyberbullying, for example, the bully and victim. Other problems in which the application of affective lexicons is of importance are the detection of aggressiveness against women or gender violence or the detection of depressive states in young people and children. Social implications: This work is interested in providing an affective lexicon that improves attempts to solve the problems of a digital society, such as the detection of cyberbullying. In this case, by achieving greater precision in the detection of emotions, it is possible to detect the roles of participants in a situation of cyber bullying, for example, the bully and victim. Other problems in which the application of affective lexicons is of importance are the detection of aggressiveness against women or gender violence or the detection of depressive states in young people and children. Originality/value: The originality of the research lies in the proposed method for automatically labelling the words of an affective lexicon with intensity values by using WS. To date, a lexicon labelled with intensity values has been constructed using the opinions of experts, but that method is more expensive and requires more time than other existing methods. On the other hand, the new method developed herein is applicable to larger lexicons, requires less time and facilitates automatic updating.
Fuzzy linguistic descriptions for execution trace comprehension and their application in an introductory course in artificial intelligence
Execution traces comprehension is an important topic in computer science since it allows software engineers to get a better understanding of the system behavior. However, traces are usually very large and hence they are difficult to interpret. Parallel, execution traces comprehension is a very important topic into the algorithms learning courses since it allows students to get a better understanding of the algorithm behavior. Therefore, there is a need to investigate ways to help students (and teachers) find and understand important information conveyed in a trace despite the trace being massive. In this paper, we propose a new approximation for execution traces comprehension based on fuzzy linguistic descriptions. A new methodology and a data-driven architecture based on linguistic modelling of complex phenomenon are presented and explained. In particular, they are applied to automatically generate linguistic reports from execution traces generated during the execution of algorithm implemented by the students of an introductory course of artificial intelligence. To the best of our knowledge, it is the first time that linguistic modelling of complex phenomenon is applied to execution traces comprehension. Throughout the article, it is shown how this kind of technology can be employed as a useful computer-assisted assessment tool that provides students and teachers with technical, immediate and personalised feedback about the algorithms that are being studied and implemented. At the same time, they provide us with two useful applications: they are an indispensable pedagogical resource for improving comprehension of execution traces, and they play an important role in the process of measuring and evaluating the “believability” of the agents implemented. To show and explore the possibilities of this new technology, a web platform has been designed and implemented by one of the authors, and it has been incorporated into the process of assessment of an introductory artificial intelligence course. Finally, an empirical evaluation to confirm our hypothesis was performed and a survey directed to the students was carried out to measure the quality of the learning-teaching process by using this methodology enriched with fuzzy linguistic descriptions.
What do our Children read about? Affect analysis of Chilean school texts
We present a study of the affective character of 1st to 8th year Chilean school texts, to which we applied lexicon-based affect analysis techniques to identify 6 basic emotions (anger, sadness, fear, disgust, surprise and happiness). First, we generated a corpus of 525 documents, 18176 paragraphs and 137516 words. Then, using the affective words frequency, we built a classifier based on Emotion Word Density to detect emotions in the texts. Our results show that the predominant affective states are happiness (58%), sadness (16%) and fear (12%). The 6 basic emotions are present in most literary forms with uniform relative density except for songs, where anger is absent. Classifier performance was validated by comparing its results against the opinions of experts in the field, and its results show an above-average conformity (accuracy = 63%), above-average predictive capacity (precision = 69%) and good classifier sensitivity (recall = 80% and f-measure = 93%).