Automatic Categorization of Answers by Applying Supervised Classification Algorithms to the Analysis of Student Responses to a Series of Multiple Choice Questions

Neyra-Romero, Lisset A.; Cumbicus-Pineda, Oscar M.; Sierra, Basilio; Cueva-Carrion, Samanta

doi:10.1007/978-3-030-32022-5_42

Cited by 3 publications

(3 citation statements)

References 2 publications

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“…For this reason, it is applied the Synthetic Minority Oversampling Technique (SMOTE), which creates new examples from existing data, i.e., using an automatic sampling strategy and with a number of k (10) neighbours, it is possible to increase the data for the minority classes. This process consist of taking a tweet at random from the minority class and from the nearest k neighbours a randomly select neighbour is chosen, and a synthetic example is created at a random point between the two examples [19].…”

Section: Phase 3: Data Cleaning Pre-processing and Transformationmentioning

confidence: 99%

Detecting Xenophobic Hate Speech in Spanish Tweets Against Venezuelan Immigrants in Ecuador Using Natural Language Processing

Romero-Vega

Cumbicus-Pineda

López-Lapo

et al. 2021

Communications in Computer and Information Science

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In recent reports, Ecuador and Venezuela are located as the countries with the worst social indicators, showing ethnic and racial discrimination between both countries, one possible cause is a large number of Venezuelan immigrants in Ecuador.The present work has the goal of determining the existence of xenophobic content from a set of tweets collected around Venezuelan immigrants in Ecuador, using the diverse phases of the Knowledge Discovery in Text (KDT) methodology. Identifying xenophobia by mean of Natural Language Processing (NLP) is not an easy task; nonetheless, with the use of techniques as Synthetic Minority Oversampling (SMOTE) and Crowdsourcing it is possible to make it. The feelings classification: xenophobic, offensive and other are possible thanks to executing of three supervised classification algorithms: Logistic Regression, Support Vector Machines (SVM) and Naive Bayes.As a result of the execution of the three algorithms, SVM algorithm obtains a better performance with an F1-score of 98%. On the other hand, of the 100% of data analysed, it is determinate that there exist a 5.76% of xenophobic sentiments, 31.23% of offensive emotions and 63% contains other feelings.

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Section: Phase 3: Data Cleaning Pre-processing and Transformationmentioning

confidence: 99%

Detecting Xenophobic Hate Speech in Spanish Tweets Against Venezuelan Immigrants in Ecuador Using Natural Language Processing

Romero-Vega

Cumbicus-Pineda

López-Lapo

et al. 2021

Communications in Computer and Information Science

Self Cite

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“…By comparing these metrics, it is possible to identify the machine-learning algorithm or classification model [49]. The process of training and testing the algorithm usually relies on a single data set, so it is possible to generate bias in the performance metrics of the algorithm [50]; however, through cross-validation techniques, it is possible to train, validate, and test with multiple data sets or folds [51][52][53]. There are several techniques to perform cross-validation, these being K-fold cross-validation, stratified K-fold, or nested cross-validation [54,55].…”

Section: Introductionmentioning

confidence: 99%

K-Fold Cross-Validation through Identification of the Opinion Classification Algorithm for the Satisfaction of University Students

Chamorro-Atalaya,

Arévalo-Tuesta,

Balarezo-Mares

et al. 2023

Int. J. Onl. Eng.

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When using machine-learning techniques to determine algorithms or ranking models that identify student satisfaction, algorithms are often trained and tested on a single data set, leading to bias in their performance metrics. This article aims to identify the best algorithm to classify the satisfaction of university students applying the K-fold cross-validation technique, comparing the error rates of the performance metrics before and after its application. The method used began with the collection of student opinions on the teaching performance of the social network Twitter during an academic semester. Then, sentiment analysis was used for data processing, through which it was possible to categorize the opinions of the students into “satisfied” or “dissatisfied.” The results showed that the algorithm with the lowest error rate in its performance metric was the support vector machine (SVM). In addition, it was identified that its classification probability reached an accuracy of 91.76%. It is concluded that SVM classification using K-fold cross-validation will contribute to determining which factors associated with the teacher’s didactic strategies should be improved in each class session, since traditional surveying techniques have shortcomings.

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“…Currently, artificial intelligence techniques make it possible to organise databases to obtain information that is not visualised at first glance [17], this is the case with tweets collected through the Twitter API, this dataset has a large amount of information that can be undermined to determine useful information that is implicit. This research proposes the application of data mining algorithms in the information collected from 2016 to 2019, to determine the feeling that people give in the International Festival of Living Arts of Loja -Ecuador having three positive, negative or neutral alternatives.…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis Related of International Festival of Living Arts Loja-Ecuador Employing Knowledge Discovery in Text

Rivera-Guamán

Cumbicus-Pineda

López-Lapo

et al. 2021

Communications in Computer and Information Science

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Nowadays, the use of social networks is part of the daily life of most people, especially young people, to share content and opinions. Twitter is one of the most popular social networks, in which users are the first actors to generate a large amount of information. The analysis of Twitter data requires a systematic process of collection, processing and classification. The main objective of this work is to classify the data tweets in three different classes: Positive, Negative and Neutral Opinions, corresponding to the "International Festival of the Living Arts in Loja (FIAVL)" in the years between 2016 and 2019. The official account of the "FIAVL" produced a total of 18k tweets in Spanish language, which followed the different phases of the Knowledge Discovery in Text (KDT) methodology for its analysis and study. Vector Support Machines (SVM) and Naive Bayes (NB) were used to classify the classes, where an accuracy rate of 98.7% was obtained, with the neutral opinion prevailing over the rest of the classes with 57%, thus it could be concluded that there are no positive or negative opinions about the FIAVL.

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Automatic Categorization of Answers by Applying Supervised Classification Algorithms to the Analysis of Student Responses to a Series of Multiple Choice Questions

Cited by 3 publications

References 2 publications

Detecting Xenophobic Hate Speech in Spanish Tweets Against Venezuelan Immigrants in Ecuador Using Natural Language Processing

Detecting Xenophobic Hate Speech in Spanish Tweets Against Venezuelan Immigrants in Ecuador Using Natural Language Processing

K-Fold Cross-Validation through Identification of the Opinion Classification Algorithm for the Satisfaction of University Students

Sentiment Analysis Related of International Festival of Living Arts Loja-Ecuador Employing Knowledge Discovery in Text

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