Automatic Categorization of Tweets on the Political Electoral Theme Using Supervised Classification Algorithms

Cumbicus-Pineda, Oscar M.; Ordoñez-Ordoñez, Pablo F.; Neyra-Romero, Lisset A.; Figueroa-Diaz, Roberth

doi:10.1007/978-3-030-05532-5_51

Cited by 4 publications

(2 citation statements)

References 3 publications

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“…For knowing the real performance of a model before applying it, there is a very common solution, which is to use the training set to train and validate at the same time, known as cross-validation; it consists of dividing the training set in k equal parts, if k = 5 then the model is trained with the first four parts and tested with the fifth, then it is trained with the first three and the fifth and tested with the fourth, this is repeated k times, always leaving out one part for the test, then the performance in each one is averaged and thus the expected performance is obtained [5]. For the validations, it is considered to use k = 10 (number of folds), which is the number of parts into which the dataset is divided to train and evaluate the models.…”

Section: Phase 4: Model Development and Hypothesis Buildingmentioning

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 4: Model Development and Hypothesis Buildingmentioning

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 the development of this article, the objectives that allow the classification of the tweets, which are extracted from a reliable repository such as Twitter, are established. Data mining techniques are also implemented in the data sets obtained in order to extract the relevant information for their subsequent classification [11].…”

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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Data Mining to Determine the Causes of Gender-Based Violence against Women in Ecuador

Cumbicus-Pineda

Abad-Eras

Neyra-Romero

2021

2021 IEEE Fifth Ecuador Technical Chapters Meeting (ETCM)

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Automatic Categorization of Tweets on the Political Electoral Theme Using Supervised Classification Algorithms

Cited by 4 publications

References 3 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

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

Data Mining to Determine the Causes of Gender-Based Violence against Women in Ecuador

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