A Comparison of Classification Models to Detect Cyberbullying in the Peruvian Spanish Language on Twitter

Chavez, Cuzcano; Marianne, Ximena

doi:10.14569/ijacsa.2020.0111018

Cited by 8 publications

(4 citation statements)

References 26 publications

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“…For indicator I1, the results are like those of [12] that in their research about the perceptions of Twitter users on menthol cigarettes: analysis of content and sentiment, expressed that the average time to analyze sentiments in communications via Twitter is estimated at Ge (4 days), was significantly shorter at Gc (10 days). There are similar results in [3] which, in a survey and a comparative study of the analysis of the sentiment of the tweets through the semi-supervised learning, estimating that the Ge (2.12 days) was significantly smaller than the Gc (8 days). Similarly, [20] is found with the investigation based on the analysis of sentiment on customer satisfaction with digital payment in Indonesia.…”

Section: Discussion: Effect On Sentiment Analysis In Twitter Communic...supporting

confidence: 62%

Machine Learning for Feeling Analysis in Twitter Communications: A Case Study in HEYDRU!, Perú

Alegre-Veliz¹,

Gaspar-Ortiz²,

Gamboa-Cruzado

et al. 2022

Int. J. Interact. Mob. Technol.

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At present, sentiment analysis has become a trend; above all, in digital product development companies, as it is essential for rapid and automatic analysis. Sentiment analysis deals with emotions with the help of software, and it is playing an unavoidable role in workplaces. The constant growth of social networks, especially the Twitter social network, has made the ability to understand and comprehend users or clients take a greater scope regarding their needs; and therefore, increase the complexity of analysis of this social network, causing excessive expenses in time, personnel and money. This work presents a solution through the application of Machine Learning (ML) for sentiment analysis and thus improve analysis, execution time and customer satisfaction. The scope of this research is limited to using the Support Vector Machine (SVM) supervised learning technique for the intended analysis. The model derives from the ML technique making use of cross validation. The applied methodology is the CRISP-ML(Q) Methodology. The results show that the use of ML allows efficient sentiment analysis in Twitter communications.

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Section: Discussion: Effect On Sentiment Analysis In Twitter Communic...supporting

confidence: 62%

Machine Learning for Feeling Analysis in Twitter Communications: A Case Study in HEYDRU!, Perú

Alegre-Veliz¹,

Gaspar-Ortiz²,

Gamboa-Cruzado

et al. 2022

Int. J. Interact. Mob. Technol.

View full text Add to dashboard Cite

show abstract

“…Furthermore, yet there is scarce proposals of sentiment analysis in other languages than English, e.g., in Spanish. To the best of our knowledge, in the context of Peru, the study of [36] is a pioneering work aiming to detect cyberbullying in tweet messages written in Spanish, we assume that other works are ongoing or not published yet.…”

Section: Literature Reviewmentioning

confidence: 99%

Approval Rating of Peruvian Politicians and Policies using Sentiment Analysis on Twitter

Vidalón¹,

Solis²,

Porras³

et al. 2022

IJACSA

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Nowadays, using the social network Twitter, a subject can easily access, post, and share information about news, events, and incidents taking place currently in the world. Recently, due to the high number of users and the capability to transfer information instantly, Twitter had attracted the interest of politicians with the goal to interact with their followers and to communicate theirs polices. Fearing the disagreements and disturbances that the application of some policies might cause, usually politicians use surveys to support their actions. However, such studies still use traditional questionnaires to recover information and are costly and time-consuming. Recent advances in automatic natural language processing have allowed the extraction of information from textual data, like tweets. In this work, we present a method to analyze Twitter data related to Peruvian politicians and able to score the latent sentiment polarity of such messages. Our proposal is based on an embedding representation of tweets, which are classified by a convolutional neural network. For evaluation, we collected a new dataset related to the current President of Peru, where the model achieved 91.2% of sensibility and 94.4% of specificity. Furthermore, we evaluated the model in two politic topics, that were totally unknown for the model. In all of them, our approach gives comparable results to renowned Peruvian pollsters.

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“…The most common type of online bullying is mean comments which includes use of aggressive and pejorative words, threats, profile hacking etc. [11] [12][13] [14]. Nearly 8 out of 10 individuals are subject to the different types of cyberbullying in India.…”

Section: Introductionmentioning

confidence: 99%

Cyberbullying Detection in Textual Modality

Evangeline¹,

Vadakkan²,

R³

et al. 2021

IJACSA

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Cyberbullying is the use of technology to harass, threaten or target another individual. Online bullying can be particularly damaging and upsetting since it is usually anonymous and it's often hard to trace the bully. Sometimes cyberbullying can lead to issues like anxiety, depression, shame, suicide, etc. Most of the cyberbullying cases are not revealed to the public and the number of cases reported to the legal system is only few. Certain victims do not reveal their bully experiences out of shame or due to difficult procedures for reporting to the legal system. Our cyberbullying detection system aims to bring cases involving cyberbullying under control by detecting and warning the bully. Such cases are also reported to appropriate authorities, which can then be verified and necessary actions can be taken depending on the situation. The technology stack used for implementation include Flask, Scikit learn, Chat application APIs, Firebase, HTML, Javascript and CSS. The model was tested on classifiers like SVM, KNN, Logistic regression and Random Forest. F1 score was used as a metric to assess the four models. While analyzing the performances of these models, it was observed that Random Forest Classifier outperformed all the models. F1 score of 93.48% was achieved using the Random Forest Classifier.

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A Comparison of Classification Models to Detect Cyberbullying in the Peruvian Spanish Language on Twitter

Cited by 8 publications

References 26 publications

Machine Learning for Feeling Analysis in Twitter Communications: A Case Study in HEYDRU!, Perú

Machine Learning for Feeling Analysis in Twitter Communications: A Case Study in HEYDRU!, Perú

Approval Rating of Peruvian Politicians and Policies using Sentiment Analysis on Twitter

Cyberbullying Detection in Textual Modality

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