Sentiment analysis in Facebook and its application to e-learning

Ortigosa, Álvaro; Martín, José Manuel García; Carro, Rosa M.

doi:10.1016/j.chb.2013.05.024

Cited by 511 publications

(262 citation statements)

References 27 publications

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“…Asimismo, hay investigaciones (Altrabsheh, 2014;Ortigosa, 2014) para monitorear y analizar en tiempo real los comentarios que los estudiantes hacen en redes sociales con el fin de mejorar las clases de los docentes. Además, algunos autores (Bravo, 2013) documentan el uso de la red social Twitter como una herramienta complementaria en la educación colaborativa y el aprendizaje informal en el área de ingeniería.…”

Section: Trabajos Relacionadosunclassified

Un modelo basado en el Clasificador Naïve Bayes para la evaluación del desempeño docente

Esparza

Fuentes

Canul-Reich

et al. 2017

RIED

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ResumenLa evaluación del desempeño docente es un proceso de medición importante en las instituciones de educación superior en México y en el mundo, ya que retroalimenta el desempeño de los docentes con el fin de mejorar las clases y estrategias para beneficio de la educación de los estudiantes. En este trabajo se describe el desarrollo y evaluación de un Modelo Computacional denominado SocialMining, basado en el algoritmo Naïve Bayes, para apoyar el análisis de las opiniones de los estudiantes en el proceso de la evaluación del desempeño docente, llevada a cabo mediante dispositivos móviles. Esta propuesta considera el uso de dispositivos móviles para la recopilación de datos aprovechando su aceptación por parte de los estudiantes en el proceso de educación y aprendizaje. Asimismo, se describe el desarrollo de corpus de subjetividad, el cual consta de un conjunto de términos afectivos relevantes de la evaluación docente para apoyar al algoritmo Naïve Bayes en la clasificación de las opiniones de los estudiantes dentro de las clases: positivo, negativo y neutral. Para medir el desempeño del proceso de la clasificación del Modelo Computacional SocialMining, se utilizan métricas como la matriz de confusión, precisión y la curva de ROC. Se presenta además un caso de estudio, en el cual se recolectan nuevas opiniones de estudiantes de la Universidad Politécnica de Aguascalientes (México) con el fin de probar el desempeño del modelo propuesto en la clasificación. Los resultados obtenidos consideran factible el Modelo Computacional SocialMining para implementarse en instituciones de educación superior. Palabras

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Section: Trabajos Relacionadosunclassified

Un modelo basado en el Clasificador Naïve Bayes para la evaluación del desempeño docente

Esparza

Fuentes

Canul-Reich

et al. 2017

RIED

View full text Add to dashboard Cite

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“…Li et al [17] set up a system to analyze the market impact by combining the stock price and news sentiment. Ortigosa et al [4] performed sentiment classification and sentiment change detection on Facebook comments using a hybrid approach. They combined lexicon-based and machine-learning methods by considering a lexicon as the source of features and using a classification model to evaluate the lexicon; this approach is similar to the one used in our experiments in this study.…”

Section: Sentiment Classificationmentioning

confidence: 99%

“…A hybrid approach combines both the above approaches and has a relative 2 Wireless Communications and Mobile Computing advantage in sentiment analysis. Ortigosa et al [4] developed a lexicon from a corpus and then chose sentiment words along with the labeled class as the input features for a machine-learning classification method. Sentiment lexicons play a key role in a majority of the above methods.…”

Section: Introductionmentioning

confidence: 99%

Automatic Approach of Sentiment Lexicon Generation for Mobile Shopping Reviews

Feng

Gong

et al. 2018

Wireless Communications and Mobile Computing

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The dramatic increase in the use of smartphones has allowed people to comment on various products at any time. The analysis of the sentiment of users' product reviews largely depends on the quality of sentiment lexicons. Thus, the generation of highquality sentiment lexicons is a critical topic. In this paper, we propose an automatic approach for constructing a domain-specific sentiment lexicon by considering the relationship between sentiment words and product features in mobile shopping reviews. The approach first selects sentiment words and product features from original reviews and mines the relationship between them using an improved pointwise mutual information algorithm. Second, sentiment words that are related to mobile shopping are clustered into categories to form sentiment dimensions. At each sentiment dimension, each sentiment word can take the value of 0 or 1, where 1 indicates that the word belongs to a particular category whereas 0 indicates that it does not belong to that category. The generated lexicon is evaluated by constructing a sentiment classification task using several product reviews written in both Chinese and English. Two popular non-domain-specific sentiment lexicons as well as state-of-the-art machine-learning and deep-learning models are chosen as benchmarks, and the experimental results show that our sentiment lexicons outperform the benchmarks with statistically significant differences, thus proving the effectiveness of the proposed approach.

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“…Another paper [9] studied the sentiment analysis in e-learning, and in the sentiment detection part they combined two approaches: machine learning approach and lexicon-based approach. The lexicon-based approach consisted of using a dictionary of keywords marked with the sentiment they represent and finding using the keyword search the sentiment of each phrase splitting it into tokens.…”

Section: Literature Reviewmentioning

confidence: 99%

Extracting Patent-Related Information from Online Social Networks: Case of Facebook

Ivanov¹,

Tekić²

2017

DAAAM Proceedings

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In this study, we investigate how to identify and extract patent-related information from Facebook, the largest online social network. In the first step, we identified a list of trustworthy sources we started search from. Then, we developed algorithms for extracting and filtering information, and based on them software tool that is able to identify and deliver a patent, the Facebook post where it is mentioned and news / blog article which discuss it. We did a pilot test and collected more than 50 examples of articles (and Facebook posts) that add value to the patent they refer to. We classified collected articles and discussed how they can be used. Finally, we outlined where developed tool can be applied.

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Sentiment analysis in Facebook and its application to e-learning

Cited by 511 publications

References 27 publications

Un modelo basado en el Clasificador Naïve Bayes para la evaluación del desempeño docente

Un modelo basado en el Clasificador Naïve Bayes para la evaluación del desempeño docente

Automatic Approach of Sentiment Lexicon Generation for Mobile Shopping Reviews

Extracting Patent-Related Information from Online Social Networks: Case of Facebook

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