Sentiment analysis of Facebook statuses using Naive Bayes classifier for language learning

Troussas, Christos; Virvou, Maria; Espinosa, Kurt Junshean; Llaguno, Kevin; Jaime, D. L.

doi:10.1109/iisa.2013.6623713

Cited by 121 publications

(71 citation statements)

References 3 publications

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“…A common preprocessing technique is identifying emoticons, which is found in Troussas et al [6]. Another preprocessing technique used widely with educational domains is the spelling check; this is found in Ortigosa et al [4] and Martin et al [7].…”

Section: A Preprocessingmentioning

confidence: 99%

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Sentiment Analysis: Towards a Tool for Analysing Real-Time Students Feedback

Altrabsheh

Cocea

Fallahkhair

2014

2014 IEEE 26th International Conference on Tools With Artificial Intelligence

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Abstract-Students' real-time feedback has numerous advantages in education, however, analysing feedback while teaching is both stressful and time consuming. To address this problem, we propose to analyse feedback automatically using sentiment analysis. Sentiment analysis is domain dependent and although it has been applied to the educational domain before, it has not been previously used for real-time feedback. To find the best model for automatic analysis we look at four aspects: preprocessing, features, machine learning techniques and the use of the neutral class. We found that the highest result for the four aspects is Support Vector Machines (SVM) with the highest level of preprocessing, unigrams and no neutral class, which gave a 95 percent accuracy.

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Section: A Preprocessingmentioning

confidence: 99%

“…In the educational domain, some of the researchers used n-grams, such as Troussas et al [6]. However, some of the researchers have used pos-tagging as a feature, such as in Ortigosa et al [4] and Martin et al [7].…”

Section: B Featuresmentioning

confidence: 99%

Sentiment Analysis: Towards a Tool for Analysing Real-Time Students Feedback

Altrabsheh

Cocea

Fallahkhair

2014

2014 IEEE 26th International Conference on Tools With Artificial Intelligence

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“…In the educational domain, Tan et al [23] and Troussas et al [25] found Naive Bayes to be the best technique, while Song et al [22] used Support Vector Machines. This research indicates that different machine learning techniques give different results even for the same domain, prompting a need for testing several techniques.…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

Learning Sentiment from Students’ Feedback for Real-Time Interventions in Classrooms

Altrabsheh

Cocea

Fallahkhair

2014

Lecture Notes in Computer Science

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Abstract. Knowledge about users sentiments can be used for a variety of adaptation purposes. In the case of teaching, knowledge about students sentiments can be used to address problems like confusion and boredom which affect students engagement. For this purpose, we looked at several methods that could be used for learning sentiment from students feedback. Thus, Naive Bayes, Complement Naive Bayes (CNB), Maximum Entropy and Support Vector Machine (SVM) were trained using real students' feedback. Two classifiers stand out as better at learning sentiment, with SVM resulting in the highest accuracy at 94%, followed by CNB at 84%. We also experimented with the use of the neutral class and the results indicated that, generally, classifiers perform better when the neutral class is excluded.

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“…They found that the Naïve Bayes classifier had the highest precision (77%) while the Rocchio classifier had the highest recall (73%) and both classifiers outperformed the Perceptron classifier [24].…”

Section: Xu Et Al (2012) Have Detected Traces Of Bullying On Thementioning

confidence: 99%

A Novel Position-based Sentiment Classification Algorithm for Facebook Comments

Surroop¹,

Canoo²,

Pudaruth³

2016

ijacsa

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Abstract-With the popularization of social networks, people are now more at ease to share their thoughts, ideas, opinions and views about all kinds of topics on public platforms. Millions of users are connected each day on social networks and they often contribute to online crimes by their comments or posts through cyber bullying, identity theft, online blackmailing, etc. Mauritius has also registered a surge in the number of cybercrime cases during the past decade. In this study, a trilingual dataset of 1031 comments was extracted from public pages on Facebook. This dataset was manually categorized into four different sentiment classes: positive, negative, very negative and neutral, using a novel sentiment classification algorithm. Out of these 1031 comments, it was found that 97.8% of the very negative sentiments, 70.7% of the negative sentiments and 77.0% of the positive sentiments were correctly extracted. Despite the added complexity of our dataset, the accuracy of our system is slightly better than similar works in the field. The accuracy of the lexicon-based approach was also much higher than when we used machine learning techniques. The outcome of this research work can be used by the Mauritius Police Force to track down potential cases of cybercrime on social networks. Decisive actions can then be implemented in time.

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Sentiment analysis of Facebook statuses using Naive Bayes classifier for language learning

Cited by 121 publications

References 3 publications

Sentiment Analysis: Towards a Tool for Analysing Real-Time Students Feedback

Sentiment Analysis: Towards a Tool for Analysing Real-Time Students Feedback

Learning Sentiment from Students’ Feedback for Real-Time Interventions in Classrooms

A Novel Position-based Sentiment Classification Algorithm for Facebook Comments

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