Intelligent Topical Sentiment Analysis for the Classification of E-Learners and Their Topics of Interest

Every day, huge numbers of instant tweets (messages) are published on Twitter as it is one of the massive social media for e-learners interactions. The options regarding various interesting topics to be studied are discussed among the learners and teachers through the capture of ideal sources in Twitter. The common sentiment behavior towards these topics is received through the massive number of instant messages about them. In this paper, rather than using the opinion polarity of each message relevant to the topic, authors focus on sentence level opinion classification upon using the unsupervised algorithm named bigram item response theory (BIRT). It differs from the traditional classification and document level classification algorithm. The investigation illustrated in this paper is of threefold which are listed as follows:(1)lexicon based sentiment polarity of tweet messages;(2)the bigram cooccurrence relationship using naïve Bayesian;(3)the bigram item response theory (BIRT) on various topics. It has been proposed that a model using item response theory is constructed for topical classification inference. The performance has been improved remarkably using this bigram item response theory when compared with other supervised algorithms. The experiment has been conducted on a real life dataset containing different set of tweets and topics.

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A Computational Strategy for Classification of Enem Issues Based on Item Response Theory

Revista de Informática Teórica e Aplicada ◽

10.22456/2175-2745.92406 ◽

2020 ◽

Vol 27 (1) ◽

pp. 92-111

Author(s):

Gustavo Henrique Nunes ◽

Bruno Alberto Soares Oliveira ◽

Ciniro Aparecido Leite Nametala

Keyword(s):

High School ◽

Item Response Theory ◽

Item Response ◽

Generation System ◽

Response Theory ◽

Hit Rate ◽

System A ◽

Intelligent Simulation ◽

Computational Strategy

The National High School Examination (ENEM) gains each year more importance, as it gradually, replacing traditional vestibular. Many simulations are done almost randomly by teachers or systems, with questions chosen without discretion. With this methodology, if a test needs to be reapplied, it is not possible to recreate it with questions that have the same difficulty as those used in the first evaluation. In this context, the present work presents the development of an ENEM Intelligent Simulation Generation System that calculates the parameters of Item Response Theory (TRI) of questions that have already been applied in ENEM and, based on them, classifies them. in groups of difficulty, thus enabling the generation of balanced tests. For this, the K-means algorithm was used to group the questions into three difficulty groups: easy, medium and difficult. To verify the functioning of the system, a simulation with 180 questions was generated along the ENEM model. It could be seen that in 37.7% of cases this happened. This hit rate was not greater because the algorithm confounded the difficulty of issues that are in close classes. However, the system has a hit rate of 92.8% in the classification of questions that are in distant groups.

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