Text Mining Approach Using TFIDF and Naive Bayes for Classification of Exam Questions Based on Cognitive Level of Blooms Taxonomy Bloomu0027s Taxonomy is a unity of three domains, which are divided into lower orders and high orders based on the Bloom Taxonomic Cognitive Domain, the level is used to classify learning objectives and serve as benchmarks for evaluating student achievement. Basically, an evaluation of student achievement can be done by giving questions on exam activities. The questions given are then classified according to the level in the Cognitive Domain. However, because the number of questions is too many and the classification is still manual, it causes the classification results are not accurate and inconsistent. Therefore, the employing of the Naive Bayes Classifier in classifying exam questions based on levels in the Cognitive Domain can be a solution. This study uses realworld dataset collected from midterms and final exams questions taken from Department of Information Systems, Telkom University from the academic year 20122013 to 20182019. In particular, we examined Words, Characters, and Ngram as indexing terms. The results showed that the classification using Naive Bayes and TFIDF with Ngram as indexing terms achieved precision of 85% and recall of 80%.

ExtractionBased Text Summarization and Sentiment Analysis of Online Reviews Using Hybrid Classification Method The field of sentiment mining and text summarization has evoked the interest of many scientists and researchers over the last few years, as the textual data has become useful for many realworld applications and challenges. Sentiment Analysis and Opinion Mining is the most popular field for analyzing and discovering insights from text data from various sources, such as Facebook, Twitter and Amazon, Zomato, etc. It involves a computational study of an individualxe2x80x99s behavior in terms of buying interest and then extracting his opinions on the business entity of the company. This entity can be viewed as an event, individual, blog post or product experience. Scholars in the fields of natural language processing, data mining, machine learning and others have tested a variety of methods for automating sentiment analysis. These reviews are increasing on a daily basis, as a result of which the summarization of the reviews plays a role where the text is summarized as needed, which provides useful information from a large number of reviews. It is very difficult for a human being to extract and interpret useful data from a very large file. In the text analysis, the value of sentences is decided on the basis of the linguistic characteristics of sentences. This paper provides a comprehensive review of current and past work on sentiment analysis and text description. In this research work, a new hybrid classification system is proposed based on coupling classification methods using arcing classifiers and their quality is evaluated within terms of accuracy. The Classifier Collection was constructed using Naive Bayes (NB), Support Vector Machine (SVM) and Genetic Algorithm (GA). The proposed work consists of a comparative study of the efficacy of the ensemble technique for sentiment classification. The feasibility and benefits of the proposed approaches are demonstrated by a restaurant review that is widely used in the field of sentiment classification. A wide range of comparative studies is performed and, ultimately, some indepth analysis is addressed and conclusions are drawn on the efficacy of the ensemble technique for sentiment classification