Single Document Automatic Text Summarization Using Term Frequency-Inverse Document Frequency (TF-IDF)

The increasing availability of online information has triggered an intensive research in the area of automatic text summarization within the Natural Language Processing (NLP). Text summarization reduces the text by removing the less useful information which helps the reader to find the required information quickly. There are many kinds of algorithms that can be used to summarize the text. One of them is TF-IDF (TermFrequency-Inverse Document Frequency). This research aimed to produce an automatic text summarizer implemented with TF-IDF algorithm and to compare it with other various online source of automatic text summarizer. To evaluate the summary produced from each summarizer, The F-Measure as the standard comparison value had been used. The result of this research produces 67% of accuracy with three data samples which are higher compared to the other online summarizers

Feature selection to enhance android malware detection using modified term frequency-inverse document frequency (MTF-IDF)

This research synthesizes an evaluation of feature selection algorithm by utilizing Term Frequency-Inverse Document Frequency (TF-IDF) as the main algorithm in Android malware detection. The TF-IDF algorithm is used to filter Android features filtered before detection process. However, IDF is unaware to the training class labels and gives incorrect weight value to some features. Therefore, the proposed approach that is Modified Term Frequency – Inverse Document Frequency (MTF-IDF) algorithm give more focus on both sample and features to give correct weight value to some features. The proposed algorithm considered features based on its level of importance where weight given based on number of features involved in the sample. The related best features in the sample are selected using weight and priority ranking process using K-means. This ensures that only important malware features are selected in the Android application sample. These experiments are conducted on a sample collected from DREBIN. Comparison between existing TF-IDF algorithm and MTF-IDF algorithm have been made under various conditions such as tested on different number of sample size, different number of features used and integration of different types of features. The results showed that feature selection using MTF-IDF can improve Android malware detection analysis. It was proven that MTF-IDF is an effective Android malware detection algorithm regardless of different kinds of features or sample sizes used. MTF-IDF algorithm also proved that it can give appropriate scaling for all features in analyzing Android malware detection

The accessibility dimension for structured document retrieval

Structured document retrieval aims at retrieving the document components that best satisfy a query, instead of merely retrieving pre-defined document units. This paper reports on an investigation of a tf-idf-acc approach, where tf and idf are the classical term frequency and inverse document frequency, and acc, a new parameter called accessibility, that captures the structure of documents. The tf-idf-acc approach is defined using a probabilistic relational algebra. To investigate the retrieval quality and estimate the acc values, we developed a method that automatically constructs diverse test collections of structured documents from a standard test collection, with which experiments were carried out. The analysis of the experiments provides estimates of the acc values

Document clustering based on inverse document frequency measure

Automatic classification techniques are capable of providing the necessary information organization by arranging the retrieved data into groups of documents with common subjects. Recently, document clustering has been put forth as an alternative method of organizing the results of retrieval. It been proposed for use in navigating and browsing document collections, and discovers hidden similarity and key concepts. It also summarize a large amount of document using key or common attributes of cluster and can be used to categorize document databases. This paper describes several narrative clustering techniques such as Porter algorithm, Gusfield algorithm, similarity based on document hierarchy and Inverse Document Frequency (IDF), which intersect the documents in a cluster to determine the set of words (or phrases) shared by all the documents in the cluster. This study proposes document clustering based on IDF, where it is assumes that importance of a keyword in calculating similarity measures is inversely proportional to the total number of documents that contain it. IDF is easy to understand, has a geometric interpretation, term weighing shown to help clustering, allow partial matching and returns ranked documents. An important finding in this study, where 30 cases of documents tested with the IDF algorithm, and the results are divided into three category; correct cluster, incorrect cluster, and unknown cluster

Mobile Forensics for Cyberbullying Detection using Term Frequency - Inverse Document Frequency (TF-IDF)

The case of cyberbullying in Indonesia was ranked third in the world in 2015 and as much as 91% was experienced by children [1]. RSA Anti Fraud Command Center (AFCC) report reports that in 2015 45% of transactions were carried out through mobile channels, while 61% of fraud occurred through mobile devices [2]. WhatsApp in July 2019, 1.6 billion users access the WhatsApp messenger on a monthly basis [10]. The data opens a reference for investigators to better anticipate cybercrime actions that can occur in the whatsapp application because more users are using the application. In this study using the TF-IDF method in detecting cyberbullying that occurs in order to be able to add a reference for investigators. The conclusions that have been obtained from the simulation of conversations between four people in a whatsapp group get the results of the cyberbullying rate that the user "a" has a cyberbullying rate of 66.80%, the user "b" has a cyberbullying rate of 50%, the user "c" has a level cyberbullying is 33.19%, user "c" has a cyberbullying rate of 0% from the data proving that the TF-IDF method can help investigators detect someone who will commit cyberbullying actions but in its development a better way is needed when preprocessing so that the abbreviation or changing words can still be detected perfectly

A practical and secure multi-keyword search method over encrypted cloud data

Cloud computing technologies become more and more popular every year, as many organizations tend to outsource their data utilizing robust and fast services of clouds while lowering the cost of hardware ownership. Although its benefits are welcomed, privacy is still a remaining concern that needs to be addressed. We propose an efficient privacy-preserving search method over encrypted cloud data that utilizes minhash functions. Most of the work in literature can only support a single feature search in queries which reduces the effectiveness. One of the main advantages of our proposed method is the capability of multi-keyword search in a single query. The proposed method is proved to satisfy adaptive semantic security definition. We also combine an effective ranking capability that is based on term frequency-inverse document frequency (tf-idf) values of keyword document pairs. Our analysis demonstrates that the proposed scheme is proved to be privacy-preserving, efficient and effective