Comparative analysis of text classification algorithms for automated labelling of quranic verses

The ultimate goal of labelling a Quranic verse is to determine its corresponding theme. However, the existing Quranic verse labelling approach is primarily depending on the availability of Quranic scholars who have expertise in Arabic language and Tafseer. In this paper, we propose to automate the labelling task of the Quranic verse using text classification algorithms. We applied three text classification algorithms namely, k-Nearest Neighbour, Support Vector Machine, and Naïve Bayes in automating the labelling procedure. In our experiment with the classification algorithms English translation of the verses are presented as features. The English translation of the verses are then classified as “Shahadah” (the first pillar of Islam) or “Pray” (the second pillar of Islam). It is found that all of the text classification algorithms are capable to achieve more than 70% accuracy in labelling the Quranic verses

Sentiment analysis of text with lossless mining

Social networks are becoming more and more real with their power to influence public opinions, election outcomes, or the creation of an artificial surge in demand or supply. The continuous stream of information is valuable, but it comes with a big data problem. The question is how to mine social text at a large scale and execute machine learning algorithms to create predictive models or historical views of previous trends. This paper introduces a cyber dictionary for every user, which contains only words used in tweets - as a case study. Then, it mines all the known and unknown words by their frequency, which provides the analytic capability to run a multi-level classifier

Performance analysis of text-oriented printing using PostScript

POSTSCRIPT is a page description language which is used to transmit printing information from a host computer (i.e. Apple Macintosh) to a printer (i.e. Apple LaserWriter Plus). It has the ability to describe pages consisting of text, vector graphics, and scanned bit-map images. Printing text is the area of concentration for this thesis. Specifically several variables that affect the printing speed of a common POSTSCRIPT printer, the Apple LaserWriter Plus, are looked at when printing text in a variety of fonts, sizes, and orientations. The variables that affect printer performance include: - use of outline vs. bit-map fonts; - the outline font rasterization process; - the use of pre-cached bit-map fonts; - background outline font rasterization; - arbitrary scaling and rotation; - downloading host-resident fonts; - Adobe and Third Party host-resident downloadable fonts vs. printer-resident fonts; - Appletalk vs. RS-232 communications interfaces; - use of the POSTSCRIPT show, ashow, and widthshow instructions; - targeting the POSTSCRIPT instructions at a particular engine resolution; - print engine overhead A sequence of POSTSCRIPT files were transmitted to the Apple LaserWriter Plus printer. The experiments were carefully constructed to exercize each of the variables listed above. Performance measurements were carefully recorded and analyzed. Where applicable, improvements were proposed to improve printer performance