Over the last period, social media achieved a widespread use worldwide where the statistics indicate that more than three billion people are on social media, leading to large quantities of data online. To analyze these large quantities of data, a special classification method known as sentiment analysis, is used. This paper presents a new sentiment analysis system based on machine learning techniques, which aims to create a process to extract the polarity from social media texts. By using machine learning techniques, sentiment analysis achieved a great success around the world. This paper investigates this topic and proposes a sentiment analysis system built on Bayesian Rough Decision Tree (BRDT) algorithm. The experimental results show the success of this system where the accuracy of the system is more than 95% on social media data.
In this work lactone (1) was prepared from the reaction of p-nitro phenyl hydrazine with ethylacetoacetate, which upon treatment with benzoyl chloride afforded the lactame (2). The reaction of (2) with 2-amino phenol produced a new Schiff base (L) in good yield. Complexes of V(IV), Zr(IV), Rh(III), Pd(II), Cd(II) and Hg(II) with the new Schiff base (L) have been prepared. The compounds (1, 2) were characterized by FT-IR and UV spectroscopy, as well as characterizing ligand (L) by the same techniques with elemental analysis (C.H.N) and (1H-NMR). The prepared complexes were identified and their structural geometries were suggested by using elemental analysis (C.H.N), flame atomic absorption technique, FT-IR and UV-Vis spectroscopy, in additio
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