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  • Author: Asif Javed x
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Abstract

Sentiment classification is the process of exploring sentiments, emotions, ideas and thoughts in the sentences which are expressed by the people. Sentiment classification allows us to judge the sentiments and feelings of the peoples by analyzing their reviews, social media comments etc. about all the aspects. Machine learning techniques and Lexicon based techniques are being mostly used in sentiment classification to predict sentiments from customers reviews and comments. Machine learning techniques includes several learning algorithms to judge the sentiments i.e Navie bayes, support vector machines etc whereas Lexicon Based techniques includes SentiWordnet, Wordnet etc. The main target of this survey is to give nearly full image of sentiment classification techniques. Survey paper provides the comprehensive overview of recent and past research on sentiment classification and provides excellent research queries and approaches for future aspects

Abstract

Polyester is a popular class of material used in material engineering. With its 0.4% moisture regain, polyethylene terephthalate (PET) is classified as highly hydrophobic, which originates from its lack of polar groups on its backbone. This study used a parallel-plate nonthermal plasma dielectric barrier discharge system operating at medium pressure in dry air and nitrogen (N2) to alter the surface properties of PET fabrics to increase their hydrophilic capabilities. Water contact angle, atomic force microscopy (AFM), and X-ray photoelectron spectroscopy (XPS) were utilized to analyze any effect from the plasma treatment. The wettability analysis revealed a reduction in the contact angle of more than 80% within 5 min for both discharges. Scanning electron microscopy analysis showed no microscopic damage to the fiber structure, guaranteeing that the fabrics’ structural integrity was preserved after treatment. AFM analysis showed an increase in the nanometer roughness, which was considered beneficial because it increased the total surface area, further increasing the hydrophilic capacity. XPS analysis revealed a sharp increase in the presence of polar functional groups, indicating that the induced surface changes are mostly chemical in nature. Comparing that of untreated fabrics to treated fabrics, a Increase in water absorption capacity was observed for air-treated and N2-treated fabrics, when these fabrics were used immediately after plasma exposure.

Abstract

Nanocrystalline NiFe2O4 particles were prepared by conventional sol-gel, citrate-nitrate sol-gel combustion and co-precipitation methods. The synthesized samples were annealed at 1000 °C for two hours and structural, chemical, morphological, optical and magnetic properties of nickel ferrite were investigated. The structural properties were investigated by X-ray diffraction (XRD) technique which confirmed the formation of single phase NiFe2O4 particles derived by the three methods. The chemical properties were analyzed by Fourier transform infrared (FT-IR) spectroscopy which confirmed the corresponding vibration modes in the samples. The optical properties were studied by UV-Vis spectroscopy. The morphological study of the as-synthesized samples was carried out by scanning electron microscopy (SEM). SEM images showed the agglomerated nanoparticles of NiFe2O4. The magnetic properties were investigated by vibrating sample magnetometer (VSM), which showed that the calcined samples exhibited typical magnetic behavior.