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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.

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Methyl ethyl ketone (MEK) was converted to heavier ketones in one step, using a multi-functional catalyst having both aldol condensation (aldolization and dehydration) and hydrogenation properties. 15% Cu supported zirconia (ZrO2) was investigated in the catalytic gas phase reaction of MEK in a fixed bed reactor. The results showed that the main product was 5-methyl-3-heptanone (C8 ketone), with side products including 5-methyl-3-heptanol, 2-butanol, and other heavy products (C12 and up). The effects of various reaction parameters, like temperature and molar ratio of reactants (H2/MEK), on the overall product selectivity were studied. It was found that with increasing the temperature of the reaction, the selectivity to the C8 ketone increased, while selectivity to the 2-butanol decreased. Also, hydrogen pressure played a significant role in the selectivity of the products. It was observed that with increasing the H2/MEK molar ratio, the 2-butanol selectivity increased because of the hydrogenation reaction, while decreasing this ratio led to increasing the aldol condensation products. In addition, it was noted that both the conversion and selectivity to the main product increased using a low loading percentage of copper, 1% Cu–ZrO2. The highest selectivity of 5-methyl-3-heptanone reached ~64%, and was obtained at a temperature of around 180 °C and a molar ratio of H2/MEK equal to 2. Other metals (Ni, Pd, and Pt) that were supported on ZrO2 also produced 5-methyl-3-heptanone as the main product, with slight differences in selectivity, suggesting that a hydrogenation catalyst is important for producing the C8 ketone, but that the exact identity of the metal is less important.