Sentiment Analysis has been mainly used to understand the judgment of the text. It has been undergoing major provocation and irony detection is considered as one among the most provocations in it. Irony is the unusual way of narrating an information which disagrees the concept which leads to uncertainty. One primary task included by most developers is data preprocessing which includes many techniques like lemmatization, tokenization and stemming. Many researches are done under irony detection which includes many feature extraction techniques. Machine learning classifiers used for these researches are Support Vector Machine (SVM), linear regression, Naïve Bayes, Random Forest and many more. Results of these research works includes accuracy, precision, recall, F-score which can be used to predict the best suited model. In this paper various methodology used in irony text detection for Sentiment Analysis is discussed.
The investigation of thermal modulation on double-diffusive stationary convection in the presence of an applied magnetic field and internal heating is carried out. A weakly nonlinear stability analysis has been performed using the finite-amplitude Ginzburg-Landau model. This finite amplitude of convection is obtained at the third order of the system. The study considers three different forms of temperature modulations. OPM-out of phase modulation, LBMO-lower boundary modulation, IPM-in phase modulation. The finite-amplitude is a function of amplitude δT, frequency ω and the phase difference θ. The effects of δT and ω on heat/mass transports have been analyzed and depicted graphically. The study shows that heat/mass transports can be controlled effectively by thermal modulation. Further, it is found that the internal Rayleigh number Ri enhances heat transfer and reduces the mass transfer in the system.
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