The Internet has seen substantial growth of regional language data in recent years. It enables people to express their opinion by incapacitating the language barriers. Urdu is a language used by 170.2 million people for communication. Sentiment analysis is used to get insight of people opinion. In recent years, researchers' interest in Urdu sentiment analysis has grown. Application of deep learning methods for Urdu sentiment analysis has been least explored. There is a lot of ground to cover in terms of text processing in Urdu since it is a morphologically rich language. In this paper, we propose a framework for Urdu Text Sentiment Analysis (UTSA) by exploring deep learning techniques in combination with various word vector representations. The performance of deep learning methods such as Long Short-Term Memory (LSTM), attention-based Bidirectional LSTM (BiLSTM-ATT), Convolutional Neural Networks (CNN) and CNN-LSTM is evaluated for sentiment analysis. Stacked layers are applied in sequential model LSTM, BiLSTM-ATT, and C-LSTM. In CNN, various filters are used with single convolution layer. Role of pre-trained and unsupervised self-trained embedding models is investigated on sentiment classification task. The results obtained show that the BiLSTM-ATT outperformed other deep learning models by accomplishing 77.9% accuracy and 72.7% F1 score.
This study explores the anisotropic stellar structures in the background of [Formula: see text] modified gravity, where [Formula: see text] is a Ricci scalar. To complete this analysis, we use the spherically symmetric space-time. Further, an anisotropic source of matter is used to investigate the stability of stellar structures. The embedded class-I approach is used to demonstrate the behavior of stellar structures. Schwarzschild space-time is taken as exterior space-time to calculate the values of involved parameters. The observational data of three different stars, like LMC X-4, Cen X-3, and EXO 1785-248, are taken for the current analysis. For this study, we used Starobinsky-like model [Formula: see text] for [Formula: see text] modified gravity. The stability analysis is discussed via the equation of state (EoS) parameters, Tolman–Oppenheimer–Volkoff (TOV) equation, anisotropy, etc. Graphical and analytical results are provided as viable results in [Formula: see text] gravity.
Twitter sentiment analysis is a challenging task that involves various preprocessing steps including dimensionality reduction. Dimensionality reduction helps ensure low computational complexity and performance improvement during the classification process. In Twitter data, each tweet has feature values which may or may not reflect a person's response. Therefore, a large number of sparse data points are generated when tweets are represented as feature matrix, eventually increasing computational overheads and error rates in Twitter sentiment analysis. This study proposes a novel preprocessing technique called class association and attribute relevancy based imputation algorithm (CAARIA) to reduce the Twitter data size. CAARIA achieves the dimensionality reduction goal by imputing those tweets that belong to the same class and also share useful information. The performance of two classifiers (Naïve Bayes and support vector machines) is evaluated on three Twitter datasets in terms of classification accuracy, measured as area under curve, and time efficiency. CAARIA is also compared against two widely used feature selection (dimensionality reduction) techniques, information gain (IG) and Pearson's correlation (PC). The findings reveal that CAARIA outperforms IG and PC in terms of classification accuracy and time efficiency. These results suggest that CAARIA is a robust data preprocessing technique for the classification task.
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