Abstract-We study the problem of predicting the political preference of users on the Twitter network, showing that the political preference of users can be predicted from their interaction with political parties. We show this by building prediction models based on a variety of contextual and behavioural features, training the models by resorting to a distant supervision approach and considering party candidates to have a predefined preference towards their parties. A language model for each party is learned from the content of the tweets by the party candidates, and the preference of a user is assessed based on the alignment of user tweets with the language models of the parties. We evaluate our work in the context of Alberta 2012 general election, and show that our model outperforms, in terms of the F-measure, sentiment and text classification approaches and is in par with the human annotators. We further use our model to analyze the preference changes over the course of the election campaign and report results that would be difficult to attain by human annotators.

Cardiac disease treatments are often being subjected to the acquisition and analysis of vast quantity of digital cardiac data. These data can be utilized for various beneficial purposes. These data’s utilization becomes more important when we are dealing with critical diseases like a heart attack where patient life is often at stake. Machine learning and deep learning are two famous techniques that are helping in making the raw data useful. Some of the biggest problems that arise from the usage of the aforementioned techniques are massive resource utilization, extensive data preprocessing, need for features engineering, and ensuring reliability in classification results. The proposed research work presents a cost-effective solution to predict heart attack with high accuracy and reliability. It uses a UCI dataset to predict the heart attack via various machine learning algorithms without the involvement of any feature engineering. Moreover, the given dataset has an unequal distribution of positive and negative classes which can reduce performance. The proposed work uses a synthetic minority oversampling technique (SMOTE) to handle given imbalance data. The proposed system discarded the need of feature engineering for the classification of the given dataset. This led to an efficient solution as feature engineering often proves to be a costly process. The results show that among all machine learning algorithms, SMOTE-based artificial neural network when tuned properly outperformed all other models and many existing systems. The high reliability of the proposed system ensures that it can be effectively used in the prediction of the heart attack.

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