International Journal of Knowledge Engineering and Soft Data Paradigms
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International Journal of Knowledge Engineering and Soft Data Paradigms (2 papers in press)
Prediction of Stock Market Price Movements Based on Sentiment Analysis on Various News Headlines by Kazi Rafshan Hasin, Sadman Hasan, M. Rashedur Rahman Abstract: Due to the recent advances in social media, stock-related news is available on every social media platform, imposing a value to improve stock price prediction performance. We used linear discriminant analysis to screen chip indicators, and we build a basic stock prediction model. We have gathered stock-related news headlines and then quantify the unstructured data into sentiment scores using text mining technology which can be correlated with the stock price changes. We have proposed an augmented prediction model, and our results suggest that prediction accuracy for stock prices can be improved, and it will be advantageous for stock investors regarding their investment strategies. In this research, we have analyzed more than 49,000 news headlines to determine the relationship between the news headlines and stock price on a given date. Using novel data visualization and Natural Language Processing techniques, we have implemented data visualizations showing how the company share prices are affected based on the sentiment scores we are getting from analyzing the news headlines. Keywords: Stock Price; Sentiment Analysis; LDA; Prediction.
Bengali Article Classification Using Ensemble Machine Learning Algorithms by Niaz Ashraf Khan, Emrul Hasan Zawad, M. Rashedur Rahman Abstract: Text classification is one of the most challenging problems in Natural Language Processing (NLP). Language models are at the heart of Natural Language Processing. The ability to represent texts as numbers has given rise to many Natural Language Processing tasks, for example, text categorization, translation, and summarization. Unfortunately, NLP for Bengali texts has not reached the state-of-art level of other Languages like English yet, mostly due to the scarcity of resources and the complexities seen in Bengali grammar. Therefore, not much work has been done in this field. In this paper, we have studied one of the word embedding methods, Word2vec, based on Continuous Bag of Words (CBOW) with several ensemble machine learning algorithms, e.g., Adaptive Boosting Classifiers, Light Gradient Boosting Machine, XGboost, and Random Forest Classifiers. The model is trained on a large corpus of Bengali news articles of a considerable size that has 99283949 words and 8284804 sentences in 392772 documents. In our experiment, Word2vec CBOW model with XGboost algorithm performed much better than other models and achieved 92.24% accuracy. Keywords: NLP; Categorization; Document Classification; Decision Tree Classifier.