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    Building Domain-Specific Lexicons: An Application to Financial News
    (Institute of Electrical and Electronics Engineers Inc., 2019) Yildirim, S.; Jothimani, D.; Kavaklio?lu, C.; Bener, A.
    Natural Language Processing (NLP) has gained attention in the recent years. Previous research (such as WordNet and Cyc) has focused on developing an all purpose (generalised) polarised lexicons. However, these lexicons do not provide much information in different domains such as Finance and Medical Sciences. Using these lexicons for text classification could affect the prediction accuracy. Therefore, there is a need for building domain- and context-specific lexicons. To achieve this, in this work, a label based propagation based word embedding algorithm has been proposed to obtain positive and negative lexicons. The proposed algorithm works on the principle of graph theory and word embedding. The proposed algorithm is tested on Dow Jones news wires text feed to classify the Financial news as hot and non-hot. Three classifiers, namely, Logistic Regression, Random Forest and XGBoost, employing polarised lexicons, seed words and random words were used. The performance of classifiers in all cases was evaluated using accuracy. Lexicons generated using the proposed approach were effective in classifying the Financial news articles as hot and non-hot compared to classifiers using seed words and random words. Proposed label propagation with word embedding algorithm generates context-specific lexicons, which aids in helps in better representation of text in natural processing tasks and avoids the problem of dimensionality. © 2019 IEEE.
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    Classification of »hot News» for Financial Forecast Using NLP Techniques
    (Institute of Electrical and Electronics Engineers Inc., 2018) Yildirim, S.; Jothimani, D.; Kavaklioglu, C.; Başar, A.
    Complex dynamics of stock market could be attributed to various factors ranging from company's financial ratios to investors' sentiment and reaction to Financial news. The paper aims to classify Financial news articles as »hot» (significant) and »non-hot» (non-significant). The study is carried out using Dow Jones newswires text feed for a period of four years spanning from 2013 till 2017. Bag-of-ngrams appraoch and Term Frequency-Inverse Document Frequency (TF-IDF) were used for text representation and text weighting, respectively. Four linear classifiers, namely, Logistic Regression (LR), Support Vector Machine (SVM), k Nearest Neighbours (kNN) and multinomial Naïve Bayes (mNB) were used. Grid search was used for hyperparameter optimisation. Performance of the classifiers was evaluated using five measures, namely, success rate, precision, recall, F1 measure and area under receiver operating characteristics curve. LR and SVM outperformed other models in terms of all five performance measures for both Bag-of-ngrams model and Bag-of-ngrams model with TF-IDF approach. Use of TF-IDF improved performance of the classifiers, especially, in case of mNB. This study serves as a stepping stone in identification of important/relevant news, which could used as predictors for stock price forecasting. © 2018 IEEE.