Conceptual framework for stock market classification model using sentiment analysis on twitter based on hybrid Naïve Bayes Classifiers - Alkubaisi, Ghaith Abdulsattar A. Jabbar (Science Publishing Corporation Inc, 2018)
Sentiment analysis has gained a lot of importance in last decade especially on the availability of data from Twitter that has created more interest for research in this field. Nevertheless, stock market classification models still suffer less accuracy and this has affected negatively the stock market indicators. In this paper, a new framework related to sentiment analysis from Twitter posts is proposed. The proposed framework represents an improved design of classification model that works to improve the classification accuracy to support decision makers in the domain of stock market exchange. This model starts with data collection part and in second phase filtration is done on data to get only the relevant data. The most important phase is the labelling part in which polarity of data is determined and negative, positive or neutral values are assigned to statements of people. The fourth part is the classification phase in which suitable patterns of stock market will be identified by hybridizing NBCs. The last phase is performance and evaluation. This study proposes to a Hybrid Naïve Bayes Classifiers (HNBCs) as a machine learning method for stock market classification, hence represents a useful study for investors, companies and researchers and will help them to formulate their policies according to sentiments of people.