• Sheetal

Stock price prediction from the available information related to the stock market remains a challenging task due to the numerous manipulating factors such as sentiment of investors, stable performance, economic fluctuation and social media tweets having some sentiments. However, the accurate prediction of stock price movement is a profitability and economic advantage associated with the financial analyst. Despite the improvement in existing stock prediction model in term of their accuracy is a most challenging task. So, in this research work, we proposed a sentiment analysis-based stock price movement prediction model using Genetic Algorithm (GA) and Artificial Neural Network (ANN) as deep learning technique on stock social media. For the feature extraction, Lexicon-based approach is used along with the GA to select an appropriate feature set based on the fitness criteria. Firstly, we perform some pre-processing steps to remove the unwanted text such as normalization, punctuation removal, stop words removal and their tokenization. Then, we extracted features using the lexicon concept and optimized using GA to train the ANN in most effective manners. The results obtained from experiments in the stock market show that the proposed model work well compare to the state-of arts works in terms of performance.

Stock Market, Stock Market Price Prediction, Social Media, Sentiment Analysis, Lexicon Dictionary, Genetic Algorithm, Artificial Neural Network

"Sentiment Analysis-based Stock Price Movement Prediction using Optimized Deep Learning on Stock Social Media", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 7, page no.b287-b299, July-2021, Available :http://www.jetir.org/papers/JETIR2107165.pdf

"Sentiment Analysis-based Stock Price Movement Prediction using Optimized Deep Learning on Stock Social Media", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 7, page no. ppb287-b299, July-2021, Available at : http://www.jetir.org/papers/JETIR2107165.pdf