this problem, a novel lattice long short-term memory (LSTM) model, combining a variant contextual feature representation … Lattice-LSTM model, the information of characters and words is effectively utilized to avoid error segmentation. Findings The …

-based semantic analysis (OAS-LSTM-TSA). The proposed model goes through three phases: preprocessing, aspect extraction and …-attention embedded LSTM (OAS-LSTM) is used to classify aspect sentiment into three classes: positive, neutral and negative. Findings To … detect and classify sentiment polarity of the aspect using the optimized aspect and self-attention embedded LSTM (OAS-LSTM …

learning methods (KNN and LSTM) and compare their performances to ARIMA time series model. Two scenarios have been considered …-periods: the test period and the prediction period. Findings The authors found that the KNN method performed better than LSTM and … techniques such as LSTM and KNN. To the best of our knowledge, no research has been conducted that used these techniques to …

future behavior of stock returns using a combined wavelet-based long short-term memory (LSTM). Design … taken as an input to the LSTM model to predict the stock closing price time series and the “future data” is created. In the … the future behavior of stock returns using a combined wavelet-based LSTM improved the performance of the portfolio. …

(LSTM) networks can learn from recent market history and outperform the market. Usually, these approaches depend on a low …

(LSTM) networks can learn from recent market history and outperform the market. Usually, these approaches depend on a low …

current benchmark on simulated data. We use a long short- term memory (LSTM) neural network that is trained on labelled data …