Abstract: The stock market plays a critical role in the economic development of countries, and it is also a market closely related to our daily life. The sentiment of shareholders may be judged as one of the factors affecting the stock price. This paper proposes a deep learning model of stock sentiment analysis price prediction based on convolution long short-term memory, named semantic convolution (SCONV). The model utilizes long short-term memory model and word2vec to analyze the emotion, extracts emotion vector, and to calculate the emotion weight of each day. Then we put the corresponding weights of the daily stock prices respectively to the average of the previous day, the previous three days, and the average of the previous week, together with the stock price into the ConvLstm. There is a dropout between ConvLstm and the increased LSTM to avoid over-fitting. In this paper, BABA.us, 000001.sh, 000651.sz are used as experimental data. BABA.us about 3 years, 000001.sh about 1.5 years and 000651.sz about 5 months are respectively implemented in the experiment. Compared with traditional models, the experimental results show that SCONV is still able to predict more precisely the trend of the stock price on a smaller sample set.

Key words: artificial intelligence, long-term and short-term memory neural network, convolution, stock price forecast, deep learning