With the advent of deep neural models in natural language processing tasks, having a large amount of training data plays an essential role in achieving accurate models. Creating valid training data, however, is a challenging issue in many low-resource languages. This problem results in a significant difference between the accuracy of available natural language processing tools for low-resource languages compared with rich languages. To address this problem in the sentiment analysis task in the Persian language, we propose a cross-lingual deep learning framework to benefit from available training data of English. We deployed cross-lingual embedding to model sentiment analysis as a transfer learning model which transfers a model from a rich-resource language to low-resource ones. Our model is flexible to use any cross-lingual word embedding model and any deep architecture for text classification. Our experiments on English Amazon dataset and Persian Digikala dataset using two different embedding models and four different classification networks show the superiority of the proposed model compared with the state-of-the-art monolingual techniques. Based on our experiment, the performance of Persian sentiment analysis improves 22% in static embedding and 9% in dynamic embedding. Our proposed model is general and language-independent; that is, it can be used for any low-resource language, once a cross-lingual embedding is available for the source–target language pair. Moreover, by benefitting from word-aligned cross-lingual embedding, the only required data for a reliable cross-lingual embedding is a bilingual dictionary that is available between almost all languages and the English language, as a potential source language.

Financial markets received more attention due to technological advancements, such as Artificial Intelligence (AI). In addition to the price index, traders and investors constantly monitor stock news on social media. Therefore, predicting the market by analyzing public opinions is an important issue. In this research, we propose three models based on Generative Adversarial Network (GAN), namely Price-GAN, Price-Sentiment-GAN, and Price-Sentiment-WGAN. The first model uses only optimized price features, and the two other models use sentiment features collected from social media as well as optimized price features. All the proposed GAN models include Long Short-Term Memory (LSTM) as generators and Convolution Neural Networks (CNN) as discriminators. To evaluate the proposed models, two different social media datasets in English and Persian are used. Our proposed models predict the close stock price for 15 English and 5 Persian stocks. All of the proposed GAN models outperform the state-of-the-art models by enhancing the performance of the English dataset by 2.44% and the Persian dataset by 12.11%.