No abstract

At a time when research in the field of sentiment analysis tends to study advanced topics in languages, such as English, other languages such as Arabic still suffer from basic problems and challenges, most notably the availability of large corpora. Furthermore, manual annotation is time-consuming and difficult when the corpus is too large. This paper presents a semi-supervised self-learning technique, to extend an Arabic sentiment annotated corpus with unlabeled data, named AraSenCorpus. We use a neural network to train a set of models on a manually labeled dataset containing 15,000 tweets. We used these models to extend the corpus to a large Arabic sentiment corpus called “AraSenCorpus”. AraSenCorpus contains 4.5 million tweets and covers both modern standard Arabic and some of the Arabic dialects. The long-short term memory (LSTM) deep learning classifier is used to train and test the final corpus. We evaluate our proposed framework on two external benchmark datasets to ensure the improvement of the Arabic sentiment classification. The experimental results show that our corpus outperforms the existing state-of-the-art systems.

Text to face generation is a sub domain of text to image synthesis, and it has a huge impact along with the wide range of applications on public safety domain. Currently, due to the lack of dataset, the research work focused on the face to text generation is very limited. Most of the work for text to face generation till now based on the partially trained generative adversarial network, in which the pre-trained text encoder has been used to extract the sematic features of input sentence. Then these semantic features have been utilized to train the image decoder. But in this research work, we have proposed the fully trained generative adversarial network to generate the realistic and natural images. We have trained the text encoder as well as the image decoder at the same time to generate the more accurate and efficient results. In addition to proposed methodology, we have also generate the dataset by the amalgamation of LFW, CelebA and locally prepared dataset. We have also labelled the images according to our defined classes. Through performing different kind of experiments, we have proved that our proposed fully trained GAN outperformed by generating the good quality images with accordance to the input sentence. Moreover, the visual results have also strengthened our experiments by generating the face images according to the given query.