Sentiment is currently one of the most emerging areas of research due to the large amount of web content coming from social networking websites. Sentiment analysis is a crucial process for recommending systems for most people. Generally, the purpose of sentiment analysis is to determine an author’s attitude toward a subject or the overall tone of a document. There is a huge collection of studies that make an effort to predict how useful online reviews will be and have produced conflicting results on the efficacy of different methodologies. Furthermore, many of the current solutions employ manual feature generation and conventional shallow learning methods, which restrict generalization. As a result, the goal of this research is to develop a general approach using transfer learning by applying the “BERT (Bidirectional Encoder Representations from Transformers)”-based model. The efficiency of BERT classification is then evaluated by comparing it with similar machine learning techniques. In the experimental evaluation, the proposed model demonstrated superior performance in terms of outstanding prediction and high accuracy compared to earlier research. Comparative tests conducted on positive and negative Yelp reviews reveal that fine-tuned BERT classification performs better than other approaches. In addition, it is observed that BERT classifiers using batch size and sequence length significantly affect classification performance.

Essay writing is widely used for student performance assessment. This paper presents a hybrid automatic essay scoring system (AES) for Arabic essays. The system attempts at saving the time teachers spend on reading and scoring Arabic essays. It utili

Many techniques in Relation Extraction (RE) require an understanding of the concept and association of relations between entities. This paper presents advanced approaches that combined many methods to extract simple and complex relations between different entities from the biomedical literature. The literature comparative study includes a wide variety of techniques for RE all of which fall into one of the following approaches such as supervised, semi-supervised, higher order relation and self-supervised approaches. Supervised approach needs very detailed data, while in semi-supervised approach only a small knowledge base to automatically annotates features. This approach does not need an initial set of labeled pages for extraction rules. Self-supervised is an integration of these two approaches. On the other hand, the term higher order relation indicates a relation that connects different biomedical relations together. The F-score performance differs from relation to relation when it is extracted. Finally, an extensive performance evaluations with real datasets for both supervised, semi-supervised, higher order relation and self-supervised approaches are described for entity - RE tasks.

Nowadays, one sub field of Information Extraction (IE) and Named Entity Recognition (NER) becomes more important. It is essential to recognize information units like the phrases and digital numbers. Identifying references to these entities in text was recognized as one of the important sub-tasks of IE and was called "NER and Classification ". In this paper, we propose a (NER) technique, which uses text classification methods for annotating the phrases and digital numbers to develop the query to be searched in the index. We also investigate which exploits the duality between set of the phrases and digital numbers to grow the target relation, assign the high scores, a passage extraction and ranking component. The overall architecture applied during development is described. To test our technique we use it to extract a relation of the phrases and digits pairs from the Web.