Towards optimize-ESA for text semantic similarity: A case study of biomedical text

Explicit Semantic Analysis (ESA) is an approach to measure the semantic relatedness between terms or documents based on similarities to documents of a references corpus usually Wikipedia. ESA usage has received tremendous attention in the field of natural language processing NLP and information retrieval. However, ESA utilizes a huge Wikipedia index matrix in its interpretation by multiplying a large matrix by a term vector to produce a high-dimensional vector. Consequently, the ESA process is too expensive in interpretation and similarity steps. Therefore, the efficiency of ESA will slow down because we lose a lot of time in unnecessary operations. This paper propose enhancements to ESA called optimize-ESA that reduce the dimension at the interpretation stage by computing the semantic similarity in a specific domain. The experimental results show clearly that our method correlates much better with human judgement than the full version ESA approach

A dropout predictor system in MOOCs based on neural networks

Massive open online courses, MOOCs, are a recent phenomenon that has achieved a tremendous media attention in the online education world. Certainly, the MOOCs have brought interest among the learners (given the number of enrolled learners in these courses). Nevertheless, the rate of dropout in MOOCs is very important. Indeed, a limited number of the enrolled learners complete their courses. The high dropout rate in MOOCs is perceived by the educator’s community as one of the most important problems. It’s related to diverse aspects, such as the motivation of the learners, their expectations and the lack of social interactions. However, to solve this problem, it is necessary to predict the likelihood of dropout in order to propose an appropriate intervention for learners at-risk of dropping out their courses. In this paper, we present a dropout predictor model based on a neural network algorithm and sentiment analysis feature that used the clickstream log and forum post data. Our model achieved an average AUC (Area under the curve) as high as 90% and the model with the feature of the learner’s sentiments analysis attained average increase in AUC of 0.5%

A Bayesian CNN-LSTM Model for Sentiment Analysis in Massive Open Online Courses MOOCs

Massive Open Online Courses (MOOCs) are increasingly used by learn-ers to acquire knowledge and develop new skills. MOOCs provide a trove of data that can be leveraged to better assist learners, including behavioral data from built-in collaborative tools such as discussion boards and course wikis. Data tracing social interactions among learners are especially inter-esting as their analyses help improve MOOCs’ effectiveness. We particular-ly perform sentiment analysis on such data to predict learners at risk of dropping out, measure the success of the MOOC, and personalize the MOOC according to a learner’s behavior and detected emotions. In this pa-per, we propose a novel approach to sentiment analysis that combines the advantages of the deep learning architectures CNN and LSTM. To avoid highly uncertain predictions, we utilize a Bayesian neural network (BNN) model to quantify uncertainty within the sentiment analysis task. Our em-pirical results indicate that: 1) The Bayesian CNN-LSTM model provides interesting performance compared to other models (CNN-LSTM, CNN, LSTM) in terms of accuracy, precision, recall, and F1-Score; and 2) there is a high correlation between the sentiment in forum posts and the dropout rate in MOOCs