Recommendation systems (RSs) have garnered immense interest for applications in e-commerce and digital media. Traditional approaches in RSs include such as collaborative filtering (CF) and content-based filtering (CBF) through these approaches that have certain limitations, such as the necessity of prior user history and habits for performing the task of recommendation. To minimize the effect of such limitation, this article proposes a hybrid RS for the movies that leverage the best of concepts used from CF and CBF along with sentiment analysis of tweets from microblogging sites. The purpose to use movie tweets is to understand the current trends, public sentiment, and user response of the movie. Experiments conducted on the public database have yielded promising results.

Rainfall prediction is an important task due to the dependence of many people on it, especially in the agriculture sector. Prediction is difficult and even more complex due to the dynamic nature of rainfalls. In this study, we carry out monthly rainfall prediction over Simtokha a region in the capital of Bhutan, Thimphu. The rainfall data were obtained from the National Center of Hydrology and Meteorology Department (NCHM) of Bhutan. We study the predictive capability with Linear Regression, Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional Long Short Term Memory (BLSTM) based on the parameters recorded by the automatic weather station in the region. Furthermore, this paper proposes a BLSTM-GRU based model which outperforms the existing machine and deep learning models. From the six different existing models under study, LSTM recorded the best Mean Square Error (MSE) score of 0.0128. The proposed BLSTM-GRU model outperformed LSTM by 41.1% with a MSE score of 0.0075. Experimental results are encouraging and suggest that the proposed model can achieve lower MSE in rainfall prediction systems.

Sentiment analysis is a rapidly growing field of research due to the explosive growth in digital information. In the modern world of artificial intelligence, sentiment analysis is one of the essential tools to extract emotion information from massive data. Sentiment analysis is applied to a variety of user data from customer reviews to social network posts. To the best of our knowledge, there is less work on sentiment analysis based on the categorization of users by demographics. Demographics play an important role in deciding the marketing strategies for different products. In this study, we explore the impact of age and gender in sentiment analysis, as this can help e-commerce retailers to market their products based on specific demographics. The dataset is created by collecting reviews on books from Facebook users by asking them to answer a questionnaire containing questions about their preferences in books, along with their age groups and gender information. Next, the paper analyzes the segmented data for sentiments based on each age group and gender. Finally, sentiment analysis is done using different Machine Learning (ML) approaches including maximum entropy, support vector machine, convolutional neural network, and long short term memory to study the impact of age and gender on user reviews. Experiments have been conducted to identify new insights into the effect of age and gender for sentiment analysis.

Sentiment Analysis is becoming an essential task for academics, as well as for commercial companies. However, most current approaches only identify the overall polarity of a sentence, instead of the polarity of each aspect mentioned in the sentence. Aspect-Based Sentiment Analysis (ABSA) identifies the aspects within the given sentence, and the sentiment that was expressed for each aspect. Recently, the use of pre-trained models such as BERT has achieved state-of-the-art results in the field of natural language processing. In this paper, we propose two ensemble models based on multilingual-BERT, namely, mBERT-E-MV and mBERT-E-AS. Using different methods, we construct an auxiliary sentence from this aspect and convert the ABSA problem to a sentence-pair classification task. We then fine-tune different pre-trained BERT models and ensemble them for a final prediction based on the proposed model; we achieve new, state-of-the-art results for datasets belonging to different domains in the Hindi language.