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Sentiment analysis provides valuable insights into people's opinions, emotions, and attitudes, enabling businesses to make more informed decisions, improve customer satisfaction, and stay competitive in today's market. Now-a-days due to the accessibility of social networking platforms like Twitter, Instagram, Facebook, WeChat, etc a bulk of data is being generated within a very small span of time. These data inherit highly potential information hidden in them. Consequently, these data may be analyzed using powerful tools of deep learning techniques to achieve tremendous societal benefits. Keeping these views in mind, the authors have planned an extensive experimental study to undertake the task of sentiment analysis. In this present work, thirty-six different Word2Vec and GloVe Embedded deep learning models have been developed on the deep learning based architecture namely Multilayer Perceptron (MLP), Convolutional Neural Network(CNN), Long-and-Short-Term Memory Network (LSTM), Bi-directional LSTM (Bi-LSTM), Gated Recurrent Unit (GRU), and Bi-directional GRU (Bi-GRU). All the developed models are compared based on their accuracies, F1-scores, and other evaluation parameters. Several promising results have emerged from this study. It has been observed that the Word2Vec embedded Bi-directional GRU model yields the best result with an average F1-score of 0.84 in the case of a train-test ratio of 80:20. Finally this type of elaborated comparative and comprehensive study may be considered unique in its nature and supposed to be used to develop expert sentiment analyser. [ABSTRACT FROM AUTHOR]

Deep learning models in recent times have shown promising results for solar energy forecasting. Solar energy depends heavily on local weather conditions, and as a result, typically hundreds of models are built, which need site and season-specific training. The model maintenance and management also become a tedious job with such a large number of models. Here, we are motivated to use transfer learning to accommodate local variations in the solar pattern over the available global pattern. It may also be noted that apparently transfer learning has been rarely/never used for solar forecasting. In this paper, we have proposed a bidirectional gated recurrent unit (BGRU) based model, which employs transfer learning for short-term solar energy forecasting. The said model yields better forecasting accuracy compared to site-specific models with a lower variance. It also takes 39.6% less parameters and 76.1% reduced time for training. The current literature suggests that selection of base scenario for transfer learning is an open problem and in this paper, we have also proposed an intuitive strategy for the same. The effectiveness of the same is established through empirical study. [ABSTRACT FROM AUTHOR]

Entity typing (ET) is the process of identifying the semantic types of every entity within a corpus. ET involves labelling each entity mention with one or more class labels. As a multi-class, multi-label task, it is considerably more challenging than named entity recognition. This means existing entity typing models require pre-identified mentions and cannot operate directly on plain text. Pipeline-based approaches are therefore used to join a mention extraction model and an entity typing model to process raw text. Another key limiting factor is that these mention-level ET models are trained on fixed context windows, which makes the entity typing results sensitive to window size selection. In light of these drawbacks, we propose an end-to-end entity typing model (E2EET) using a Bi-GRU to remove the dependency on window size. To demonstrate the effectiveness of our E2EET model, we created a stronger baseline mention-level model by incorporating the latest contextualised transformer-based embeddings (BERT). Extensive ablative studies demonstrate the competitiveness and simplicity of our end-to-end model for entity typing. [ABSTRACT FROM AUTHOR]

E-commerce websites produce a large number of online reviews, posts, and comments about a product or service. These reviews are used to assist consumers in buying or recommending a product. However, consumers are expressing their views on a specific aspect category of a product. In particular, aspect category detection is one of the subtasks of aspect-based sentiment analysis, and it classifies a given text into a set of predefined aspects. Naturally, a class imbalance problem occurs in real-world applications. The class imbalance is studied over the last two decades using machine learning algorithms. However, there is very little empirical research in deep learning with the class imbalance problem. In this paper, we propose bidirectional LSTM and GRU networks to deal with imbalance aspect categories. The proposed method applies a data-level technique to reduce class imbalance. Specifically, we employ the stratified sampling technique to deal with imbalanced classes. Moreover, we create word vectors with the corpus-specific word embeddings and pre-trained word embeddings. This word representations fed into the proposed method and their merge modes such as addition, multiplication, average, and concatenation. The performance of this method is evaluated with a confusion matrix, precision, recall, F1-score with micro-average, macro-average, and weighted average. The experimental result analysis suggests that the proposed method outperforms with pre-trained word embeddings. [ABSTRACT FROM AUTHOR]