The Semantic Web aims to make information intelligible for computers. In the Semantic Web, unstructured information from text is represented using ontologies, such that computers can understand text better. However, adding text information to existing ontologies by hand is time-consuming. Information extraction rules can help to automate this process. In the process of learning information extraction rules, patterns are constructed that consist of lexico-syntactic and lexico-semantic features from text, which aim to extract Resource Description Framework subject-predicate-object expressions. In this paper, we investigate the following four metaheuristics for learning ontology-based information extraction rules: Particle Swarm Optimization, 2-Phase Optimization, Ant Colony Optimization, and Genetic Algorithm (GA). We evaluate all methods using financial news data. GA gives the best F1-measure results, but the other metaheuristics are faster.

Currently, deep learning models are commonly used for Aspect-Based Sentiment Analysis (ABSA). These deep learning models are often seen as black boxes, meaning that they are inherently difficult to interpret. To improve deep learning models, it is crucial to understand their inner workings. We aim to interpret black box models by implementing model-agnostic local interpretation methods. Inspired by Local Interpretable Model-agnostic Explanations (LIME) and Local Rule-based Explanations (LORE) and combined with a Similarity-based Sampling (SS) method, we propose SS-LIME and SS-LORE, and use Anchor to explain two state-of-the-art ABSA deep learning models. The deep learning models build upon the Left-Center-Right separated neural network with Rotatory attention (LCR-Rot) model, extended by iterating multiple times over the rotatory attention mechanism (LCR-Rot-hop) and hierarchical attention and context-dependent word embeddings (LCR-Rot-hop++). We evaluate the proposed models in terms of fidelity, hit rate, and user interpretability using the SemEval 2016 dataset consisting of restaurant reviews for ternary sentiment classification. Results show that the LCR-Rot-hop and LCR-Rot-hop++ models are best explained by SS-LIME and SS-LORE, respectively. Furthermore, we conclude that the LCR-Rot-hop++ model can be better interpreted than the LCR-Rot-hop model. • We explain the predictions of two deep learning models for sentiment analysis. • We provide interpretation models for ternary classification. • We use post-hoc classifiers with a homogeneous sampling method. • We extend the Submodular Pick algorithm by considering local importance.

Sentiment analysis extracts the sentiment of content creators, enabling users to easily gain valuable insights from such data. Most existing methods rely on supervised learning approaches using labeled data. However, the retrieval of such labeled training data is difficult and expensive, especially for new domains and/or languages. This work focuses on simultaneously detecting aspect categories and sentiment polarities for a given sentence in a weakly-supervised setting. Two methods are proposed that combine an unsupervised labeling algorithm with a neural network architecture. The first proposed two-step model (SB-ASC) takes seed sentences as input for the labeling algorithm. By leveraging the power of pre-trained Sentence-BERT embeddings, the method is able to understand the contextual meaning of sentences to create a high-quality labeled dataset. This dataset is used by a class imbalance-robust BERT-based neural network that jointly learns latent features of aspect categories and the corresponding sentiment. The second proposed method (WB-ASC) uses the same neural network structure but takes seed words instead of seed sentences as input for the labeling algorithm. We conclude that SB-ASC outperforms WB-ASC as well as baselines and state-of-the-art weakly-supervised methods for aspect sentiment detection, achieving F1 scores for aspect category detection of 71.35%, 86.99%, and 73.86%, and F1 scores for sentiment classification of 89.24%, 89.98%, and 75.58% for the SemEval 2016 restaurant-5, restaurant-3, and laptop datasets, respectively. Furthermore, using domain-specific contextual language models boosts performance. • We predict aspect categories and sentiment polarities using weak supervision. • We propose two weak supervision methods. • We find that using seed sentences instead of words as input improves predictions. • We show that using domain-specific contextual language models boosts performance.

This work researches the drivers of e-commerce conversion of one of the largest e-commerce companies in the Netherlands. We focus on product page conversion, i.e., the probability that a customer who visits a specific product page also buys the product. This probability differs between products, which we explain by a variety of factors like pricing, delivery times, reviews, seller type, and the quality of the content. Understanding the drivers of conversion is the first step in increasing it, and therefore an important step in increasing overall sales. We describe the process of transforming Big Data into valuable insights using Bayesian statistics and apply a Bayesian Binomial model to a dataset of 15 million records using a distributed MCMC algorithm. In addition, we apply a simple Binomial GLM on a small sample of these 15 million records for comparison. We find that using the full 15 million records results in a significant reduction in the variance of the estimated parameters. In terms of the actual drivers of e-commerce conversion, we observe that products with competitive pricing, short delivery times, and good content all achieve a higher conversion on average. Furthermore, reviews, average review score, and the number of reviews for a product have a large positive effect on conversion compared to other characteristics. This provides a unique insight into what makes people decide whether or not to purchase a product.

Every day millions of news articles and (micro)blogs that contain financial information are posted online. These documents often include insightful financial aspects with associated sentiments. In this paper, we predict financial aspect classes and their corresponding polarities (sentiment) within sentences. We use data from the Financial Question & Answering (FiQA) challenge, more precisely the aspect-based financial sentiment analysis task. We incorporate the hierarchical structure of the data by using the parent aspect class predictions to improve the child aspect class prediction (two-step model). Furthermore, we incorporate model output from the child aspect class prediction when predicting the polarity. We improve the F1 score by 7.6% using the two-step model for aspect classification over direct aspect classification in the test set. Furthermore, we improve the state-of-the-art test F1 score of the original aspect classification challenge from 0.46 to 0.70. The model that incorporates output from the child aspect classification performs up to par in polarity classification with our plain RoBERTa model. In addition, our plain RoBERTa model outperforms all the state-of-the-art models, lowering the MSE score by at least 28% and 33% for the cross-validation set and the test set, respectively.