Topic-dependent sentiment analysis of financial blogs

While most work in sentiment analysis in the financial domain has focused on the use of content from traditional finance news, in this work we concentrate on more subjective sources of information, blogs. We aim to automatically determine the sentiment of financial bloggers towards companies and their stocks. To do this we develop a corpus of financial blogs, annotated with polarity of sentiment with respect to a number of companies. We conduct an analysis of the annotated corpus, from which we show there is a significant level of topic shift within this collection, and also illustrate the difficulty that human annotators have when annotating certain sentiment categories. To deal with the problem of topic shift within blog articles, we propose text extraction techniques to create topic-specific sub-documents, which we use to train a sentiment classifier. We show that such approaches provide a substantial improvement over full documentclassification and that word-based approaches perform better than sentence-based or paragraph-based approaches

A Multi-label Classification System to Distinguish among Fake, Satirical, Objective and Legitimate News in Brazilian Portuguese

Currently, there has been a significant increase in the diffusion of fake news worldwide, especially the political class, where the possible misinformation that can be propagated, appearing at the elections debates around the world. However, news with a recreational purpose, such as satirical news, is often confused with objective fake news. In this work, we decided to address the differences between objectivity and legitimacy of news documents, where each article is treated as belonging to two conceptual classes: objective/satirical and legitimate/fake. Therefore, we propose a DSS (Decision Support System) based on a Text Mining (TM) pipeline with a set of novel textual features using multi-label methods for classifying news articles on these two domains. For this, a set of multi-label methods was evaluated with a combination of different base classifiers and then compared with a multi-class approach. Also, a set of real-life news data was collected from several Brazilian news portals for these experiments. Results obtained reported our DSS as adequate (0.80 f1-score) when addressing the scenario of misleading news, challenging the multi-label perspective, where the multi-class methods (0.01 f1-score) overcome by the proposed method. Moreover, it was analyzed how each stylometric features group used in the experiments influences the result aiming to discover if a particular group is more relevant than others. As a result, it was noted that the complexity group of features could be more relevant than others

An Exploration of Representation Learning and Sequential Modeling Approaches for Supervised Topic Classification in Job Advertisements

This thesis applies the explorative double diamond design process borrowed to iteratively frame a research problem applicable in the context of a recruitment web service and then find the best approach to solve it. Thereby the problem focus is laid on multi-class classification, in particular the task of labelling sentences in job advertisements with one of six topics which were found to be covered in every typical job description. A dataset is obtained for evaluation and conventional N-Gram Vector Space models are compared with Representation Learning approaches, notably continuous distributed representations, and Sequential Modeling techniques using Recurrent Neural Networks. Results of the experiments show that the Representation Learning and Sequential Modeling approaches perform on par or better than traditional feature engineering methods and show a promising direction in and beyond research in Computational Linguistics and Natural Language Processing

Tackling Lower-Resource Language Challenges: A Comparative Study of Norwegian Pre-Trained BERT Models and Traditional Approaches for Football Article Paragraph Classification

In lower-resource language settings, domain-specific tasks such as paragraph classification of football articles present significant challenges. Traditional machine learning models face difficulties in effectively capturing the linguistic complexities inherent in the paragraphs, emphasizing the need for more advanced approaches to overcome these obstacles. This thesis investigates the potential of Norwegian pre-trained BERT (Bidirectional Encoder Representations from Transformers) models for paragraph classification tasks in the context of Norwegian football articles, a domain requiring a nuanced understanding of the Norwegian language. BERT is a powerful model architecture for language-specific processing tasks, which learns from the context of words in a sentence in both directions. Specifically, this thesis compares the performance of Transformer-based BERT models with traditional machine learning models in multi-class and multi-label classification tasks. An existing dataset of about 5,500 football article paragraphs is utilized to evaluate multi-class classification results. In addition, a newly annotated multi-label dataset of just over 2,000 samples is introduced for the multi-label classification assessment. The results reveal promising performance for the Norwegian pre-trained BERT models in both classification tasks, achieving an accuracy of ∼ 0.88 and a weighted-average F1-score of ∼ 0.87 in the multi-class classification task and accuracy of ∼ 0.40 and a weighted-average F1-score of ∼ 0.58 in the multi-label classification task, significantly outperforming the results of the traditional machine learning models. This study highlights the effectiveness of Transformer-based models in lower-resource language settings. It emphasizes the need for continued research and development in Natural Language Processing for underrepresented languages

Neural Vector Spaces for Unsupervised Information Retrieval

We propose the Neural Vector Space Model (NVSM), a method that learns representations of documents in an unsupervised manner for news article retrieval. In the NVSM paradigm, we learn low-dimensional representations of words and documents from scratch using gradient descent and rank documents according to their similarity with query representations that are composed from word representations. We show that NVSM performs better at document ranking than existing latent semantic vector space methods. The addition of NVSM to a mixture of lexical language models and a state-of-the-art baseline vector space model yields a statistically significant increase in retrieval effectiveness. Consequently, NVSM adds a complementary relevance signal. Next to semantic matching, we find that NVSM performs well in cases where lexical matching is needed. NVSM learns a notion of term specificity directly from the document collection without feature engineering. We also show that NVSM learns regularities related to Luhn significance. Finally, we give advice on how to deploy NVSM in situations where model selection (e.g., cross-validation) is infeasible. We find that an unsupervised ensemble of multiple models trained with different hyperparameter values performs better than a single cross-validated model. Therefore, NVSM can safely be used for ranking documents without supervised relevance judgments.Comment: TOIS 201

Automatic Genre Classification in Web Pages Applied to Web Comments

Automatic Web comment detection could significantly facilitate information retrieval systems, e.g., a focused Web crawler. In this paper, we propose a text genre classifier for Web text segments as intermediate step for Web comment detection in Web pages. Different feature types and classifiers are analyzed for this purpose. We compare the two-level approach to state-of-the-art techniques operating on the whole Web page text and show that accuracy can be improved significantly. Finally, we illustrate the applicability for information retrieval systems by evaluating our approach on Web pages achieved by a Web crawler