Questioning the news about economic growth : sparse forecasting using thousands of news-based sentiment values

The modern calculation of textual sentiment involves a myriad of choices as to the actual calibration. We introduce a general sentiment engineering framework that optimizes the design for forecasting purposes. It includes the use of the elastic net for sparse data-driven selection and the weighting of thousands of sentiment values. These values are obtained by pooling the textual sentiment values across publication venues, article topics, sentiment construction methods, and time. We apply the framework to the investigation of the value added by textual analysis-based sentiment indices for forecasting economic growth in the US. We find that the additional use of optimized news-based sentiment values yields significant accuracy gains for forecasting the nine-month and annual growth rates of the US industrial production, compared to the use of high-dimensional forecasting techniques based on only economic and financial indicators. (C) 2018 The Author(s). Published by Elsevier B.V. on behalf of International Institute of Forecasters

Understanding destination brand experience through data mining and machine learning

This research formalises a new methodology to measure and analyse Destination Brand Experience, improving upon traditional approaches by offering greater objectivity and rigour. Adopting a case study approach, five distinct and complementary types of analysis have been conducted: comprehensive sentiment analysis and topic modelling, an analysis using multiple thesauri, statistical analyses for hypothesis testing, and machine learning for classification. The methodological innovation, through the construction of thesauri, has enabled the measurement of sensory, affective, intellectual, and behavioural dimensions in unique and emblematic attractions, experiences, and transportation within a tourist destination, based on visitor reviews. This new approach allows tourism professionals and destination managers to identify areas for improvement and develop strategies to enhance tourist satisfaction. The findings suggest that there are significant differences in the relationships between specific dimensions and that gender and culture moderate or impact these relationships.Funding for open Access charge: Universidad de Málaga / CBUA. This study was supported by the European Regional Development Fund Operational Programme of Andalusia 2014–2020, through the Andalusian Research, Development and Innovation Plan (Plan Andaluz de Investigación, Desarrollo e Innovación) PAIDI 2020 (Grant: P20_00457), and by the Spanish Ministry of Education, Culture and Sport (Ministerio de Educación, Cultura y Deporte del Gobierno de España) (Grant: FPU20/00235)

Sentiment Analysis of Text Guided by Semantics and Structure

As moods and opinions play a pivotal role in various business and economic processes, keeping track of one's stakeholders' sentiment can be of crucial importance to decision makers. Today's abundance of user-generated content allows for the automated monitoring of the opinions of many stakeholders, like consumers. One challenge for such automated sentiment analysis systems is to identify whether pieces of natural language text are positive or negative. Typical methods of identifying this polarity involve low-level linguistic analysis. Existing systems predominantly use morphological, lexical, and syntactic cues for polarity, like a text's words, their parts-of-speech, and negation or amplification of the conveyed sentiment. This dissertation argues that the polarity of text can be analysed more accurately when additionally accounting for semantics and structure. Polarity classification performance can benefit from exploiting the interactions that emoticons have on a semantic level with words – emoticons can express, stress, or disambiguate sentiment. Furthermore, semantic relations between and within languages can help identify meaningful cues for sentiment in multi-lingual polarity classification. An even better understanding of a text's conveyed sentiment can be obtained by guiding automated sentiment analysis by the rhetorical structure of the text, or at least of its most sentiment-carrying segments. Thus, the sentiment in, e.g., conclusions can be treated differently from the sentiment in background information. The findings of this dissertation suggest that the polarity of natural language text should not be determined solely based on what is said. Instead, one should account for how this message is conveyed as well

FinXABSA: Explainable Finance through Aspect-Based Sentiment Analysis

This paper presents a novel approach for explainability in financial analysis by deriving financially-explainable statistical relationships through aspect-based sentiment analysis, Pearson correlation, Granger causality & uncertainty coefficient. The proposed methodology involves constructing an aspect list from financial literature and applying aspect-based sentiment analysis on social media text to compute sentiment scores for each aspect. Pearson correlation is then applied to uncover financially explainable relationships between aspect sentiment scores and stock prices. Findings for derived relationships are made robust by applying Granger causality to determine the forecasting ability of each aspect sentiment score for stock prices. Finally, an added layer of interpretability is added by evaluating uncertainty coefficient scores between aspect sentiment scores and stock prices. This allows us to determine the aspects whose sentiment scores are most statistically significant for stock prices. Relative to other methods, our approach provides a more informative and accurate understanding of the relationship between sentiment analysis and stock prices. Specifically, this methodology enables an interpretation of the statistical relationship between aspect-based sentiment scores and stock prices, which offers explainability to AI-driven financial decision-making

Domain adaptation in Natural Language Processing

Domain adaptation has received much attention in the past decade. It has been shown that domain knowledge is paramount for building successful Natural Language Processing (NLP) applications. To investigate the domain adaptation problem, we conduct several experiments from different perspectives. First, we automatically adapt sentiment dictionaries for predicting the financial outcomes “excess return” and “volatility”. In these experiments, we compare manual adaptation of the domain-general dictionary with automatic adaptation, and manual adaptation with a combination consisting of first manual, then automatic adaptation. We demonstrate that automatic adaptation performs better than manual adaptation, namely the automatically adapted sentiment dictionary outperforms the previous state of the art in predicting excess return and volatility. Furthermore, we perform qualitative and quantitative analyses finding that annotation based on an expert’s a priori belief about a word’s meaning is error-prone – the meaning of a word can only be recognized in the context that it appears in. Second, we develop the temporal transfer learning approach to account for the language change in social media. The language of social media is changing rapidly – new words appear in the vocabulary, and new trends are constantly emerging. Temporal transfer-learning allows us to model these temporal dynamics in the document collection. We show that this method significantly improves the prediction of movie sales from discussions on social media forums. In particular, we illustrate the success of parameter transfer, the importance of textual information for financial prediction, and show that temporal transfer learning can capture temporal trends in the data by focusing on those features that are relevant in a particular time step, i.e., we obtain more robust models preventing overfitting. Third, we compare the performance of various domain adaptation models in low-resource settings, i.e., when there is a lack of large amounts of high-quality training data. This is an important issue in computational linguistics since the success of NLP applications primarily depends on the availability of training data. In real-world scenarios, the data is often too restricted and specialized. In our experiments, we evaluate different domain adaptation methods under these assumptions and find the most appropriate techniques for such a low-data problem. Furthermore, we discuss the conditions under which one approach substantially outperforms the other. Finally, we summarize our work on domain adaptation in NLP and discuss possible future work topics.Die Domänenanpassung hat in den letzten zehn Jahren viel Aufmerksamkeit erhalten. Es hat sich gezeigt, dass das Domänenwissen für die Erstellung erfolgreicher NLP-Anwendungen (Natural Language Processing) von größter Bedeutung ist. Um das Problem der Domänenanpassung zu untersuchen, führen wir mehrere Experimente aus verschiedenen Perspektiven durch. Erstens passen wir Sentimentlexika automatisch an, um die Überschussrendite und die Volatilität der Finanzergebnisse besser vorherzusagen. In diesen Experimenten vergleichen wir die manuelle Anpassung des allgemeinen Lexikons mit der automatischen Anpassung und die manuelle Anpassung mit einer Kombination aus erst manueller und dann automatischer Anpassung. Wir zeigen, dass die automatische Anpassung eine bessere Leistung erbringt als die manuelle Anpassung: das automatisch angepasste Sentimentlexikon übertrifft den bisherigen Stand der Technik bei der Vorhersage der Überschussrendite und der Volatilität. Darüber hinaus führen wir eine qualitative und quantitative Analyse durch und stellen fest, dass Annotationen, die auf der a priori Überzeugung eines Experten über die Bedeutung eines Wortes basieren, fehlerhaft sein können. Die Bedeutung eines Wortes kann nur in dem Kontext erkannt werden, in dem es erscheint. Zweitens entwickeln wir den Ansatz, den wir Temporal Transfer Learning benennen, um den Sprachwechsel in sozialen Medien zu berücksichtigen. Die Sprache der sozialen Medien ändert sich rasant – neue Wörter erscheinen im Vokabular und es entstehen ständig neue Trends. Temporal Transfer Learning ermöglicht es, diese zeitliche Dynamik in der Dokumentensammlung zu modellieren. Wir zeigen, dass diese Methode die Vorhersage von Filmverkäufen aus Diskussionen in Social-Media-Foren erheblich verbessert. In unseren Experimenten zeigen wir (i) den Erfolg der Parameterübertragung, (ii) die Bedeutung von Textinformationen für die finanzielle Vorhersage und (iii) dass Temporal Transfer Learning zeitliche Trends in den Daten erfassen kann, indem es sich auf die Merkmale konzentriert, die in einem bestimmten Zeitschritt relevant sind, d. h. wir erhalten robustere Modelle, die eine Überanpassung verhindern. Drittens vergleichen wir die Leistung verschiedener Domänenanpassungsmodelle in ressourcenarmen Umgebungen, d. h. wenn große Mengen an hochwertigen Trainingsdaten fehlen. Das ist ein wichtiges Thema in der Computerlinguistik, da der Erfolg der NLP-Anwendungen stark von der Verfügbarkeit von Trainingsdaten abhängt. In realen Szenarien sind die Daten oft zu eingeschränkt und spezialisiert. In unseren Experimenten evaluieren wir verschiedene Domänenanpassungsmethoden unter diesen Annahmen und finden die am besten geeigneten Techniken dafür. Darüber hinaus diskutieren wir die Bedingungen, unter denen ein Ansatz den anderen deutlich übertrifft. Abschließend fassen wir unsere Arbeit zur Domänenanpassung in NLP zusammen und diskutieren mögliche zukünftige Arbeitsthemen

From Word to Sense Embeddings: A Survey on Vector Representations of Meaning

Over the past years, distributed semantic representations have proved to be effective and flexible keepers of prior knowledge to be integrated into downstream applications. This survey focuses on the representation of meaning. We start from the theoretical background behind word vector space models and highlight one of their major limitations: the meaning conflation deficiency, which arises from representing a word with all its possible meanings as a single vector. Then, we explain how this deficiency can be addressed through a transition from the word level to the more fine-grained level of word senses (in its broader acceptation) as a method for modelling unambiguous lexical meaning. We present a comprehensive overview of the wide range of techniques in the two main branches of sense representation, i.e., unsupervised and knowledge-based. Finally, this survey covers the main evaluation procedures and applications for this type of representation, and provides an analysis of four of its important aspects: interpretability, sense granularity, adaptability to different domains and compositionality.Comment: 46 pages, 8 figures. Published in Journal of Artificial Intelligence Researc