Sentiment Analysis for Words and Fiction Characters From The Perspective of Computational (Neuro-)Poetics

Two computational studies provide different sentiment analyses for text segments (e.g., ‘fearful’ passages) and figures (e.g., ‘Voldemort’) from the Harry Potter books (Rowling, 1997 - 2007) based on a novel simple tool called SentiArt. The tool uses vector space models together with theory-guided, empirically validated label lists to compute the valence of each word in a text by locating its position in a 2d emotion potential space spanned by the > 2 million words of the vector space model. After testing the tool’s accuracy with empirical data from a neurocognitive study, it was applied to compute emotional figure profiles and personality figure profiles (inspired by the so-called ‚big five’ personality theory) for main characters from the book series. The results of comparative analyses using different machine-learning classifiers (e.g., AdaBoost, Neural Net) show that SentiArt performs very well in predicting the emotion potential of text passages. It also produces plausible predictions regarding the emotional and personality profile of fiction characters which are correctly identified on the basis of eight character features, and it achieves a good cross-validation accuracy in classifying 100 figures into ‘good’ vs. ‘bad’ ones. The results are discussed with regard to potential applications of SentiArt in digital literary, applied reading and neurocognitive poetics studies such as the quantification of the hybrid hero potential of figures

Econometrics meets sentiment : an overview of methodology and applications

The advent of massive amounts of textual, audio, and visual data has spurred the development of econometric methodology to transform qualitative sentiment data into quantitative sentiment variables, and to use those variables in an econometric analysis of the relationships between sentiment and other variables. We survey this emerging research field and refer to it as sentometrics, which is a portmanteau of sentiment and econometrics. We provide a synthesis of the relevant methodological approaches, illustrate with empirical results, and discuss useful software

Role of sentiment classification in sentiment analysis: a survey

Through a survey of literature, the role of sentiment classification in sentiment analysis has been reviewed. The review identifies the research challenges involved in tackling sentiment classification. A total of 68 articles during 2015 – 2017 have been reviewed on six dimensions viz., sentiment classification, feature extraction, cross-lingual sentiment classification, cross-domain sentiment classification, lexica and corpora creation and multi-label sentiment classification. This study discusses the prominence and effects of sentiment classification in sentiment evaluation and a lot of further research needs to be done for productive results

Six papers on computational methods for the analysis of structured and unstructured data in the economic domain

This work investigates the application of computational methods for structured and unstructured data. The domains of application are two closely connected fields with the common goal of promoting the stability of the financial system: systemic risk and bank supervision. The work explores different families of models and applies them to different tasks: graphical Gaussian network models to address bank interconnectivity, topic models to monitor bank news and deep learning for text classification. New applications and variants of these models are investigated posing a particular attention on the combined use of textual and structured data. In the penultimate chapter is introduced a sentiment polarity classification tool in Italian, based on deep learning, to simplify future researches relying on sentiment analysis. The different models have proven useful for leveraging numerical (structured) and textual (unstructured) data. Graphical Gaussian Models and Topic models have been adopted for inspection and descriptive tasks while deep learning has been applied more for predictive (classification) problems. Overall, the integration of textual (unstructured) and numerical (structured) information has proven useful for systemic risk and bank supervision related analysis. The integration of textual data with numerical data in fact, has brought either to higher predictive performances or enhanced capability of explaining phenomena and correlating them to other events.This work investigates the application of computational methods for structured and unstructured data. The domains of application are two closely connected fields with the common goal of promoting the stability of the financial system: systemic risk and bank supervision. The work explores different families of models and applies them to different tasks: graphical Gaussian network models to address bank interconnectivity, topic models to monitor bank news and deep learning for text classification. New applications and variants of these models are investigated posing a particular attention on the combined use of textual and structured data. In the penultimate chapter is introduced a sentiment polarity classification tool in Italian, based on deep learning, to simplify future researches relying on sentiment analysis. The different models have proven useful for leveraging numerical (structured) and textual (unstructured) data. Graphical Gaussian Models and Topic models have been adopted for inspection and descriptive tasks while deep learning has been applied more for predictive (classification) problems. Overall, the integration of textual (unstructured) and numerical (structured) information has proven useful for systemic risk and bank supervision related analysis. The integration of textual data with numerical data in fact, has brought either to higher predictive performances or enhanced capability of explaining phenomena and correlating them to other events

Validating a sentiment dictionary for German political language - a workbench note

Automated sentiment scoring offers relevant empirical information for many political science applications. However, apart from English language resources, validated dictionaries are rare. This note introduces a German sentiment dictionary and assesses its performance against human intuition in parliamentary speeches, party manifestos, and media coverage. The tool published with this note is indeed able to discriminate positive and negative political language. But the validation exercises indicate that positive language is easier to detect than negative language, while the scores are numerically biased to zero. This warrants caution when interpreting sentiment scores as interval or even ratio scales in applied research

Enhanced Topic-Based Modeling for Twitter Sentiment Analysis

abstract: In this thesis multiple approaches are explored to enhance sentiment analysis of tweets. A standard sentiment analysis model with customized features is first trained and tested to establish a baseline. This is compared to an existing topic based mixture model and a new proposed topic based vector model both of which use Latent Dirichlet Allocation (LDA) for topic modeling. The proposed topic based vector model has higher accuracies in terms of averaged F scores than the other two models.Dissertation/ThesisMasters Thesis Computer Science 201

Literature review - Twitter as A Tool of Market Intelligence for Businesses: Sentiment analysis approach

Purpose As an emerging technology, sentiment analysis of Twitter has aroused interest in the field of business research. The thesis has three primary objectives. The first objective is to identify how businesses could utilize sentiment analysis of Twitter in their market intelligence functions. The second is to determine how sentiment analysis of Twitter compares to more traditional methods of market intelligence. Thirdly, this thesis aspires to bring technology-oriented discipline easier to approach for business researchers. Methodology The research method of this thesis is a literature review. The thesis revises prior published and peer-reviewed articles with a focus on sentiment analysis of Twitter and its applications to market intelligence. Findings There are three significant findings in this thesis. 1. Companies have utilized sentiment analysis for various purposes of market intelligence with encouraging results. 2. Sentiment analysis of Twitter has a variety of similarities with traditional market intelligence methods. In the future, it will be an auspicious technique for market intelligence as its accuracy is improved, and companies utilize it more frequently for practical purposes. 3. Even though Twitter sentiment analysis has raised plenty of interest, there is no clear research field within the business, and more specifically, market intelligence related literature. Future research For future research, this thesis provides a review of the possibilities and uses of Twitter sentiment analysis in the context of market intelligence. Its focus is to support especially business research. Reviewed literature illustrates that there are a large number of research avenues to be addressed in the future. The first objective for future research is to implement a more precise research field of business research. The second objective is to conduct more comparative studies between Twitter sentiment analysis and qualitative business research methods. Another intriguing research topic is Twitter sentiment analysis in the context of Finnish companies.Tutkielman tiivistelmätiedoissa näkyvä hyväksymisvuosi on 2019.The year of approval showing in the abstract of the thesis is 2019