Readability metrics assess the ease with which human beings read and understand written texts. With the advent of machine learning techniques that allow computers to also analyse text, this provides an interesting opportunity to investigate whether readability metrics can be used to inform on the ease with which machines understand texts. To that end, the specific machine analysed in this paper uses word embeddings to conduct unsupervised sentiment analysis. This specification minimises the need for labelling and human intervention, thus relying heavily on the machine instead of the human. Across two different datasets, sentiment predictions are made using Google’s Word2Vec word embedding algorithm, and are evaluated to produce a dichotomous output variable per sentiment. This variable, representing whether a prediction is correct or not, is then used as the dependent variable in a logistic regression with 17 readability metrics as independent variables. The resulting model has high explanatory power and the effects of readability metrics on the results from the sentiment analysis are mostly statistically significant. However, metrics affect sentiment classification in the two datasets differently, indicating that the metrics are expressions of linguistic behaviour unique to the datasets. The implication of the findings is that readability metrics could be used directly in sentiment classification models to improve modelling accuracy. Moreover, the results also indicate that machines are able to pick up on information that human beings do not pick up on, for instance that certain words are associated with more positive or negative sentiments. / Läsbarhetsmått bedömer hur lätt eller svårt det är för människor att läsa och förstå skrivna texter. Eftersom nya maskininlärningstekniker har utvecklats kan datorer numera också analysera texter. Därför är en intressant infallsvinkel huruvida läsbarhetsmåtten också kan användas för att bedöma hur lätt eller svårt det är för maskiner att förstå texter. Mot denna bakgrund använder den specifika maskinen i denna uppsats ordinbäddningar i syfte att utföra oövervakad sentimentanalys. Således minimeras behovet av etikettering och mänsklig handpåläggning, vilket resulterar i en mer djupgående analys av maskinen istället för människan. I två olika dataset jämförs rätt svar mot sentimentförutsägelser från Googles ordinbäddnings-algoritm Word2Vec för att producera en binär utdatavariabel per sentiment. Denna variabel, som representerar om en förutsägelse är korrekt eller inte, används sedan som beroende variabel i en logistisk regression med 17 olika läsbarhetsmått som oberoende variabler. Den resulterande modellen har högt förklaringsvärde och effekterna av läsbarhetsmåtten på resultaten från sentimentanalysen är mestadels statistiskt signifikanta. Emellertid är effekten på klassificeringen beroende på dataset, vilket indikerar att läsbarhetsmåtten ger uttryck för olika lingvistiska beteenden som är unika till datamängderna. Implikationen av resultaten är att läsbarhetsmåtten kan användas direkt i modeller som utför sentimentanalys för att förbättra deras prediktionsförmåga. Dessutom indikerar resultaten också att maskiner kan plocka upp på information som människor inte kan, exempelvis att vissa ord är associerade med positiva eller negativa sentiment.

The influence of Information and Communication Technologies (ICTs) particularly internet technology has had a fundamental impact on the way government is administered, provides services and interacts with citizens. Currently, the use of social media is no longer limited to informal environments but is an increasingly important medium of communication between citizens and governments. The extensive and increasing use of social media will continue to generate huge amounts of user-generated content known as Big Social Data (BSD). The growing body of BSD presents innumerable opportunities as well as challenges for local government planning, management and delivery of public services to citizens. However, the governments have not yet utilised the potential of BSD for better understanding the public and gaining new insights from this new way of interactions. Some of the reasons are lacking in the mechanism and guidance to analyse this new format of data. Thus, the aim of this study is to evaluate how the body of BSD can be mined, analysed and applied in the context of local government in the UK. The objective is to develop a Big Social Data Analytics (BSDA) model that can be applied in the case of local government. Data generated from social media over a year were collected, collated and analysed using a range of social media analytics and network analysis tools and techniques. The final BSDA model was applied to a local council case to evaluate its impact in real practice. This study allows to better understand the methods of analysing the BSD in the public sector and extend the literature related to e-government, social media, and social network theory / Universiti Utara Malaysia

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We present results from a project on sentiment analysis of drama texts, more concretely the plays of Gotthold Ephraim Lessing. We conducted an annotation study to create a gold standard for a systematic evaluation. The gold standard consists of 200 speeches of Lessing’s plays and was manually annotated with sentiment information by five annotators. We use the gold stand-ard data to evaluate the performance of different German sentiment lexicons and processing configurations like lemmatization, the extension of lexicons with historical linguistic variants, and stop words elimination, to explore the influence of these parameters and to find best prac-tices for our domain of application. The best performing configuration accomplishes an accu-racy of 70%. We discuss the problems and challenges for sentiment analysis in this area and describe our next steps toward further research

Social media has recently become an essential component of our daily modern life, with platforms like Facebook, YouTube, and Twitter serving as popular venues for people to share their opinions on various topics, including sustainable food. The interest in consumer sentiments towards sustainable practices has increased particularly after Covid-2019. This study investigates the attitudes and needs of Swedish consumers regarding sustainable food consumption as reflected in their social media interactions using 4588 comments from Facebook and YouTube. The methodology used are sentiment analysis and topic modelling with VADER and Latent Dirichlet Allocation (LDA) respectively. The results reveal a generally strong positive attitude toward sustainable food. However, the study observes further a decline in positive sentiments over time, indicating changing consumer opinions. The primary topic identified is market challenges, such as high pricing. Furthermore, health concerns and environmental considerations are identified both as important factors influencing the choice of sustainable food. The findings highlight the necessity for policy interventions to enhance the affordability and accessibility of sustainable food, as well as the effective use of social media for raising consumer awareness.

本篇研究專注於首次公開發行公司上市櫃初始交易日之異常報酬與新聞情緒兩 者間之關係。本研究建立情緒字典以判別新聞之正負情緒,並過濾出與首次公開發 行有關之新聞,利用本研究建立之情緒字典以過濾出正負情緒之詞組。利用正負情 緒詞組數量計算出三種新聞情緒變數,並採實證研究方法檢測三種新聞情緒變數與 首次公開發行公司之初始交易日之異常報酬兩者間之關係。根據本研究之實證結果, 發現初始交易日之前的新聞能影響首次公開發行之異常報酬,而相關新聞之情緒語 調亦和異常報酬有關。此外,本研究亦檢測三種情緒變數和三種傳統變數之交乘項 對異常報酬之影響,發現公司規模大小與首日交易量與情緒變數之交乘項會對初始 交易日之異常報酬有影響。總言論之,本研究對新聞會影響首次公開發行初始交易 日之異常報酬提供了實證證據。 / This study focuses on the relation between IPOs’ abnormal returns on initial trading days and news sentiment. To identify the tone of news, sentiment dictionary was established for this study, and news regarding IPO firms was picked out to count positive and negative words and phrases based on the sentiment dictionary. Using quantities of positive and negative words and phrases, three news variables were adopted and calculated. And linear regression was utilized to investigate the relation between IPOs’ abnormal returns on initial trading days and news sentiment. According to empirical results, I find that news prior to the IPO’s initial trading day can affect IPOs’ abnormal returns. The number of negative words and phrases is negatively related to the abnormal returns; the tone of news is positively related to the abnormal returns. Furthermore, I also investigated whether interaction terms of news variables and three control variables are related to abnormal returns on IPOs’ initial trading days. I find that interaction terms of the natural logarithm of firm size and two news variables and interaction terms of the natural logarithm of first-day trading volume and two news variables are related to abnormal returns. Overall, there is evidence that news can influence IPOs’ abnormal returns on initial trading days.

There has been much hype in vocational and academic circles surrounding the emergence of web 2.0 or social media; however, relatively little work was dedicated to substantiating the actual concept of web 2.0. Many have dismissed it as not deserving of this new title, since the term web 2.0 assumes a certain interpretation of web history, including enough progress in certain direction to trigger a succession [i.e. web 1.0 → web 2.0]. Others provided arguments in support of this development, and there has been a considerable amount of enthusiasm in the literature. Much research has been busy evaluating current use of web 2.0, and analysis of the user generated content, but an objective and thorough assessment of what web 2.0 really stands for has been to a large extent overlooked. More recently the idea of collective intelligence facilitated via web 2.0, and its potential applications have raised interest with researchers, yet a more unified approach and work in the area of collective intelligence is needed. This thesis identifies and critically evaluates a wider context for the web 2.0 environment, and what caused it to emerge; providing a rich literature review on the topic, a review of existing taxonomies, a quantitative and qualitative evaluation of the concept itself, an investigation of the collective intelligence potential that emerges from application usage. Finally, a framework for harnessing collective intelligence in a more systematic manner is proposed. In addition to the presented results, novel methodologies are also introduced throughout this work. In order to provide interesting insight but also to illustrate analysis, a case study of the recent financial crisis is considered. Some interesting results relating to the crisis are revealed within user generated content data, and relevant issues are discussed where appropriate.

Topic modeling refers to the process of algorithmically sorting documents into categories based on some common relationship between the documents. This common relationship between the documents is considered the “topic” of the documents. Sentiment analysis refers to the process of algorithmically sorting a document into a positive or negative category depending whether this document expresses a positive or negative opinion on its respective topic. In this paper, I consider the open problem of document classification into a topic category, as well as a sentiment category. This has a direct application to the retail industry where companies may want to scour the web in order to find documents (blogs, Amazon reviews, etc.) which both speak about their product, and give an opinion on their product (positive, negative or neutral). My solution to this problem uses a Non-negative Matrix Factorization (NMF) technique in order to determine the topic classifications of a document set, and further factors the matrix in order to discover the sentiment behind this category of product.

A Mineração de Opiniões é um processo que tem por objetivo extrair as opiniões e suas polaridades de sentimentos expressas em textos em língua natural. Essa área de pesquisa tem ganhado destaque devido ao volume de opiniões que os usuários compartilham na Internet, como revisões em sites de e-commerce, rede sociais e tweets. A Mineração de Opiniões baseada em Aspectos é uma alternativa promissora para analisar a polaridade do sentimento em um maior nível de detalhes. Os métodos tradicionais para extração de aspectos e classificação de sentimentos exigem a participação de especialistas de domínio para criar léxicos ou definir regras de extração para diferentes idiomas e domínios. Além disso, tais métodos usualmente exploram algoritmos de aprendizado supervisionado, porém exigem um grande conjunto de dados rotulados para induzir um modelo de classificação. Os desafios desta tese de doutorado estão relacionados a como diminuir a necessidade de grande esforço humano tanto para rotular dados, quanto para tratar a dependência de domínio para as tarefas de extração de aspectos e classificação de sentimentos dos aspectos para Mineração de Opiniões. Para reduzir a necessidade de grande quantidade de exemplos rotulados foi proposta uma abordagem semissupervisionada, denominada por Aspect-based Sentiment Propagation on Heterogeneous Networks (ASPHN) em que são propostas representações de textos nas quais os atributos linguísticos, os aspectos candidatos e os rótulos de sentimentos são modelados por meio de redes heterogêneas. Para redução dos esforços para construir recursos específicos de domínio foi proposta uma abordagem baseada em aprendizado por transferência entre domínios denominada Cross-Domain Aspect Label Propagation through Heterogeneous Networks (CD-ALPHN) que utiliza dados rotulados de outros domínios para suportar tarefas de aprendizado em domínios sem dados rotulados. Nessa abordagem são propostos uma representação em uma rede heterogênea e um método de propagação de rótulos. Os vértices da rede são os aspectos rotulados do domínio de origem, os atributos linguísticos e os candidatos a aspectos do domínio alvo. Além disso, foram analisados métodos de extração de aspectos e propostas algumas variações para considerar cenários nãosupervisionados e independentes de domínio. As soluções propostas nesta tese de doutorado foram avaliadas e comparadas as do estado-da-arte utilizando coleções de revisões de diferentes produtos e serviços. Os resultados obtidos nas avaliações experimentais são competitivos e demonstram que as soluções propostas são promissoras. / Opinion Mining is a process that aims to extract opinions and their sentiment polarities expressed in natural language texts. This area of research has been in the highlight because of the volume of opinions that users share on the available visualization means on the Internet (reviews on e-commerce sites, social networks, tweets, others). Aspect-based Opinion Mining is a promising alternative for analyzing the sentiment polarity on a high level of detail. The traditional methods for aspect extraction and sentiment classification require the participation of domain experts to create lexicons or define extraction rules for different languages and domains. In addition, such methods usually exploit supervised machine learning algorithms, but require a large set of labeled data to induce a classification model. The challenges of this doctoral thesis are related on to how to reduce the need for great human effort both: (i) to label data; and (ii) to treat domain dependency for the tasks of aspect extraction and aspect sentiment classification for Opinion Mining. In order to reduce the need for a large number of labeled examples, a semi-supervised approach was proposed, called Aspect-based Sentiment Propagation on Heterogeneous Networks (ASPHN). In this approach, text representations are proposed in which linguistic attributes, candidate aspects and sentiment labels are modeled by heterogeneous networks. Also, a cross-domain learning approach called Cross-Domain Aspect Label Propagation through Heterogeneous Networks (CD-ALPHN) is proposed in order to reduce efforts to build domain-specific resources, This approach uses labeled data from other domains to support learning tasks in domains without labeled data. A representation in a heterogeneous network and a label propagation method are proposed in this cross-domain learning approach. The vertices of the network are the labeled aspects of the source domain, the linguistic attributes, and the candidate aspects of the target domain. In addition, aspect extraction methods were analyzed and some variations were proposed to consider unsupervised and domain independent scenarios. The solutions proposed in this doctoral thesis were evaluated and compared to the state-of-the-art solutions using collections of different product and service reviews. The results obtained in the experimental evaluations are competitive and demonstrate that the proposed solutions are promising.

Sistemas de recomendação surgiram da necessidade de selecionar e apresentar conteúdo relevante a usuários de acordo com suas preferências. Dentre os diversos métodos existentes, aqueles baseados em conteúdo faz em uso exclusivo da informação inerente aos itens. Estas informações podem ser criadas a partir de técnicas de indexação automática e manual. Enquanto que as abordagens automáticas necessitam de maiores recursos computacionais e são limitadas á tarefa específica que desempenham, os métodos manuais são caros e propensos a erros. Por outro lado, com a expansão da Web e a possibilidade de usuários comuns criarem novos conteúdos e anotações sobre diferentes itens e produtos, uma alternativa é obter esses metadados criados colaborativamente pelos próprios usuários. Entretanto, essas informações, em especial revisões e comentários, podem conter ruídos, além de estarem em uma forma desestruturada. Deste modo, este trabalho1 tem como objetivo desenvolver métodos de construção de representações de itens baseados em descrições colaborativas para um sistema de recomendação. Objetiva-se analisar o impacto que diferentes técnicas de extração de características, aliadas à análise de sentimento, causam na precisão da geração de sugestões, avaliando-se os resultados em dois cenários de recomendação: predição de notas e geração de ranques. Dentre as técnicas analisadas, observa-se que a melhor apresenta um ganho no poder descritivo dos itens, ocasionando uma melhora no sistema de recomendação. / Recommender systems arose from the need to select and present relevant content to users according to their preferences. Among several existent methods, those based on content make exclusive use of information inherent to the items. This information can be created through automatic and manual indexing techniques. While automa-tic approaches require greater computing resources and are limited to the specific task they perform, manual methods are expensive and prone to errors. On the other hand, with the expansion of theWeb and the possibility of common users to create new content and descriptions about different items and products, an alternative is to get these metadata created collaboratively by the users. However, this information, especially reviews and comments, may contain noise, be- sides being in a unstructured fashion. Thus, this study aims to develop methods for the construction of items representations based on collaborative descriptions for a recommender system. This study aims to analyze the impact that different feature extraction techniques, combined with sentiment analysis, caused in the accuracy of the generated suggestions, evaluating the results in both recommendations cenarios: rating prediction and ranking generation. Among the analyzed techniques, it is observed that the best is able to describe items in a more effcient manner, resulting in an improvement in the recommendation system.