Consumer's opinions and sentiments on products can reflect the performance of products in general or in various aspects. Analyzing these data is becoming feasible, considering the availability of immense data and the power of natural language processing. However, retailers have not taken full advantage of online comments. This work is dedicated to a solution for automatically analyzing and summarizing these valuable data at both product and category levels. In this research, a system was developed to retrieve and analyze extensive data from public online resources. A parallel framework was created to make this system extensible and efficient. In this framework, a star topological network was adopted in which each computing unit was assigned to retrieve a fraction of data and to assess sentiment. Finally, the preprocessed data were collected and summarized by the central machine which generates the final result that can be rendered through a web interface. The system was designed to have sound performance, robustness, manageability, extensibility, and accuracy.

Aspect-based sentiment analysis is the field of study which extracts and interpret the sentiment, usually classified as positive or negative, towards some target or aspect in an opinionated text. This doctoral dissertation details an empirical study of techniques and methods for aspect extraction in aspect-based sentiment analysis with the focus on Portuguese. Three different approaches were explored: frequency-based, relation-based and machine learning. In each one, this work shows a comparative study between a Portuguese and an English corpora and the differences found in applying the approaches. In addition, richer linguistic knowledge is also explored by using syntatic dependencies and semantic roles, leading to better results. This work lead to the establishment of new benchmarks for the aspect extraction in Portuguese. / A análise do sentimento orientada a aspectos é o campo de estudo que extrai e interpreta o sentimento, geralmente classificado como positivo ou negativo, em direção a algum alvo ou aspecto em um texto de opinião. Esta tese de doutorado detalha um estudo empírico de técnicas e métodos para extração de aspectos em análises de sentimentos baseadas em aspectos com foco na língua Portuguesa. Foram exploradas três diferentes abordagens: métodos baseados na frequências, métodos baseados na relação e métodos de aprendizagem de máquina. Em cada abordagem, este trabalho mostra um estudo comparativo entre um córpus para o Português e outro para o Inglês e as diferenças encontradas na aplicação destas abordagens. Além disso, o conhecimento linguístico mais rico também é explorado pelo uso de dependências sintáticas e papéis semânticos, levando a melhores resultados. Este trabalho resultou no estabelecimento de novos padrões de avaliação para a extração de aspectos em Português.

Aspect-based sentiment analysis is the field of study which extracts and interpret the sentiment, usually classified as positive or negative, towards some target or aspect in an opinionated text. This doctoral dissertation details an empirical study of techniques and methods for aspect extraction in aspect-based sentiment analysis with the focus on Portuguese. Three different approaches were explored: frequency-based, relation-based and machine learning. In each one, this work shows a comparative study between a Portuguese and an English corpora and the differences found in applying the approaches. In addition, richer linguistic knowledge is also explored by using syntatic dependencies and semantic roles, leading to better results. This work lead to the establishment of new benchmarks for the aspect extraction in Portuguese. / A análise do sentimento orientada a aspectos é o campo de estudo que extrai e interpreta o sentimento, geralmente classificado como positivo ou negativo, em direção a algum alvo ou aspecto em um texto de opinião. Esta tese de doutorado detalha um estudo empírico de técnicas e métodos para extração de aspectos em análises de sentimentos baseadas em aspectos com foco na língua Portuguesa. Foram exploradas três diferentes abordagens: métodos baseados na frequências, métodos baseados na relação e métodos de aprendizagem de máquina. Em cada abordagem, este trabalho mostra um estudo comparativo entre um córpus para o Português e outro para o Inglês e as diferenças encontradas na aplicação destas abordagens. Além disso, o conhecimento linguístico mais rico também é explorado pelo uso de dependências sintáticas e papéis semânticos, levando a melhores resultados. Este trabalho resultou no estabelecimento de novos padrões de avaliação para a extração de aspectos em Português.

<p>When people write a review about a business, they write and rate it based on their personal experience of the business. Sentiment analysis is a natural language processing technique that determines the sentiment of text, including reviews. However, unlike computers, the personal experience of humans emphasizes their preferences and observations that they deem important while ignoring other components that may not be as important to them personally. Traditional sentiment analysis does not consider such preferences. To utilize these human preferences in sentiment analysis, this paper explores various methods of weighting aspects in an attempt to improve sentiment analysis accuracy. Two types of methods are considered. The first method applies human preference by assigning weights to aspects in calculating overall sentiment analysis. The second method uses the results of the first method to improve the accuracy of traditional supervised sentiment analysis. The results show that the methods have high accuracy when people have strong opinions, but the weights of the aspects do not significantly improve the accuracy.</p>

Video games are experience-based products and user satisfaction is key for their popularity. To design for as strong an experience as possible, game developers incorporate evaluation methods that help to discover their users’ expectations and needs. Despite such efforts, problems still occur with the game design that lower the user experience. To counter these problems, the evaluation methods should be investigated and improved. To address this need, I have explored various design tools and user experience theories. Applying these in a game evaluation context, I have analyzed user-created game reviews and conducted longitudinal user interview- and game diary studies in connection to playing a newly released game, in other words different methods to take advantage of users' expectations, opinions, attitudes and experiences. One result of the analysis of the obtained data is a set of “slogans” that illustrate how and why users lose interest in a game. A second result is a method for extracting user attitudes from pre-produced user reviews and how this can be used in game development. Thirdly, I introduce an alternative model, aimed at game user experience development, the Playsona. The Playsona is a lightweight tool that introduces a variant of the Persona-method, specifically for video game design. / <p>At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 4: Manuscript.</p>

Identifying differing opinions on a given topic as expressed by multiple people (as in a set of written reviews for a given product, for example) presents challenges. Opinions about a particular subject are often nuanced: a person may have both negative and positive opinions about different aspects of the subject of interest, and these aspect-specific opinions can be independent of the overall opinion on the subject. Being able to identify, collect, and count these nuanced opinions in a large set of data offers more insight into the strengths and weaknesses of competing products and services than does aggregating the overall ratings of such products and services. I make two useful and useable contributions in working with opinionated text. First, I present my implementation of a semi-supervised co-training machine classification method for identifying both product aspects (features of products) and sentiments expressed about such aspects. It offers better precision than fully-supervised methods while requiring much less text to be manually tagged (a time-consuming process). This algorithm can also be run in a fully supervised manner when more data is available. Second, I apply this co-training approach to reviews of restaurants and various electronic devices; such text contains both factual statements and opinions about features/aspects of products. The algorithm automatically identifies the product aspects and the words that indicate aspect-specific opinion polarity, while largely avoiding the problem of misclassifying the products themselves as inherently positive or negative. This method performs well compared to other approaches. When run on a set of reviews of five technology products collected from Amazon, the system performed with some demonstrated competence (with an average precision of 0.83) at the difficult task of simultaneously identifying aspects and sentiments, though comparison to contemporaries' simpler rules-based approaches was difficult. When run on a set of opinionated sentences about laptops and restaurants that formed the basis of a shared challenge in the SemEval-2014 Task 4 competition, it was able to classify the sentiments expressed about aspects of laptops better than any team that competed in the task (achieving 0.72 accuracy). It was above the mean in its ability to identify the aspects of restaurants about which people expressed opinions, even when co-training using only half of the labelled training data at the outset. While the SemEval-2014 aspect-based sentiment extraction task considered only separately the tasks of identifying product aspects and determining their polarities, I take an extra step and evaluate sentences as a whole, inferring aspects and the aspect-specific sentiments expressed simultaneously, a more difficult task that seems more applicable to real-world tasks. I present first results of this sentence-level task. The algorithm uses both lexical and syntactic information in a manner that is shown to be able to handle new words that it has never before seen. It offers some demonstrated ability to adapt to new subject domains for which it has no training data. The system is characterizable by very high precision and weak-to-average recall and it estimates its own confidence in its predictions; this characteristic should make the algorithm suitable for use on its own or for combination in a confidence-based voting ensemble. The software created for and described in the course of this dissertation is made available online.

Many companies strive to make data-driven decisions. To achieve this, they need to explore new tools for Business Intelligence. The aim of this study was to examine the performance and usability of aspect-based sentiment analysis as a tool for Business Intelligence in E-commerce. The study was conducted in collaboration with Ellos Group AB which supplied anonymous customer feedback data. The implementation consists of two parts, aspect extraction and sentiment classification. The f irst part, aspect extraction, was implemented using dependency parsing and various aspect grouping techniques. The second part, sentiment classification, was implemented using the language model KB-BERT, a Swedish version of the BERT model. The method for aspect extraction achieved a satisfactory precision of 79,5% but only a recall of 27,2%. Moreover, the result for sentiment classification was unsatisfactory with an accuracy of 68,2%. Although the results underperform expectations, we conclude that aspect-based sentiment analysis in general is a great tool for Business Intelligence. Both as a means of generating customer insights from previously unused data and to increase productivity. However, it should only be used as a supportive tool and not to replace existing processes for decision-making. / Många företag strävar efter att fatta datadrivna beslut. För att åstadkomma detta behöver de utforska nya metoder för Business Intelligence. Syftet med denna studie var att undersöka prestandan och användbarheten av aspektbaserad sentimentanalys som ett verktyg för Business Intelligence inom e-handeln. Studien genomfördes i samarbete med Ellos Group AB som tillhandahöll data bestående av anonym kundfeedback. Implementationen består av två delar, aspektextraktion och sentimentklassificering. Aspektextraktion implementerades med hjälp av dependensparsning och olika aspektgrupperingstekniker. Sentimentklassificering implementerades med hjälp av språkmodellen KB-BERT, en svensk version av BERT. Metoden för aspektextraktion uppnådde en tillfredsställande precision på 79,5% men endast en recall på 27,2%. Resultatet för sentimentklassificering var otillfredsställande med en accuracy på 68,2%. Även om resultaten underpresterar förväntningarna drar vi slutsatsen att aspektbaserad sentimentanalys i allmänhet är ett bra verktyg för Business Intelligence. Både som ett sätt att generera kundinsikter från tidigare oanvända data och som ett sätt att öka produktiviteten. Det bör dock endast användas som ett stödjande verktyg och inte ersätta befintliga processer för beslutsfattande.