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In this study we gather customer reviews from Prisjakt, a Swedish price comparison site, with the goal to study the relationship between review and rating, known as sentiment analysis. The purpose of the study is to evaluate three different supervised machine learning models on a fine-grained dependent variable representing the review rating. For classification, a binary and multinomial model is used with the one-versus-one strategy implemented in the Support Vector Machine, with a linear kernel, evaluated with F1, accuracy, precision and recall scores. We use Support Vector Regression by approximating the fine-grained variable as continuous, evaluated using MSE. Furthermore, three models are evaluated on a balanced and unbalanced dataset in order to investigate the effects of class imbalance. The results show that the SVR performs better on unbalanced fine-grained data, with the best fine-grained model reaching a MSE 4.12, compared to the balanced SVR (6.84). The binary SVM model reaches an accuracy of 86.37% and weighted F1 macro of 86.36% on the unbalanced data, while the balanced binary SVM model reaches approximately 80% for both measures. The multinomial model shows the worst performance due to the inability to handle class imbalance, despite the implementation of class weights. Furthermore, results from feature engineering shows that SVR benefits marginally from certain regex conversions, and tf-idf weighting shows better performance on the balanced sets compared to the unbalanced sets.

Les définitions des mots « sentiment », « opinion » et « émotion » sont toujours très vagues comme l’atteste aussi le dictionnaire qui semble expliquer un mot en utilisant le deux autres. Tout le monde est affecté par les opinions : les entreprises pour vendre les produits, les gens pour les acheter et, plus en général, pour prendre des décisions, les chercheurs en intelligence artificielle pour comprendre la nature de l’être humain. Aujourd’hui on a une quantité d’information disponible jamais vue avant, mais qui résulte peu accessible. Les mégadonnées (en anglais « big data ») ne sont pas organisées, surtout pour certaines langues – dont la difficulté à les exploiter. La recherche française souffre d’une manque de ressources « prêt-à-porter » pour conduire des tests. Cette thèse a l’objectif d’explorer la nature des sentiments et des émotions, dans le cadre du Traitement Automatique du Langage et des Corpus. Les contributions de cette thèse sont plusieurs : création de nouvelles ressources pour l’analyse du sentiment et de l’émotion, emploi et comparaison de plusieurs techniques d’apprentissage automatique, et plus important, l’étude du problème sous différents points de vue : classification des commentaires en ligne en polarité (positive et négative), Aspect-Based Sentiment Analysis des caractéristiques du produit recensé. Enfin, un étude psycholinguistique, supporté par des approches lexicales et d’apprentissage automatique, sur le rapport entre qui juge et l’objet jugé. / "Sentiment", "opinion" and "emotion" are words really vaguely defined; not even the dictionary seems to be of any help, being it the first to define each of the three by using the remaining two. And yet, the civilised world is heavily affected by opinions: companies need them to understand how to sell their products; people use them to buy the most fitting product and, more generally, to weigh their decisions; researchers exploit them in Artificial Intelligence studies to understand the nature of the human being. Today we can count on a humongous amount of available information, though it’s hard to use it. In fact, the so-called “Big data” are not always structured – especially for certain languages. French research suffers from a lack of readily available resources for tests. In the context of Natural Language Processing, this thesis aims to explore the nature of sentiment and emotion. Some of our contributions to the NLP research community are: creation of new resources for sentiment and emotion analysis, tests and comparisons of several machine learning methods to study the problem from different points of view - classification of online reviews using sentiment polarity, classification of product characteristics using Aspect- Based Sentiment Analysis. Finally, a psycholinguistic study - supported by a machine learning and lexical approaches – on the relation between who judges, the reviewer, and the object that has been judged, the product.

The aim of this thesis is to study and identify time periods of high activity in commodity and stock market sentiment based on a data mining approach. The method is to develop tools to extract relevant information from web searches and Twitter feeds based on the tally of certain keywords and their combinations at regular intervals. Periods of high activity are identified by a measure of complexity developed for analysis of living systems. Experiments were conducted to see if the measure of activity could be applied as a predictor of changes in stock market and commodity prices.

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This master thesis in human geography explores how neighborhoods are represented and perceived on the popular social media platform Instagram. By analyzing user-generated content, both visually and textually, this study aims to uncover the predominant themes, characteristics, and subjective perspectives associated with neighborhood representation on Instagram. Through a systematic analysis of the content shared by Instagram users, the research identifies recurring themes, visual motifs, and distinguishing features that emerge when portraying and expressing experiences of different neighborhoods using topic modelling and sentiment analysis in Orange. The study specifically focuses on Umeå, allowing for a deeper understanding of how Instagram users perceive and portray the various neighborhoods within the city. The findings of this research hold potential implications for urban planning practices, as they shed light on the factors influencing neighborhood representation on Instagram and their relevance to decision-making processes related to urban development, community engagement, and social well-being. Overall, this study provides valuable insights into the interplay between social media and neighborhood representation.

Two studies are presented, linking word-frequency information within the Global Corpus of Web-based English to real world facts. The first study concerns how patterns of the use of place names reflect geospatial and geopolitical relationships of English-speaking nations. The second study concerns how the emotional connotation of words before place names reflects general well-being in that place. Taken together, these studies demonstrate that the surface structure of language, as embodied in word frequencies, is a useful source of information about the real world. / Thesis / Master of Science (MSc) / This thesis involves two studies, using the Global Corpus of Web-based English. The first study shows how you can reconstruct a rudimentary map of English speaking countries of the world purely on the basis of how often different words happen in texts. The second study shows that when we discuss countries of the world online, how happy and exciting the adjectives are before place names relate to how long people live in that country.

This study investigates the use of emojis in sentiment analysis for the Swedish language, with the objective to assess if emojis improve the performance of the model. Sentiment analysis is an NLP classification task aimed at extracting people's opinions, sentiments, and attitudes from language. Though sentiment analysis as a research area has made a lot of progress recently, there are still some challenges to overcome. In this work, two of these challenges were considered; the analysis of a non-English language and the impact of emojis. These areas were explored through creating a sentiment annotated dataset of Swedish texts containing emojis, and creating a Swedish sentiment analysis model for evaluation. The sentiment analysis model created, SweVADER, was based on the English Lexicon-based model VADER.  The best performing SweVADER model achieved an accuracy of 0.53 and an F1-score of 0.47. Furthermore, the presence of emojis improved the analysis for most models, but not by much. The results indicate that the use of emojis can improve the sentiment analysis, but there were other features affecting the results as well. The sentiment lexicon used plays a key role, and pre-processing techniques like stemming could affect the performance too. A takeaway from this study is that emojis contain important sentiment information, and should not be disregarded. Furthermore, emojis are useful when analyzing texts, if there is a lack of linguistic resources for the language in question.

In today’s world, it is not enough for companies to consider any one social media channel in isolation. Instead, they must provide their customers with a unified experience across channels and consider interdependencies between channels. Most marketing research that examines user generated content is focused on a single channel and is limited to the English language. This thesis analyses Swedish language content collected from eight different social media platforms: Facebook, YouTube, Instagram, TikTok, Twitter, Tripadvisor, Trustpilot, and Google Reviews. The platforms were compared pairwise by the prevalence of positive, negative, and neutral sentiment in comments and reviews about the theme park Liseberg. The sentiment was predicted using a lexical approach where each word in a wordlist was assigned a weight to denote positive or negative sentiment associated with the word. The study found that there is a statistically significant difference between the positivity, negativity, and neutrality expressed by users on the different social media channels. There was no difference in sentiment between YouTube and Instagram comments, but there were differences in at least one of the three sentiment categories for all other pairwise comparisons of platforms. Having an understanding of the attitudes towards the brand in different channels can support marketers in determining their optimal mix of social media channels. These results are also of interest to researchers who should take the differences between social media platforms into consideration when designing studies around user generated content.

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