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.

No description available.

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.

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.

No description available.

Spectral learning algorithms, which learn an unknown function by learning a spectral representation of the function, have been widely used in computational learning theory to prove many interesting learnability results. These algorithms have also been successfully used in real-world applications. However, previous work has left open many questions about how to best use these methods in real-world learning scenarios. This dissertation presents several significant advances in real-world spectral learning. It presents new algorithms for finding large spectral coefficients (a key sub-problem in spectral learning) that allow spectral learning methods to be applied to much larger problems and to a wider range of problems than was possible with previous approaches. It presents an empirical comparison of new and existing spectral learning methods, showing among other things that the most common approach seems to be the least effective in typical real-world settings. It also presents a multi-spectrum learning approach in which a learner makes use of multiple representations when training. Empirical results show that a multi-spectrum learner can usually match or exceed the performance of the best single-spectrum learner. Finally, this dissertation shows how a particular application, sentiment analysis, can benefit from a spectral approach, as the standard approach to the problem is significantly improved by incorporating spectral features into the learning process.

This degree project investigates if a company’s stock price development can be predicted using the general opinion expressed in tweets about the company. The project starts off with the model from a previous project and then tries to improve the results using state-of-the-art neural network sentiment analysis and more tweet data. This project also attempts to perform hourly predictions along with daily predictions in order to investigate the method further. The results show a decrease in accuracy compared to the previous project. The results also indicate that the neural network sentiment analysis improves the accuracy of the stock price development when compared to the baseline model under comparable conditions. / Detta examensarbete undersöker om ett företags aktievärdesutveckling kan förutspås genom att använda sig av den generella opinionen hos tweets skrivna om företaget. Examensarbetet utgår ifrån en model från ett tidigare projekt och försöker förbättra resultaten från denna genom att använda sig av dels state-of-the-art sentimentanalys med neurala nätverk, dels mer tweet data. Examensarbetet undersöker både prognoser timvis samt dygnsvis för att undersöka metoden djupare. Resultaten tyder på en minskad träffsäkerhet jämfört med det tidigare projektet. Resultaten indikerar också att sentimentanalys med neurala nätverk förbättrar träffsäkerheten hos aktievärdesprognosen jämfört med tidigare sentimentanalysmetod givet jämförbara förutsättningar.

Traditional methods used to investigate the usage of parks consists of questionnaire which is both a very time- and- resource consuming method. Today more than four billion people daily use some form of social media platform. This has led to the creation of huge amount of data being generated every day through various social media platforms and has created a potential new source for retrieving large amounts of data. This report will investigate a modern approach, using Natural Language Processing on Twitter data to understand how parks in Stockholm being used. Natural Language Processing (NLP) is an area within artificial intelligence and is referred to the process to read, analyze, and understand large amount of text data and is considered to be the future for understanding unstructured text. Twitter data were obtained through Twitters open API. Data from three parks in Stockholm were collected between the periods 2015-2019. Three analysis were then performed, temporal, sentiment, and topic modeling analysis. The results from the above analysis show that it is possible to understand what attitudes and activities are associated with visiting parks using NLP on social media data. It is clear that sentiment analysis is a difficult task for computers to solve and it is still in an early stage of development. The results from the sentiment analysis indicate some uncertainties. To achieve more reliable results, the analysis would consist of much more data, more thorough cleaning methods and be based on English tweets. One significant conclusion given the results is that people’s attitudes and activities linked to each park are clearly correlated with the different attributes each park consists of. Another clear pattern is that the usage of parks significantly peaks during holiday celebrations and positive sentiments are the most strongly linked emotion with park visits. Findings suggest future studies to focus on combining the approach in this report with geospatial data based on a social media platform were users share their geolocation to a greater extent. / Traditionella metoder använda för att förstå hur människor använder parker består av frågeformulär, en mycket tids -och- resurskrävande metod. Idag använder mer en fyra miljarder människor någon form av social medieplattform dagligen. Det har inneburit att enorma datamängder genereras dagligen via olika sociala media plattformar och har skapat potential för en ny källa att erhålla stora mängder data. Denna undersöker ett modernt tillvägagångssätt, genom användandet av Natural Language Processing av Twitter data för att förstå hur parker i Stockholm används. Natural Language Processing (NLP) är ett område inom artificiell intelligens och syftar till processen att läsa, analysera och förstå stora mängder textdata och anses vara framtiden för att förstå ostrukturerad text. Data från Twitter inhämtades via Twitters öppna API. Data från tre parker i Stockholm erhölls mellan perioden 2015–2019. Tre analyser genomfördes därefter, temporal, sentiment och topic modeling. Resultaten från ovanstående analyser visar att det är möjligt att förstå vilka attityder och aktiviteter som är associerade med att besöka parker genom användandet av NLP baserat på data från sociala medier. Det är tydligt att sentiment analys är ett svårt problem för datorer att lösa och är fortfarande i ett tidigt skede i utvecklingen. Resultaten från sentiment analysen indikerar några osäkerheter. För att uppnå mer tillförlitliga resultat skulle analysen bestått av mycket mer data, mer exakta metoder för data rensning samt baserats på tweets skrivna på engelska. En tydlig slutsats från resultaten är att människors attityder och aktiviteter kopplade till varje park är tydligt korrelerat med de olika attributen respektive park består av. Ytterligare ett tydligt mönster är att användandet av parker är som högst under högtider och att positiva känslor är starkast kopplat till park-besök. Resultaten föreslår att framtida studier fokuserar på att kombinera metoden i denna rapport med geospatial data baserat på en social medieplattform där användare delar sin platsinfo i större utsträckning.