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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
141

Exploring the Potential of Twitter Data and Natural Language Processing Techniques to Understand the Usage of Parks in Stockholm / Utforska potentialen för användning av Natural Language Processing på Twitter data för att förstå användningen av parker i Stockholm

Norsten, Theodor January 2020 (has links)
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.
142

Predicting Bitcoin price fluctuation with Twitter sentiment analysis / Förutspå Bitcoin prisändringar med hjälp av semantisk analys på Twitter data

Stenqvist, Evita, Lönnö, Jacob January 2017 (has links)
Programmatically deriving sentiment has been the topic of many a thesis: it’s application in analyzing 140 character sentences, to that of 400-word Hemingway sentences; the methods ranging from naive rule based checks, to deeply layered neural networks. Unsurprisingly, sentiment analysis has been used to gain useful insight across industries, most notably in digital marketing and financial analysis. An advancement seemingly more excitable to the mainstream, Bitcoin, has risen in number of Google searches by three-folds since the beginning of this year alone, not unlike it’s exchange rate. The decentralized cryptocurrency, arguably, by design, a pure free market commodity – and as such, public perception bears the weight in Bitcoins monetary valuation. This thesis looks toward these public perceptions, by analyzing 2.27 million Bitcoin-related tweets for sentiment fluctuations that could indicate a price change in the near future. This is done by a naive method of solely attributing rise or fall based on the severity of aggregated Twitter sentiment change over periods ranging between 5 minutes and 4 hours, and then shifting these predictions forward in time 1, 2, 3 or 4 time periods to indicate the corresponding BTC interval time. The prediction model evaluation showed that aggregating tweet sentiments over a 30 min period with 4 shifts forward, and a sentiment change threshold of 2.2%, yielded a 79% accuracy. / Ämnet sentiment analysis, att programmatiskt härleda underliggande känslor i text, ligger som grund för många avhandlingar: hur det tillämpas bäst på 140 teckens meningar såväl som på 400-ords meningar a’la Hemingway, metoderna sträcker sig ifrån naiva, regelbaserade, till neurala nätverk. Givetvis sträcker sig intresset för sentiment analys utanför forskningsvärlden för att ta fram insikter i en rad branscher, men framförallt i digital marknadsföring och financiell analys. Sedan början på året har den digitala valutan Bitcoin stigit trefaldigt i sökningar på Google, likt priset på valutan. Då Bitcoins decentraliserade design är helt transparant och oreglerad, verkar den under ideala marknadsekonomiska förutsättningar. På så vis regleras Bitcoins monetära värde av marknadens uppfattning av värdet. Denna avhandling tittar på hur offentliga uppfattningar påverkar Bitcoins pris. Genom att analysera 2,27 miljoner Bitcoin-relaterade tweets för sentiment ändringar, föutspåddes ändringar i Bitcoins pris under begränsade förhållningar. Priset förespåddes att gå upp eller ner beroende på graden av sentiment ändring under en tidsperiod, de testade tidsperioderna låg emellan 5 minuter till 4 timmar. Om en förutspånning görs för en tidsperiod, prövas den emot 1, 2, 3 och 4 skiftningar framåt i tiden för att ange förutspådd Bitcoin pris interval. Utvärderingen av förutspåningar visade att aggregerade tweet-sentiment över en 30-minutersperiod med 4 skift framåt och ett tröskelvärde för förändring av sentimentet på 2,2 % gav ett resultat med 79 % noggrannhet.
143

A Performance Survey of Text-Based Sentiment Analysis Methods for Automating Usability Evaluations

Van Damme, Kelsi 01 June 2021 (has links) (PDF)
Usability testing, or user experience (UX) testing, is increasingly recognized as an important part of the user interface design process. However, evaluating usability tests can be expensive in terms of time and resources and can lack consistency between human evaluators. This makes automation an appealing expansion or alternative to conventional usability techniques. Early usability automation focused on evaluating human behavior through quantitative metrics but the explosion of opinion mining and sentiment analysis applications in recent decades has led to exciting new possibilities for usability evaluation methods. This paper presents a survey of modern, open-source sentiment analyzers’ usefulness in extracting and correctly identifying moments of semantic significance in the context of recorded mock usability evaluations. Though our results did not find a text-based sentiment analyzer that could correctly parse moments as well as human evaluators, one analyzer was found to be able to parse positive moments found through audio-only cues as well as human evaluators. Further research into adjusting settings on current sentiment analyzers for usability evaluations and using multimodal tools instead of text-based analyzers could produce valuable tools for usability evaluations when used in conjunction with human evaluators.
144

Designing and evaluating an algorithm to pick out minority comments online

Liu, Elin January 2022 (has links)
Social media and online discussion forums have allowed people to hide behind a veil of anonymity, which has made the platforms feel unsafe for people with a different opinion than the majority. Recent research on robots and bots have found that they are a good option when it comes to inducing cooperation or acting as a conversation partner to encourage critical thinking. These robots and bots are based on an algorithm that is able to identify and classify comments, usually into positive and negative comments, left by users. The problem attended to in this thesis is to explore the possibility of creating an algorithm that can classify and pick out a minority opinion with an accuracy of at least 90%. The purpose is to create one of the vital algorithms for a larger project. The goal of this thesis is to provide a functioning algorithm with an accuracy of at least 90% for future implementations. In this thesis, the research approach is quantitative. The results show that it is possible to create an algorithm with the ability to classify and identify comments that also can pick out a minority opinion. Furthermore, the algorithm also achieved an accuracy of at least 90% when it comes to classification of comments, which makes the search for a minority opinion much easier. / Sociala medier och diskussionsforum online har tillåtit människor att gömma sig bakom sin datorskärm och vara anonym. Detta har gjort sociala medier till en osäker plats för människor som inte delar samma åsikt som majoriteten om olika diskussionsämnen. Ny forskning om robotar och sociala botar har funnit att dem är effektiva med att få människor att samarbeta samt att dem är en bra konversationspartner som framkallar mer kritiskt tänkande. Dessa robotar och sociala botar är baserade på en algoritm som kan identifiera och klassificera kommentarer, oftast till positiva eller negativa kommentarer som användare av sociala medier har lämnat. Problemet som avhandlingen försöker lösa är om det är möjligt att skapa en algoritm som kan identifiera och klassificera kommentarer, men även hitta och ta fram en åsikt som inte är en del av majoriteten med en träffsäkerhet på minst 90%. Ändamålet är att skapa en viktig byggsten för ett större forskningsprojekt. Målet med avhandlingen är att skapa en funktionerande algoritm för framtida undersökning som förhoppningsvis kan motarbeta partiskhet i sociala medier. Avhandlingens ståndpunkt är kvantitativ. Resultaten från avhandlingen visar att det är möjligt att skapa en algoritm som kan klassificera samt hitta en åsikt som inte är en del av majoriteten. Dessutom har algoritmen hög noggrannhet när det gäller klassificeringen vilket underlättar sökandet av en åsikt.
145

Sentiment-Driven Cryptocurrency Price Prediction : A Comparative Analysis of AI Models

Kotapati, Jammithri, Vendrapu, Suma January 2023 (has links)
Background: In the last few years, there has been rapid growth in the use of cryptocurrency, as it is a form of digital currency and was developed using blockchain technology, so it is almost impossible to counterfeit cryptocurrency. Due to these features, it has attracted a lot of popularity and attention in the market. There has been a research gap in predicting accurate cryptocurrency prices by using sentiment analysis. This study will use Artificial Intelligence-based methods and sentiment analysis to develop a model for predicting cryptocurrency prices. By using the mentioned methods in this thesis, the developed model will provide precise results. Objectives: The objective of the thesis is to compare artificial intelligence models for cryptocurrency price prediction and analyze the importance of sentiment analysis by understanding the public pulse in cryptocurrencies and how it affects price fluctuations, analyzing the correlation within news articles, social media posts, and price fluctuations, as well as evaluating the model performance by employing metrics like RSME, MSE, MAE, and R2 error. Methods: The thesis follows the use of a systematic literature review along with an experimental model for comparing artificial intelligence models. Sentiment analysis played a crucial role in understanding market dynamics. By using linear regression, random forest, and gradient boosting algorithms artificial intelligence models are built to predict cryptocurrency prices using sentiment analysis. The developed models are then compared using performance metrics. This research has analyzed and evaluated each model's performance in predicting cryptocurrency prices. Results: The results of the systematic literature review indicated that market sentiment affects cryptocurrency prices. Prices have increased when market sentiment has been positive, whereas prices dropped when sentiment has been negative. The correlation between cryptocurrency values and market mood, however, is complicated as it depends on a variety of factors. Based on the evaluation measures, the random forest artificial intelligence model is the most accurate in predicting cryptocurrency prices after evaluating the three artificial intelligence models. Conclusions: This study utilized sentiment analysis and artificial intelligence to forecast cryptocurrency prices. It highlighted the significance of sentiment analysis as a tool for predicting the short-term price of cryptocurrencies by demonstrating how negative sentiment is correlated with decreases in price compared to positive sentiment with price increases. However, it recognized that it was necessary to take into consideration the complexity and broad range of effects on cryptocurrency markets. Research in the future will examine comprehensive sentiment analysis methods and broadening data sources.
146

Joint Dynamic Online Social Network Analytics Using Network, Content and User Characteristics

Ruan, Yiye 18 May 2015 (has links)
No description available.
147

Linguistic Approach to Information Extraction and Sentiment Analysis on Twitter

Nepal, Srijan 11 October 2012 (has links)
No description available.
148

Sentiment Analysis On Java Source Code In Large Software Repositories

Sinha, Vinayak 02 June 2016 (has links)
No description available.
149

Approaches to Automatically Constructing Polarity Lexicons for Sentiment Analysis on Social Networks

Khuc, Vinh Ngoc 16 August 2012 (has links)
No description available.
150

Investigating MOOCs with the use of sentiment analysis of learners' feedback. What makes great MOOCs across different domains?

Nefedova, Natalia January 2022 (has links)
Recently, distance education has become popular and has gotten much attention. Information and Communication Technology advances fostered distance learning creation and enabled individuals to participate in the education process via various web-based platforms and study entirely online. Thus, the notion of e-learning and distance learning emerged. Massive Open Online Courses (MOOCs) appeared as part of e-learning in 2008 and attracted great interest, especially during the COVID-19 pandemic. It was anticipated that this kind of study also could be integrated into higher education and revolutionize the learning approach. However, several issues related to MOOCs limit their full potential. One of the most significant problems is substantial rate of learners’ attrition. It was discovered that only 5-10 percent of MOOC learners complete a course. This thesis aims to examine what influences individuals’ decision to leave MOOCs and how learners perceive various course components to get ideas regarding how MOOCs could be enhanced. To do this, the mixed-method study was undertaken where quantitative data analysis of learners’ reviews from discussion forums and qualitative interviews were adopted. It allowed to get two perspectives and broaden the thesis out- come. For the current research, data was collected from six courses in three different subjects-«Health», «Art and Humanity/Design» and «Computer/Data Science». In the first part of the work, sentiment analysis and topic modeling using Python packages were carried out, and then the results were used to construct an interview questionnaire. Lexicon-based sentiment analysis technique and LDA topic modeling algorithm were utilized and proved to be robust methods to extract texts’ polarity and peoples’ opinions. In the qualitative part, 19 topics of discussion were identified, which were consolidated into eight topics with higher abstraction – materials, instructor, content, time, assignment, feedback, program(course), and algorithms. Then during the qualitative part, participants expressed their opinions regarding these topics, and analysis codes were predefined, and new topics did not emerge. The results showed learners’ perceptions related to presented topics and how these aspects influence experience with MOOCs. The outcome also showed a slight disparity between different subject learners, in both qualitative and quantitative studies identified topics of discussion were not exactly the same, showing that learners from different educational domains tend to discuss different themes.

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