Sökning: "computational text analysis"

Visar resultat 1 - 5 av 31 uppsatser innehållade orden computational text analysis.

  1. 1. The Sound of Skepticism Analyzing Climate Change Denial in Swedish Podcasts and YouTube Channels

    Kandidat-uppsats, Göteborgs universitet / / Institutionen för sociologi och arbetsvetenskap

    Författare :Victoria Vallström; [2024-02-14]
    Nyckelord :denialism; climate skepticism; social movements; countermovements; digital media; digital data; computational grounded theory; topic modeling; computational text analysis;

    Sammanfattning : This study explores Sweden's climate change denial by analyzing the spoken-word discourse of its countermovement, focusing on digital media content from Swedish parliament member Elsa Widding with an aim to provide empirical insights into the discourse of Sweden's Climate Change Countermovement (CCCM). Questions guiding this study are: What are the most prevalent topics and themes related to climate change denial and skepticism? How do they align with established categories of climate change denial, shaping the overall narrative? What mobilizing ideas and meanings are present, how are they shaped, and how do they contribute to the movement's goals? The material consists of Elsa Widding's complete audio-based "movement texts'' from 2019-2023, including YouTube content, podcasts, and appearances on Riks, totaling over 2000 minutes of audio transcribed into text via AI technology. LÄS MER

  2. 2. IŻ SWÓJ JĘZYK MAJĄ! An exploration of the computational methods for identifying language variation in Polish

    Master-uppsats, Göteborgs universitet / Institutionen för filosofi, lingvistik och vetenskapsteori

    Författare :Maria Irena Szawerna; [2023-06-19]
    Nyckelord :language variation; Polish; diachronic linguistics; part-of-speech tagging; lemmatization; corpus linguistics;

    Sammanfattning : Computational approaches to language variation continue to contribute in a relevant way to various fields, including Natural Language Processing (NLP) and linguistics. Being able to accommodate variation within natural language increases the robustness of NLP models and their usefulness in real-life applications; simultaneously, detecting and describing variation and trends that govern it is one of the main goals of sociolinguistics and historical linguistics, meaning that some of the advances in NLP can contribute to these fields as well. LÄS MER

  3. 3. Detection of insurance fraud using NLP and ML

    Master-uppsats, Lunds universitet/Matematisk statistik

    Författare :Rasmus Bäcklund; Hampus Öhman; [2023]
    Nyckelord :Technology and Engineering;

    Sammanfattning : Machine-Learning can sometimes see things we as humans can not. In this thesis we evaluated three different Natural Language Procces-techniques: BERT, word2vec and linguistic analysis (UDPipe), on their performance in detecting insurance fraud based on transcribed audio from phone calls (referred to as audio data) and written text (referred to as text-form data), related to insurance claims. LÄS MER

  4. 4. Contextual short-term memory for LLM-based chatbot

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Mikael Lauri Aleksi Törnwall; [2023]
    Nyckelord :Chatbot; Artificial Intelligence; Machine Learning; Language Model; Large Language Model; GPT-3; Natural Language Processing; Text Summarization; Dialogue Summarization; Prompt Design; Prompt Programming; Chatbot; Artificiell Intelligens; Maskininlärning; Språkmodell; Stor Språkmodell; GPT-3; Naturlig Ppråkbehandling; Textsammanfattning; Sammanfattning av Dialog; Design för Inmatningsprompt; Inmatningsprompt Programmering;

    Sammanfattning : The evolution of Language Models (LMs) has enabled building chatbot systems that are capable of human-like dialogues without the need for fine-tuning the chatbot for a specific task. LMs are stateless, which means that a LM-based chatbot does not have a recollection of the past conversation unless it is explicitly included in the input prompt. LÄS MER

  5. 5. Automatic Analysis of Peer Feedback using Machine Learning and Explainable Artificial Intelligence

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Kevin Huang; [2023]
    Nyckelord :Text classification; Peer feedback; Explainable Artificial Intelligence; BERT; RoBERTa; DistilBERT; Decision Tree; MLP; CSCL; STEM education; Textklassificering; Feedback till kamrater; Förklarig Artificiell Intelligens; BERT; RoBERTa; DistilBERT; Decision Tree; MLP; CSCL; STEM-utbildning;

    Sammanfattning : Peer assessment is a process where learners evaluate and provide feedback on one another’s performance, which is critical to the student learning process. Earlier research has shown that it can improve student learning outcomes in various settings, including the setting of engineering education, in which collaborative teaching and learning activities are common. LÄS MER