Semantic Topic Modeling and Trend Analysis

Semantic Topic Modeling and Trend Analysis

This thesis focuses on finding an end-to-end unsupervised solution to solve a two-step problem of extracting semantically meaningful topics and trend analysis of these topics from a large temporal text corpus. To achieve this, the focus is on using the latest develop- ments in Natural Language Proce...

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Bibliographic Details
Main Author: Mann, Jasleen Kaur
Format: Others
Language:English
Published: Linköpings universitet, Statistik och maskininlärning 2021
Subjects:
NLP
unsupervised topic modelling
trend analysis
LDA
BERT
Sentence-BERT
TF-IDF
transformer based language models
document clustering
Language Technology (Computational Linguistics)
Språkteknologi (språkvetenskaplig databehandling)
Computer Sciences
Datavetenskap (datalogi)
Online Access:http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-173924

Internet

http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-173924

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