Using machine learning and information visualisation for discovering latent topics in Twitter news
We propose a method to discover latent topics and visualise large collections of tweets for easy identification and interpretation of topics, and exemplify its use with tweets from a Colombian mass media giant in the period 2014--2019. The latent topic analysis is performed in two ways: with the training of a Latent Dirichlet Allocation model, and with the combination of the FastText unsupervised model to represent tweets as vectors and the implementation of K-means clustering to group tweets into topics. Using a classification task, we found that people respond differently according to the various news topics. The classification tasks consists of the following: given a reply to a news tweet, we train a supervised algorithm to predict the topic of the news tweet solely from the reply. Furthermore, we show how the Colombian peace treaty has had a profound impact on the Colombian society, as it is the topic in which most people engage to show their opinions.
Code (0)
등록된 구현이 없습니다.
Tasks
BIG-bench Machine LearningClusteringGeneral ClassificationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
TopicTracker: A Platform for Topic Trajectory Identification and Visualisation
Topic trajectory information provides crucial insight into the dynamics of topics and their evolutionary relationships over a given time. Also, this information can help to improve our understanding on how new topics hav…
Crime Topic Modeling
The classification of crime into discrete categories entails a massive loss of information. Crimes emerge out of a complex mix of behaviors and situations, yet most of these details cannot be captured by singular crime t…
ClusteringA Data-driven Latent Semantic Analysis for Automatic Text Summarization using LDA Topic Modelling
With the advent and popularity of big data mining and huge text analysis in modern times, automated text summarization became prominent for extracting and retrieving important information from documents. This research in…
ArticlesExtractive SummarizationText SummarizationDiscovering Significant Topics from Legal Decisions with Selective Inference
We propose and evaluate an automated pipeline for discovering significant topics from legal decision texts by passing features synthesized with topic models through penalised regressions and post-selection significance t…
Language ModelingLanguage ModellingTopic ModelsDiscovering conversational topics and emotions associated with Demonetization tweets in India
Social media platforms contain great wealth of information which provides us opportunities explore hidden patterns or unknown correlations, and understand people's satisfaction with what they are discussing. As one showc…