ContextLab/hypertools
A Python toolbox for gaining geometric insights into high-dimensional data
Twitter Trends is a web-based application that automatically detects and analyzes emerging topics in real time through hashtags and user mentions in tweets. Twitter being the major microblogging service is a reliable source for trends detection. The project involved extracting live streaming tweets, processing them to find top hashtags and user mentions and displaying details for each trending topic using trends graph, live tweets and summary of related articles. It also included Topic Modelling and Entity Categorization to classify the tweets and extract valuable information about its contents and find similar tweets and related articles and URLs. A trending topic is represented as a word cloud created from set of keywords (hashtags or user mentions) that belong to that topic. Thus this application provides the required information to get an overhaul of the topics which are trending at that particular time. This data can be used to support social analysis, finance, marketing or news tracking.
$ git clone https://github.com/Medha11/Twitter-Trends.gitA Python toolbox for gaining geometric insights into high-dimensional data
Leveraging BERT and c-TF-IDF to create easily interpretable topics.
Top2Vec learns jointly embedded topic, document and word vectors.
A Toolkit for Industrial Topic Modeling
Fast vectorization, topic modeling, distances and GloVe word embeddings in R.
Data from GitHub ยท snapshot Sep 24, 2026