基于微博的网络舆情话题分析和用户画像系统

Data analytics, exploration, sentiment analysis and topic analysis (LDA) on Amazon customer reviews. And cool interactive plots.

A Shiny App for Visualising Male and Female Representation in Text

Analyzed 4.5 million Reddit posts to find which topics get the most engagement, what kind of language drives higher scores, and how tags and topics connect across communities.

Python library to perform topic detection on textual data that are generated over time.

Uses NLP for Investigating Spell-Checking Usage in Data Pre-Processing Phase for Topic Modeling on TripAdvisor Hotel Reviews

Replication Code for: AI Ethics on Blockchain: Topic Analysis on Twitter Data for Blockchain Security

Deliver insights into the topical content of undergraduate degree programmes.

RepoPulse 是一个 GitHub Trending 仓库分析工具，能够自动抓取 GitHub 上最热门的仓库，并根据仓库的名称和描述进行智能分类，计算出当前最热门的技术话题。

The auto-generation of descriptive text through word embedding + LSTM + n-gram algorithms and the coherence & topic analysis of the generated text.

The app displays results from a machine learning topic analysis of forum posts. Results are fetched using an Apollo client and GraphQL. VisX (d3) is used for displaying the chart in React.

Repository containing scripts used during my diploma thesis.

Ein Toolkit zur Extraktion von Konzepten und ihren Häufigkeiten von Webseiten und Datenbanken

Example implementation of how to use PySpark with MLlib to apply semantic and topic analysis to understand what a given text is on about and draw some conclusions about the data.

Este proyecto se enfoca en realizar un análisis de la opinión pública en redes sociales respecto a las elecciones presidenciales en México 2024, a través de procesamiento de lenguaje natural, análisis de sentimientos y modelado temático

A scraper written in Rust to gather Steam user reviews along with models in Python.

An analysis of Enron Emails

An application to provide clients with a onestop, centralized platform to gain a snapshot of their company and competitors, financially, among consumers, and in the media.

Topic modeling (LDA) extracts latent topics distributed across documents and represent them with most likely words.