中文情感分析库(Chinese Sentiment))可对文本进行情绪分析、正负情感分析。Text analysis, supporting multiple methods including word count, readability, document similarity, sentiment analysis, Word2Vec .

cntext 是一个专为社会科学实证研究设计的中文文本分析 Python 库。它不仅提供传统的词频统计和情感分析，还支持词嵌入训练、语义投影计算等高级功能，帮助研究者从大规模非结构化文本中测量抽象构念——如态度、认知、文化观念与心理状态。

FieldWorks is a suite of software tools for language and cultural data, with support for complex scripts.

Mining Argument Structures with Expressive Inference (Linear and LSTM Engines)

Extension of the SentenceSimplification project

Mining Discourse Markers for Unsupervised Sentence Representation Learning

Rezonator: Dynamics of human engagement

End-to-end shallow discourse parser

rst-workbench enables the hassle-free installation of RST parsers. It lets you visually compare their results in your browser.

Attention mechanism in CNNs to extract events of interest

Sense Disambiguation of Connectives for PDTB-Style Discourse Parsing

An annotated corpus of discussion forum threads from Massive Open Online Courses.

Discourse-Aware Sentiment Analysis

A collection of various discourse segmenters

MigrationInTheTimes: Visualising changes in the construction of meaning with Word Vector Space

ALIGN 2.0: Modern Python package for multi-level linguistic alignment analysis. Faster, streamlined, and feature-rich while maintaining full compatibility with the original ALIGN methodology (Duran, Paxton, & Fusaroli, 2019). Recommended for all new projects. Original version: https://github.com/nickduran/align-linguistic-alignment

Taking a corpus of text-based documents, we can now run doc2vec, kmeans clustering, and topic models to learn about entire discourses in just a few minutes. This gives valuable insight into the information present in a discourse from a high level using deep learning

Measure the civility of posts on your Discourse forum using Google Perspective API

The RST-LSTM module refines predictions of the sequential text classifier (BERT) on documents with complex discourse

LLM-inspired BiLSTM pipeline for real-time, multi-label toxicity inference across adversarial discourse modalities.