Emotional Dialogue Acts corpus contains dialogue act labels for the multimodal conversational emotion datasets IEMOCAP and MELD. https://www.aclweb.org/anthology/2020.lrec-1.78/

Python library for extracting quantitative, reproducible metrics of multi-level alignment between speakers in naturalistic language corpora.

Rezonator: Dynamics of human engagement

command line interface (CLI) to analyze and export imessage conversations into .csv files containing messages, timestamps, and IDs 📲

This is my Masters thesis project titled "Speaker Detection and Conversation Analysis on Mobile Devices".

MER is a software that identifies and highlights manipulative communication in text from human conversations and AI-generated responses. MER benchmarks language models for manipulative expressions, fostering development of transparency and safety in AI. It also supports manipulation victims by detecting manipulative patterns in human communication.

Label dialogue with Dialogue Acts and Adjacency Pairs

This project leverages Langchain and Groq to analyze conversations between recovery agents and borrowers. The analysis includes summarization, key actions identification, sentiment analysis, named entity recognition (NER), and non-compliance checks.

Conversation Analysis Annotation Schema for Computational Modelling of Dialogue

Python package based on Gibson's framework (2003) for turn-taking in group conversation analysis.

This project utilizes the LangChain and Groq to perform various analyses on loan recovery conversations. The primary functionalities include summarizing conversations, identifying key actions or next steps, and undertaking sentiment analysis of both the recovery agent and the borrower.

ADR is a software that identifies and highlights agreements and disagreements in discussion forum messages as a response to the original post.

An intuitive conversation labeling tool for extracting insights from sales and customer interaction data, complete with customizable metrics and segmentation control.

The project was done for a college class. It aimed to analyse the debate between Dr John Lennox and Prof Richard Dawkins

Analysis of 89,000 ChatGPT conversations to understand interaction patterns and response behaviors.