NLP

Explaining Humour Style Classifications: An XAI Approach to Understanding Computational Humour Analysis

foaad — Sun, 04 May 2025 23:28:35 +0000

Explaining Humour Style Classifications: An XAI Approach to Understanding Computational Humour Analysis

foaad Sun, 05/04/2025 - 16:28

Authorship

Mary Ogbuka Kenneth, Foaad Khosmood, Abbas Edalat

Publication

Journal of Data Mining & Digital Humanities, NLP4DH

Journal

Wednesday, April 2, 2025 - 12:00

Tags

doi

JDMDH

Abstract

Humour styles can have either a negative or a positive impact on well-being. Given the importance of these styles to mental health, significant research has been conducted on their automatic identification. However, the automated machine learning models used for this purpose are black boxes, making their prediction decisions opaque. Clarity and transparency are vital in the field of mental health. This paper presents an explainable AI (XAI) framework for understanding humour style classification, building upon previous work in computational humour analysis.

Systematic Literature Review: Computational Approaches for Humour Style Classification

foaad — Mon, 19 Feb 2024 19:04:52 +0000

Systematic Literature Review: Computational Approaches for Humour Style Classification

foaad Mon, 02/19/2024 - 11:04

Authorship

Mary Ogbuka Kenneth, Foaad Khosmood, Abbas Edalat

Publication

arXiv

Non refereed paper

Tuesday, January 30, 2024 - 12:00

Tags

arXiv.org

Abstract

Understanding various humour styles is essential for comprehending the multifaceted nature of humour and its impact on fields such as psychology and artificial intelligence. This understanding has revealed that humour, depending on the style employed, can either have therapeutic or detrimental effects on an individual's health and relationships. Although studies dedicated exclusively to computational-based humour style analysis remain somewhat rare, an expansive body of research thrives within related task, particularly binary humour and sarcasm recognition.

Improving Startup Success with Text Analysis

foaad — Tue, 12 Dec 2023 04:56:13 +0000

Improving Startup Success with Text Analysis

foaad Mon, 12/11/2023 - 20:56

Authorship

Emily Gavrilenko, Foaad Khosmood, Mahdi Rastad and Sadra Amiri Moghaddam

Publication

ICAIF 2023: ACM International Conference on AI in Finance, Workshop on NLP and Network Analysis

Workshop

Location

New York, NY

Monday, November 27, 2023 - 12:00

Tags

arXiv

Abstract

Investors are interested in predicting future success of startup companies, preferably using publicly available data which can be gathered using free online sources. Using public-only data has been shown to work, but there is still much room for improvement. Two of the best performing prediction experiments use 17 and 49 features respectively, mostly numeric and categorical in nature. In this paper, we significantly expand and diversify both the sources and the number of features (to 171) to achieve better prediction.

Automatic Bill Recommendation for Statehouse Journalists

foaad — Wed, 06 Sep 2023 19:49:18 +0000

Automatic Bill Recommendation for Statehouse Journalists

foaad Wed, 09/06/2023 - 12:49

Authorship

Michelle Perkonigg and Foaad Khosmood

Publication

Electronic Government

Conference

Sunday, August 20, 2023 - 12:00

Tags

EGOV 2023 Proceedings

Abstract

AI4Reporters is a project designed to produce automated electronic tip sheets for news reporters covering the statehouses (state level legislatures) in the United States. The project aims to capture the most important information that occurred in a bill discussion to allow reporters to quickly decide if they want to pursue a story on the subject. In this paper, we present, discuss and evaluate a module for the tip sheets that is designed to recommend additional bills to investigate for the reporter that receives the tip sheet.

Perkonigg_Electronic_Government_2023.pdf

Feature Engineering for US State Legislative Hearings: Stance, Affiliation, Engagement and Absentees

foaad — Sun, 30 Jul 2023 00:32:22 +0000

Feature Engineering for US State Legislative Hearings: Stance, Affiliation, Engagement and Absentees foaad Sat, 07/29/2023 - 17:32

Authorship

Joshua Grace and Foaad Khosmood

Publication

Digital Humanities 2023

Conference

Location

Graz, Austria

Saturday, July 1, 2023 - 12:00

Tags

DH 2023 Paper

arXiv preprint version (2021)

DH 2023 Proceedings

Abstract

In US State government legislatures, most activity occurs in committees made of lawmakers discussing bills. This paper presents systems to extract legislators' engagement and absence during committee meetings and the stance and affiliation of non-lawmakers making public comments. We propose a system to track the affiliation of organizations in public comments and whether the organizational representative supports or opposes the bill.

Government Transparency at The State Level and the Challenge for Democracy

foaad — Sun, 30 Jul 2023 00:04:00 +0000

Government Transparency at The State Level and the Challenge for Democracy

foaad Sat, 07/29/2023 - 17:04

Authorship

Foaad Khosmood

Publication

Rocky Mountain Council for Latin American Studies Conference

Poster or Panel Discussion

Location

Santa Fe, New Mexico

Wednesday, March 29, 2023 - 12:00

Tags

Conference Agenda

RMCLAS website

Abstract

Government transparency challenges exist in many forms but at the level of US State legislatures, but they are particularly egregious as public hearings are not documented in text. No official written record of public legislative meetings exists in any state legislature in America. Although a number of states have close captioned videos, these are not accurate or searchable. They are only in English. There is also no identification of speakers, bills, votes, locations, committees, etc.

Evaluation of Automatic Text Summarization using Synthetic Facts

foaad — Mon, 15 Aug 2022 03:49:30 +0000

Evaluation of Automatic Text Summarization using Synthetic Facts foaad Sun, 08/14/2022 - 20:49

Authorship

Jay Ahn and Foaad Khosmood

Publication

arXiv

Non refereed paper

Monday, April 11, 2022 - 12:00

Tags

arXiv.org

Abstract

In US State government legislatures, most of the activity occurs in committees made up of lawmakers discussing bills. When analyzing, classifying or summarizing these committee proceedings, some important features become broadly interesting. In this paper, we engineer four useful features, two applying to lawmakers (engagement and absence), and two to non-lawmakers (stance and affiliation). We propose a system to automatically track the affiliation of organizations in public comments and whether the organizational representative supports or opposes the bill.

Panoptyk: information driven MMO engine

foaad — Mon, 15 Aug 2022 01:54:03 +0000

Panoptyk: information driven MMO engine

foaad Sun, 08/14/2022 - 18:54

Authorship

Mitchell Miller, Sean Mendonca, Nathan Philliber and Foaad Khosmood

Publication

Foundations of Digital Games

Poster or Panel Discussion

Location

San Luis Obispo, California

Monday, August 26, 2019 - 12:00

Tags

ACM DL

Abstract

Project Panoptyk is a game engine designed to run Massive Multiplayer Online (MMO) games with information creation, sharing, and exchange as the central gameplay focus. This engine is a work in progress, intended to serve as a platform for simulating human/robot interaction, as well as automatic generation of game assets, quests, and real-estate. The project also aims to create an open platform allowing indie and research communities to experiment with MMO concepts.

FDG_2019 Panoptyk Poster.pdf

Gaining efficiency in human assisted transcription and speech annotation in legislative proceedings

foaad — Mon, 15 Aug 2022 01:47:08 +0000

Gaining efficiency in human assisted transcription and speech annotation in legislative proceedings

foaad Sun, 08/14/2022 - 18:47

Authorship

Thorsten Ruprechter, Foaad Khosmood, Toshihiro Kuboi, Alex Dekhtyar and Christian Gütl

Publication

Proceedings of the 19th Annual International Conference on Digital Government Research: Governance in the Data Age

Poster or Panel Discussion

Location

Delft, Netherlands

Wednesday, May 30, 2018 - 12:00

Tags

ACM DL

Abstract

We present a study using the Digital Democracy transcription tool. Human transcribers work to up-level and annotate California state legislative proceedings using the tool. Four phases of UI and functionality improvements are introduced and for each phase, the resulting change in efficiency is measured and presented.

Gaining efficiency Poster-DGO 2018.pdf

Learning alignments from legislative discourse

foaad — Mon, 15 Aug 2022 01:37:02 +0000

Learning alignments from legislative discourse

foaad Sun, 08/14/2022 - 18:37

Authorship

Daniel Kauffman, Foaad Khosmood, Toshihiro Kuboi and Alex Dekhtyar

Publication

Proceedings of the 19th Annual International Conference on Digital Government Research: Governance in the Data Age

Poster or Panel Discussion

Location

Delft, Netherlands

Wednesday, May 30, 2018 - 12:00

Tags

ACM DL

Abstract

In this work, we seek to quantify the extent to which a legislator's spoken language indicates their degree of alignment toward an organization that has a taken a documented position on some legislation. To perform this study, we use a corpus of bill discussion transcripts provided by Digital Democracy1. We then apply proven learning methods in the field of natural language processing to predict alignment scores between each member of the California state legislature and a select set of state-recognized organizations.

Kauffman_Alignments_DGO2018.pdf

kauffman-dgo-poster.pdf