A psycholinguistic NLP framework for forensic text analysis of deception and emotion

TL;DR

This paper introduces a new NLP framework that uses psycholinguistic features to detect deception and emotion in forensic text analysis.

Contribution

The novel approach combines n-grams, deception detection, emotion analysis, and entity-topic correlation for forensic text analysis.

Findings

The method successfully identified guilty parties in a fictional murder case using psycholinguistic cues.

Entity-topic correlation, deception detection, and emotion analysis were key to narrowing down suspects.

The framework outperformed previous methods by analyzing a larger and more diverse dataset.

Abstract

Psycholinguistics is an interdisciplinary area of research that bridges elements of linguistics with various branches of psychology. One of its goals is to identify and explain the links that exist between our psyche and the language we speak. In this research, we are expanding upon previous research that we did using several different Natural Language Processing (NLP) techniques to identify persons of interest from a scenario that was generated by a large language model (LLM). We used a different approach to this topic, which allowed us to develop a more nuanced method of reverse engineering and breaking down the psycholinguistic features of each suspect. Through the application of n-grams paired with deception, emotion, and subjectivity over time, we were able to identify and measure cues that can be used to better identify persons of interest from a larger pool of candidates. That dataset was smaller and somewhat limited in scope. We successfully identified the guilty parties from the fictional murder case using a combination of Latent Dirichlet Allocation, word vectors, and pairwise correlations. This research was larger in scope, number of potential suspects, and in the diversity of the corpus used. We were able to determine the guilty parties identified in ground truth using our methodology in this case specifically by focusing on entity to topic correlation, deception detection, and emotion analysis.

Figures