# Identifying Persons of Interest in Digital Forensics Using NLP-Based AI
## 2 Literature Review
### NLP Tools  
The task of correlating a suspect with topics and keywords can be conducted using a wide variety of tools used in NLP. We found through a review of the literature and empirical research that Latent Dirichlet Allocation (LDA), pairwise correlation, and word embedding plots yielded the best results. LDA was introduced in 2003, so it is one of the older techniques in the canon of tools used in computational linguistics. LDA works on the assumption that every dataset of text consists of a finite number of latent topics that can be visually represented [38]. The LDAvis graphing tool is a good way of visualizing topics and their associated words [39]. Pairwise correlation is a statistical function that takes pairs of words and calculates the strength of their relationship to other pairs of words in the dataset. The relationship between word pairs is visualized in a network graph of nodes and edges. Clusters of nodes and edges represent topics. These clusters visualize relationships between named entities and keywords [40]. Word embeddings were first introduced in 2003. Since that time, they have been very popular in NLP. With word embeddings, a word is represented as a vector of numbers that is recognizable to a computer. Different word vectors can be compared using distance metrics such as cosine distance. When word vectors are displayed on a two-dimensional graph, words that appear closer together are closer in their context [41].  
We synthesized key data points from the literature that we surveyed. Based on the five previously discussed themes we extracted during the review, we made the following inferences which served as guiding parameters for our methodology. First, it is not possible to assign guilt to a person based on circumstantial evidence. There are not enough given variables in the data to unequivocally assign attribution to one or two people. Instead, we can identify Persons of Interest (PoI) based on emotion and correlation with keywords. If a suspect has noticeable and consistent spikes in fear, anger, anxiety, shame, or guilt, they will warrant more scrutiny [27]. Also, if a suspect is highly correlated with topics and keywords associated with a crime, the associations will require closer scrutiny.  
Second, sentiment by itself does not possess the full context necessary to predict behavior patterns. However, sentiment can reasonably serve as an initial indicator suggesting possible miscreant behavior. Sentiment, in this case, refers to a discrete range of emotions within the negative sentiment spectrum. Specifically, these emotions are anger, anxiety, guilt, shame, and fear. We must acknowledge that these emotions will not be universally manifested in suspects. There will be false positives and false negatives. One example of this is a person who possesses a sociopathic personality. Such a person may not feel any guilt, shame, or fear following the commission of a criminal act. We make the assumption that the negative spectrum of emotions will manifest in people with an average personality or temperament [42].  
Third, our objective is to identify correlations and associations between people and topics [43]. An association between a person and a keyword or topic does not identify guilt per se. When we are dealing with a discrete pool of candidates for an event such as a homicide investigation, a strong correlation between a person and a relevant topic suggests one of three possibilities. First, the person has engaged in frequent conversations with others in the same candidate pool about the subject matter. Second, the person has second-hand involvement in the homicide. This involvement could be as a witness or as someone who has some peripheral involvement in the crime. Third, the person is directly involved with or is a co-conspirator in the criminal activity. The relevant keywords identified in the person-to-topic correlations for a homicide could be, for example, \"alibi\", \"confess\", \"hiding\", and \"body\". The relevant keywords will depend on the background of the investigation.