Here is more information on functional words.

From a traditional narrative psychology perspective, the words people use can provide deep insights into their personality. A few examples are explained below. However, with large language models (LLM) and natural language processing (NLP), single words are no longer the primary determinant. LLM's can determine the entire context and intended meaning of an interviewee’s statements. 

Sequential Order: The degree to which a person's narrative is coherent and well-structured reflects their ability to organize their thoughts and communicate effectively. Words that move your discourse forward look like:

• • First …, then …, after that ...
• • After …, I … before finally ...

Conditional Logic: Words that show a causal connection or restraint look like:

• • Because of X, I did ...
• • When it became clear that …, I ...
• • While this was happening, I held back on Z.
• • I didn’t escalate…, because…

Cognitive Complexity: The use of prepositions ("of", "in", "between") and articles ("a", "an", "the") along with their corresponding nouns are associated with higher cognitive complexity. An algorithm will evaluate interviewees as being more analytical.

Pronouns: Individuals with a collective orientation tend to use "we" and "us" (first-person plural) instead of "I" and "me" (first-person singular) and may be more effective in promoting a sense of unity and shared purpose among team members. Research has indeed shown that workers who use more inclusive language tend to be more liked.

Self-Referential Language: Self-referential terms (e.g., "I", "me", "my") are the opposite of first-person plural.  When used excessively, they could, for some jobs and reviewers, indicate a self-focused perspective. On the other hand, the use of personal experiences, and thus, first-person singular, is needed for interviews. It's also necessary for effective persuasion skills. Striking a balance between self-referential language and other-focused language could indicate an ability to consider multiple perspectives, which is a key aspect of emotional intelligence and leadership.

Future-Oriented Language: Candidates who use more future-oriented language (e.g., "will", "going to", "plan") might be more forward-thinking or planning-oriented, which could be beneficial in some roles.

Concrete vs. Abstract Language: Concrete language refers to words and phrases that are specific, detailed, and grounded in sensory experience (e.g., "last Tuesday", "a large wooden desk"), while abstract language is more general and conceptual (e.g., "in the future", "values", "success"). A balance between these two can suggest an ability to handle both details and big-picture concepts.

Passive vs. Active Voice: Active voice (e.g., "I completed the project") signifies direct action and responsibility, whereas passive voice (e.g., "The project was completed") can suggest evasion of responsibility. Candidates who frequently use active voice might be more proactive and convey a drive to results.

Certainty vs. Tentative Language: Words that express certainty (e.g., "always", "never") could suggest decisiveness or rigidity, depending on the context. Conversely, tentative language (e.g., "maybe", "possibly") could suggest openness, flexibility, or lack of confidence, again depending on context.

Complexity of Vocabulary: The use of a varied and complex vocabulary could indicate a high level of verbal intelligence and education. However, it's also important to consider the appropriateness of the vocabulary to the context – excessive use of jargon (keywords) or unnecessarily complex language could indicate a lack of clarity or an attempt to impress rather than communicate effectively.

Positive vs. Negative Emotion Words: The frequency of positive emotion words (e.g., "happy", "love", "excited") versus negative emotion words (e.g., "sad", "hate", "angry") have been shown to provide clues about a person's emotional state and perspective. A higher use of positive emotion words might suggest optimism and resilience. However, algorithmic models do not evaluate single concept words without understanding the context. The surrounding words are used. So, saying a word like 'hate' does not necessarily negatively impact a candidate's score.

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