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Autobiographical Memory

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Autobiographical memory is a unique type of episodic memory that involves recollecting personal life experiences. It allows individuals to remember significant events from their past, creating a narrative of their lives. One interesting phenomenon related to autobiographical memory is the reminiscence bump. This effect refers to the tendency of adults to recall more events from their second and third decades of life — typically between ages 10 to 30 — than from other periods. This period is...

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Related Experiment Video

Updated: Jun 6, 2026

Brain Imaging Investigation of the Neural Correlates of Emotional Autobiographical Recollection

Brain Imaging Investigation of the Neural Correlates of Emotional Autobiographical Recollection

Published on: August 26, 2011

Can computational sentiment analysis classify autobiographical memories? Comparing VADER and TextBlob.

Khalil Husein¹, Lakshay Goel², Eric Anderson¹

¹Department of Psychology, University of Waterloo.

Canadian Journal of Experimental Psychology = Revue Canadienne De Psychologie Experimentale

|June 4, 2026

Summary

This summary is machine-generated.

Computational sentiment analysis tools like VADER can rapidly assess emotional valence in autobiographical narratives. VADER showed better accuracy than TextBlob, achieving moderate congruence comparable to human coders for psychological text analysis.

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Last Updated: Jun 6, 2026

Brain Imaging Investigation of the Neural Correlates of Emotional Autobiographical Recollection

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Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury

Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury

Published on: May 15, 2016

Area of Science:

Psychology
Computational Linguistics
Natural Language Processing

Background:

Manual coding of narrative texts for psychological analysis is time-consuming and limits sample size.
Natural language processing (NLP) offers a potential solution for rapid text analysis.
The accuracy of NLP sentiment analysis tools for psychological data compared to human coders is not well-established.

Purpose of the Study:

To compare the accuracy of two popular lexicon-based sentiment analysis tools, VADER and TextBlob, against self-reported valence in autobiographical narratives.
To evaluate the congruence of computational sentiment analysis with human judgment in psychological research.

Main Methods:

Compared Valence Aware Dictionary and sEntiment Reasoner (VADER) and TextBlob against self-reported valence.
Analyzed 3,309 autobiographical narratives from two datasets.
Assessed classification accuracy for five and three valence types (negative, neutral, positive).

Main Results:

Both VADER and TextBlob showed a bias towards classifying narratives as neutral.
VADER demonstrated higher congruence with self-reported valence compared to TextBlob across both datasets.
Classification accuracy improved to moderate levels (MCC = .32) when limited to three valence types, comparable to human coders.
Tool performance was consistent across different dataset sizes.

Conclusions:

VADER is a more suitable tool than TextBlob for rapidly analyzing the broad emotional valence of large volumes of text data in psychological research.
NLP tools, particularly VADER, can provide a feasible alternative to manual coding for large-scale narrative analysis.
Limiting sentiment classification to three categories enhances the accuracy of computational tools for psychological text analysis.