What does this research mean for the field?

VADER is a more effective tool than TextBlob for rapidly classifying the broad emotional valence of autobiographical memories, achieving performance comparable to human coders when limited to three valence types. Novelty: ClaimNovelty.INCREMENTAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

This research aims to assess the effectiveness of VADER and TextBlob in classifying autobiographical memories based on emotional valence.

June 7, 2026

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

Key Points

This research aims to assess the effectiveness of VADER and TextBlob in classifying autobiographical memories based on emotional valence.
Compared classification accuracy of VADER and TextBlob for autobiographical memories.
Limited classification to three valence types: negative, neutral, or positive.
Evaluated tool performance across different data set sizes.
VADER achieved a classification consistency rate of .32, comparable to human coders in similar studies.
Performance of both tools remained stable regardless of dataset size.
VADER outperformed TextBlob in determining broad emotional valence in large text data.

Abstract

= .32), on par with that of human coders reported in other work, when classification was limited to three valence types (negative, neutral, or positive) rather than five. Tool performance remained consistent regardless of data set size. VADER, more so than TextBlob, is a useful means of rapidly determining the broad emotional valence within large volumes of text data that would be unfeasible for manual coding. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

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

Key Points

Abstract

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