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Beyond sentiment: an algorithmic strategy for identifying evaluations within large text corpora.

Maximilian Overbeck¹, Christian Baden¹, Tali Aharoni¹

¹Department of Communication and Journalism, The Hebrew University of Jerusalem, Jerusalem, Israel.

Communication Methods and Measures

|February 27, 2025

Summary

This summary is machine-generated.

This study introduces a new supervised machine learning (SML) strategy for identifying object-specific evaluations in text. The method accurately classifies evaluative language in political texts, outperforming existing sentiment analysis tools.

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Area of Science:

Computational Linguistics
Natural Language Processing
Political Science

Background:

Traditional sentiment analysis struggles to distinguish object-specific evaluations from general sentiment.
Identifying the semantic relationship between evaluative expressions and evaluated objects is crucial for accurate analysis.
Existing methods often produce false positives by misinterpreting potentially evaluative terms.

Purpose of the Study:

To develop and evaluate a novel supervised machine learning (SML) strategy for classifying object-specific evaluations in large text corpora.
To address the challenge of determining the evaluative function of terms in relation to specific objects.
To improve the accuracy of evaluation classification in political discourse.

Main Methods:

A supervised machine learning (SML) classifier was developed to determine if sentiment terms function evaluatively towards an object.
The classifier was trained and tested on a corpus of 10,004 text segments from U.S. news outlets and politician/journalist Tweets.
The focus was on evaluations of political predictions concerning the 2016 and 2020 U.S. presidential elections.

Main Results:

The proposed SML classifier significantly outperformed both off-the-shelf sentiment analysis tools and a pre-trained transformer-based sentiment classifier.
The classifier demonstrated high accuracy in identifying evaluative expressions and correctly discarded numerous non-evaluative uses of sentiment terms.
This reduction in false positives enhances the reliability of object-specific evaluation measurement.

Conclusions:

The developed SML strategy offers a more accurate approach to classifying object-specific evaluations in text compared to conventional sentiment analysis.
This method contributes to a more precise measurement of evaluations in large-scale text analysis, particularly in political contexts.
Future research should explore further refinements and applications of this evaluation classification strategy.