preprocess_text

Analyze and compare conversations using various similarity measures
including topic, lexical, semantic, structural, stylistic, sentiment,
participant, and timing similarities. Supports both pairwise conversation
comparisons and analysis of multiple dyads. Methods are based on established research:
Topic modeling: Blei et al. (2003) <doi:10.1162/jmlr.2003.3.4-5.993>;
Landauer et al. (1998) <doi:10.1080/01638539809545028>;
Lexical similarity: Jaccard (1912) <doi:10.1111/j.1469-8137.1912.tb05611.x>;
Semantic similarity: Salton & Buckley (1988) <doi:10.1016/0306-4573(88)90021-0>;
Mikolov et al. (2013) <doi:10.48550/arXiv.1301.3781>;
Pennington et al. (2014) <doi:10.3115/v1/D14-1162>;
Structural and stylistic analysis: Graesser et al. (2004) <doi:10.1075/target.21131.ryu>;
Sentiment analysis: Rinker (2019) <https://github.com/trinker/sentimentr>.

Chao Liu Liu

conversim

Conversation Similarity Analysis

Chao Liu 

preprocess_text function

<dl><dt>text</dt>
<dd>A character string to be preprocessed</dd></dl>

Arguments

This file contains core similarity calculation functions such as topic similarity, lexical similarity, semantic similarity,
structural similarity, stylistic similarity, sentiment similarity, participant similarity, and timing similarity. — preprocess_text

<dl>

<dt>text</dt>
<dd>A character string to be preprocessed</dd>

</dl>

This file contains core similarity calculation functions such as topic similarity, lexical similarity, semantic similarity,
structural similarity, stylistic similarity, sentiment similarity, participant similarity, and timing similarity.

preprocess_text: This file contains core similarity calculation functions such as topic similarity, lexical similarity, semantic similarity, structural similarity, stylistic similarity, sentiment similarity, participant similarity, and timing similarity.

Description

Usage

Value

Arguments

Details

Examples