combine_sim_seq

Combine similarity measures for a single dyad

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 

combine_sim_seq function

<dl><dt>similarities</dt>
<dd>A list of similarity measures for a single dyad</dd>
<dt>weights</dt>
<dd>A numeric vector of weights for each similarity measure (default is equal weights)</dd></dl>

Arguments

Combine Similarity Measures — combine_sim_seq

<dl>

<dt>similarities</dt>
<dd>A list of similarity measures for a single dyad</dd>


<dt>weights</dt>
<dd>A numeric vector of weights for each similarity measure (default is equal weights)</dd>

</dl>

combine_sim_seq: Combine Similarity Measures

Description

Usage

Value

Arguments

Details

Examples