agg_seq

Aggregate similarity sequence 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 

agg_seq function

<dl><dt>sequence</dt>
<dd>A numeric vector of similarity scores for a single dyad</dd>
<dt>num_segments</dt>
<dd>The number of segments to aggregate into</dd></dl>

Arguments

Aggregate Similarity Sequence — agg_seq

<dl>

<dt>sequence</dt>
<dd>A numeric vector of similarity scores for a single dyad</dd>


<dt>num_segments</dt>
<dd>The number of segments to aggregate into</dd>

</dl>

agg_seq: Aggregate Similarity Sequence

Description

Usage

Value

Arguments

Details

Examples