topic_similarity

This function calculates the topic similarity between two conversations using either
Latent Dirichlet Allocation (LDA) or Latent Semantic Analysis (LSA).

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 

topic_similarity function

<dl><dt>conv1</dt>
<dd>A character vector representing the first conversation</dd>
<dt>conv2</dt>
<dd>A character vector representing the second conversation</dd>
<dt>method</dt>
<dd>A character string specifying the method to use: "lda" or "lsa"</dd>
<dt>num_topics</dt>
<dd>An integer specifying the number of topics to use in the model</dd></dl>

Arguments

Calculate topic similarity between two conversations — topic_similarity

<dl>

<dt>conv1</dt>
<dd>A character vector representing the first conversation</dd>


<dt>conv2</dt>
<dd>A character vector representing the second conversation</dd>


<dt>method</dt>
<dd>A character string specifying the method to use: "lda" or "lsa"</dd>


<dt>num_topics</dt>
<dd>An integer specifying the number of topics to use in the model</dd>

</dl>

topic_similarity: Calculate topic similarity between two conversations

Description

Usage

Value

Arguments

Examples