README

The conversim package provides tools for analyzing similarity between conversations, with a focus on calculating topic, lexical, semantic, stylistic, and sentiment similarities. This package can handle comparisons between two long speeches, a sequence of conversations in one or multiple dyads. Some utility functions are also provided that allow researchers to explore and visualize conversational patterns.

Installation

install.packages("conversim")

Load the package and example datasets

library(conversim)
load(system.file("extdata", "dyad_example_data.Rdata", package = "conversim"))
load(system.file("extdata", "speeches_data.RData", package = "conversim"))

Example usage

Analyzing Similarities between Two Long Speeches

# preprocess_text function
preprocessed_A <- preprocess_text(speeches_data$text[1])
preprocessed_B <- preprocess_text(speeches_data$text[2])

# topic_similarity function
lda_similarity <- topic_similarity(speeches_data$text[1], speeches_data$text[2], method = "lda", num_topics = 5)
lsa_similarity <- topic_similarity(speeches_data$text[1], speeches_data$text[2], method = "lsa", num_topics = 5)

# lexical_similarity function
lex_similarity <- lexical_similarity(preprocessed_A, preprocessed_B)

# semantic_similarity function
tfidf_similarity <- semantic_similarity(speeches_data$text[1], speeches_data$text[2], method = "tfidf")
word2vec_similarity <- semantic_similarity(speeches_data$text[1], speeches_data$text[2], method = "word2vec")

# structural_similarity function
struct_similarity <- structural_similarity(strsplit(speeches_data$text[1], "\n")[[1]], strsplit(speeches_data$text[2], "\n")[[1]])

# stylistic_similarity function
style_similarity <- stylistic_similarity(speeches_data$text[1], speeches_data$text[2])

# sentiment_similarity function
sent_similarity <- sentiment_similarity(speeches_data$text[1], speeches_data$text[2])

Analyzing Similarities over a Sequence of Conversations in a Single Dyad

# Preprocess the conversations from multiple dyads
preprocessed_data <- preprocess_dyads(dyad_example_data)

# Select one dyad for comparison
conversation <- preprocessed_data %>% filter(dyad_id == 1) %>% select(speaker_id, processed_text)

# Calculate topic similarity sequence
topic_sim <- topic_sim_seq(conversation, method = "lda", num_topics = 2, window_size = 3)

## Lexical Similarity Sequence
lexical_sim <- lex_sim_seq(conversation, window_size = 3)

## Semantic Similarity Sequence
semantic_sim <- sem_sim_seq(conversation, method = "tfidf", window_size = 3)

## Stylistic Similarity Sequence
stylistic_sim <- style_sim_seq(conversation, window_size = 3)

## Sentiment Similarity Sequence
sentiment_sim <- sent_sim_seq(conversation, window_size = 3)

Analyzing Similarities over a Sequence of Conversations across Multiple Dyads

# Preprocess the conversations from multiple dyads
preprocessed_data <- preprocess_dyads(dyad_example_data)

# Calculate topic similarity for multiple dyads
topic_sim_results <- topic_sim_dyads(preprocessed_data, method = "lda", num_topics = 3, window_size = 2)

# Calculate lexical similarity for multiple dyads
lexical_sim_results <- lexical_sim_dyads(preprocessed_data, window_size = 2)

# Calculate semantic similarity for multiple dyads
semantic_sim_results <- semantic_sim_dyads(preprocessed_data, method = "tfidf", window_size = 2)

# Calculate structural similarity for multiple dyads
structural_sim_results <- structural_sim_dyads(preprocessed_data)

# Calculate stylistic similarity for multiple dyads
stylistic_sim_results <- stylistic_sim_dyads(preprocessed_data)

# Calculate sentiment similarity for multiple dyads
sentiment_sim_results <- sentiment_sim_dyads(preprocessed_data)

# Calculate participant similarity for multiple dyads
participant_sim_results <- participant_sim_dyads(preprocessed_data)

# Calculate timing similarity for multiple dyads
timing_sim_results <- timing_sim_dyads(preprocessed_data)