| Title: | Conversation Similarity Analysis |
| Version: | 0.1.0 |
| Description: | 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. |
| License: | GPL (≥ 3) |
| Imports: | tm (≥ 0.7-8), lsa (≥ 0.73.2), topicmodels (≥ 0.2-12), sentimentr (≥ 2.7.1), word2vec, lme4, slam, ggplot2, stats |
| RoxygenNote: | 7.3.2 |
| Encoding: | UTF-8 |
| Suggests: | knitr, rmarkdown, testthat (≥ 3.0.0) |
| Config/testthat/edition: | 3 |
| VignetteBuilder: | knitr |
| URL: | https://github.com/chaoliu-cl/conversim, http://liu-chao.site/conversim/ |
| BugReports: | https://github.com/chaoliu-cl/conversim/issues |
| NeedsCompilation: | no |
| Packaged: | 2024-09-19 18:30:46 UTC; psych |
| Author: | Chao Liu [aut, cre, cph] |
| Maintainer: | Chao Liu <chaoliu@cedarville.edu> |
| Depends: | R (≥ 3.5.0) |
| Repository: | CRAN |
| Date/Publication: | 2024-09-20 14:10:13 UTC |
Aggregate Similarity Sequence
Description
Aggregate similarity sequence for a single dyad
Usage
agg_seq(sequence, num_segments)
Arguments
sequence |
A numeric vector of similarity scores for a single dyad |
num_segments |
The number of segments to aggregate into |
Details
This function aggregates a similarity sequence into a specified number of segments for a single dyad.
Value
A numeric vector of aggregated similarity scores
Examples
seq <- c(0.5, 0.6, 0.7, 0.6, 0.8, 0.7, 0.9, 0.8, 0.7, 0.8)
# Aggregate the sequence into 3 segments
agg_3 <- agg_seq(seq, 3)
print(agg_3)
# Aggregate the sequence into 5 segments
agg_5 <- agg_seq(seq, 5)
print(agg_5)
Calculate correlation between similarity measures
Description
This function calculates the correlation between different similarity measures.
Usage
calc_sim_cor(comparison_df)
Arguments
comparison_df |
A data frame output from compare_sim_meas() |
Value
A correlation matrix
Examples
topic_similarities <- list("1" = c(0.5, 0.6, 0.7), "2" = c(0.4, 0.5, 0.6))
lexical_similarities <- list("1" = c(0.6, 0.7, 0.8), "2" = c(0.5, 0.6, 0.7))
comparison_df <- compare_sim_meas(
list(topic_similarities, lexical_similarities),
c("Topic", "Lexical")
)
calc_sim_cor(comparison_df)
print(plot)
Calculate similarity sequence
Description
This function calculates a sequence of similarities between consecutive windows in a conversation.
Usage
calc_sim_seq(conversation, window_size, similarity_func)
Arguments
conversation |
A dataframe containing the conversation, with a column named 'processed_text'. |
window_size |
An integer specifying the size of each window. |
similarity_func |
A function that calculates similarity between two text strings. |
Value
A list containing two elements:
sequence |
A numeric vector of similarity scores between consecutive windows |
average |
The mean of the similarity scores |
Examples
conversation <- data.frame(processed_text = c("hello", "world", "how", "are", "you"))
result <- calc_sim_seq(conversation, 2, function(x, y) sum(x == y) / max(length(x), length(y)))
Calculate summary statistics for similarities
Description
This function calculates summary statistics for the similarities of multiple dyads.
Usage
calc_sum_stats(similarities)
Arguments
similarities |
A list of similarity sequences for each dyad |
Value
A matrix with summary statistics for each dyad
Examples
similarities <- list(
"1" = c(0.5, 0.6, 0.7),
"2" = c(0.4, 0.5, 0.6)
)
calc_sum_stats(similarities)
print(plot)
Combine Similarity Measures
Description
Combine similarity measures for a single dyad
Usage
combine_sim_seq(similarities, weights = NULL)
Arguments
similarities |
A list of similarity measures for a single dyad |
weights |
A numeric vector of weights for each similarity measure (default is equal weights) |
Details
This function combines multiple similarity measures into a single overall similarity score for a single dyad.
Value
A list containing the combined sequence and average similarity
Examples
sim1 <- list(sequence = c(0.8, 0.7, 0.9), average = 0.8)
sim2 <- list(sequence = c(0.6, 0.8, 0.7), average = 0.7)
combine_sim_seq(list(sim1, sim2))
print(plot)
Utility and visualization functions for speech similarity analysis
Description
This file contains utility functions and visualization tools to complement the main similarity calculation functions for comparing two speeches. Combine multiple similarity measures
Usage
combine_sims(similarities, weights = NULL)
Arguments
similarities |
A named list of similarity scores |
weights |
A named list of weights for each similarity measure (optional) |
Details
This function combines multiple similarity measures into a single score.
Value
A single combined similarity score
Examples
sims <- list(topic = 0.8, lexical = 0.6, semantic = 0.7, structural = 0.9)
combine_sims(sims)
combine_sims(sims, weights = list(topic = 2, lexical = 1, semantic = 1.5, structural = 1))
print(plot)
Compare multiple similarity measures
Description
This function compares multiple similarity measures for the same set of dyads.
Usage
compare_sim_meas(similarity_list, measure_names)
Arguments
similarity_list |
A list of lists, where each inner list contains similarities for each dyad |
measure_names |
A vector of names for each similarity measure |
Value
A data frame with all similarity measures for each dyad
Examples
topic_similarities <- list("1" = c(0.5, 0.6, 0.7), "2" = c(0.4, 0.5, 0.6))
lexical_similarities <- list("1" = c(0.6, 0.7, 0.8), "2" = c(0.5, 0.6, 0.7))
compare_sim_meas(
list(topic_similarities, lexical_similarities),
c("Topic", "Lexical")
)
print(plot)
Compare stylistic features
Description
This function visualizes the comparison of stylistic features between two speeches.
Usage
compare_style(stylistic_result)
Arguments
stylistic_result |
The result from stylistic_similarity function |
Value
A ggplot object
Examples
text1 <- "The quick brown fox jumps over the lazy dog. It's a sunny day."
text2 <- "A lazy cat sleeps on the warm windowsill. Birds chirp outside."
result <- stylistic_similarity(text1, text2)
compare_style(result)
print(plot)
Calculate Correlation Between Similarity Measures for a Single Dyad
Description
Calculate Correlation Between Similarity Measures for a Single Dyad
Usage
cor_sim_seq(similarities, method = "pearson")
Arguments
similarities |
A list of similarity measures for a single dyad |
method |
The correlation method to use (default is "pearson") |
Details
This function calculates the correlation between different similarity measures for a single dyad.
Value
A correlation matrix
Examples
sim1 <- list(sequence = c(0.8, 0.7, 0.9), average = 0.8)
sim2 <- list(sequence = c(0.6, 0.8, 0.7), average = 0.7)
cor_sim_seq(list(sim1, sim2))
print(plot)
Create windows from a conversation
Description
This function creates a list of windows from a conversation dataframe.
Usage
create_windows(conversation, window_size)
Arguments
conversation |
A dataframe containing the conversation, with a column named 'processed_text'. |
window_size |
An integer specifying the size of each window. |
Value
A list of character vectors, where each vector represents a window of text.
Examples
conversation <- data.frame(processed_text = c("hello", "world", "how", "are", "you"))
windows <- create_windows(conversation, 3)
Generate similarity report
Description
This function generates a comprehensive report of all similarity measures.
Usage
gen_sim_report(
speech1,
speech2,
topic_method = "lda",
semantic_method = "tfidf",
glove_path = NULL
)
Arguments
speech1 |
A character string representing the first speech |
speech2 |
A character string representing the second speech |
topic_method |
Method for topic similarity calculation ("lda" or "lsa") |
semantic_method |
Method for semantic similarity calculation ("tfidf", "word2vec", or "glove") |
glove_path |
Path to pre-trained GloVe file (if using "glove" method) |
Value
A list containing all similarity measures and visualizations
Examples
speech1 <- "This is the first speech. It talks about important topics."
speech2 <- "This is the second speech. It covers similar subjects."
report <- gen_sim_report(speech1, speech2)
Create Similarity Heatmap
Description
Create a heatmap of similarity measures for a single dyad
Usage
heatmap_sim(similarities, titles)
Arguments
similarities |
A list of similarity measures for a single dyad |
titles |
A character vector of titles for each similarity measure |
Details
This function creates a heatmap of multiple similarity measures for a single dyad.
Value
A ggplot object
Examples
sim1 <- list(sequence = c(0.5, 0.6, 0.7, 0.6, 0.8), average = 0.64)
sim2 <- list(sequence = c(0.4, 0.5, 0.6, 0.7, 0.7), average = 0.58)
similarities <- list(sim1, sim2)
titles <- c("Measure 1", "Measure 2")
# Plot multiple similarity measures
plot <- plot_sim_multi(similarities, titles)
print(plot)
Calculate lexical similarity sequence for a single dyad
Description
This function calculates lexical similarity over a sequence of conversation exchanges within a single dyad.
Usage
lex_sim_seq(conversation, window_size = 3)
Arguments
conversation |
A data frame representing the conversation |
window_size |
An integer specifying the size of the sliding window |
Value
A list containing the sequence of similarities and the average similarity
Examples
conversation <- data.frame(
processed_text = c("Hello world", "World of programming",
"Programming is fun", "Fun world of coding")
)
result <- lex_sim_seq(conversation, window_size = 2)
print(result)
Calculate lexical similarity for multiple dyads
Description
This function calculates lexical similarity over a sequence of conversation exchanges for multiple dyads.
Usage
lexical_sim_dyads(conversations, window_size = 3)
Arguments
conversations |
A data frame with columns 'dyad_id', 'speaker', and 'processed_text' |
window_size |
An integer specifying the size of the sliding window |
Value
A list containing the sequence of similarities for each dyad and the overall average similarity
Examples
library(lme4)
convs <- data.frame(
dyad_id = c(1, 1, 1, 1, 2, 2, 2, 2),
speaker = c("A", "B", "A", "B", "C", "D", "C", "D"),
processed_text = c("i love pizza", "me too favorite food",
"whats your favorite topping", "enjoy pepperoni mushrooms",
"i prefer pasta", "pasta delicious like spaghetti carbonara",
"ever tried making home", "yes quite easy make")
)
lexical_sim_dyads(convs, window_size = 2)
Calculate lexical similarity between two conversations
Description
This function calculates the lexical similarity between two conversations based on the overlap of unique words.
Usage
lexical_similarity(conv1, conv2)
Arguments
conv1 |
A character string representing the first conversation |
conv2 |
A character string representing the second conversation |
Value
A numeric value representing the lexical similarity
Examples
conv1 <- "The quick brown fox jumps over the lazy dog"
conv2 <- "The lazy dog sleeps under the quick brown fox"
lexical_similarity(conv1, conv2)
Normalize Similarity Scores
Description
Normalize similarity scores
Usage
norm_sim(similarities)
Arguments
similarities |
A numeric vector of similarity scores |
Details
This function normalizes similarity scores to a 0-1 range.
Value
A numeric vector of normalized similarity scores
Examples
similarities <- c(0.2, 0.5, 0.8, 1.0, 0.3)
norm_sim(similarities)
print(plot)
Calculate participant similarity for multiple dyads
Description
This function calculates an extended measure of participant similarity for multiple dyads.
Usage
participant_sim_dyads(conversations)
Arguments
conversations |
A data frame with columns 'dyad_id', 'speaker', and 'processed_text' |
Value
A list containing participant similarity for each dyad and the overall average similarity
Examples
convs <- data.frame(
dyad_id = c(1, 1, 1, 1, 2, 2, 2, 2),
speaker = c("A", "B", "A", "B", "C", "D", "C", "D"),
processed_text = c("i love pizza", "me too favorite food",
"whats your favorite topping", "enjoy pepperoni mushrooms",
"i prefer pasta", "pasta delicious like spaghetti carbonara",
"ever tried making home", "yes quite easy make")
)
participant_sim_dyads(convs)
Plot Correlation Heatmap for a Single Dyad
Description
Plot Correlation Heatmap for a Single Dyad
Usage
plot_cor_heatmap(cor_matrix, titles)
Arguments
cor_matrix |
A correlation matrix for a single dyad |
titles |
A character vector of titles for each similarity measure |
Details
This function creates a heatmap of correlations between similarity measures for a single dyad.
Value
A ggplot object
Examples
sim1 <- list(sequence = c(0.8, 0.7, 0.9), average = 0.8)
sim2 <- list(sequence = c(0.6, 0.8, 0.7), average = 0.7)
cor_matrix <- cor_sim_seq(list(sim1, sim2))
plot_cor_heatmap(cor_matrix, c("Topic", "Lexical"))
print(plot)
Plot comparison of multiple similarity measures
Description
This function creates a ggplot object comparing multiple similarity measures for the same set of dyads.
Usage
plot_sim_comp(comparison_df, title)
Arguments
comparison_df |
A data frame output from compare_sim_meas() |
title |
A string specifying the plot title |
Value
A ggplot object
Examples
topic_similarities <- list("1" = c(0.5, 0.6, 0.7), "2" = c(0.4, 0.5, 0.6))
lexical_similarities <- list("1" = c(0.6, 0.7, 0.8), "2" = c(0.5, 0.6, 0.7))
comparison_df <- compare_sim_meas(
list(topic_similarities, lexical_similarities),
c("Topic", "Lexical")
)
plot_sim_comp(comparison_df, "Comparison of Similarity Measures")
print(plot)
Plot heatmap of similarity measure correlations
Description
This function creates a ggplot object showing a heatmap of correlations between similarity measures.
Usage
plot_sim_cor_heatmap(cor_matrix, title)
Arguments
cor_matrix |
A correlation matrix output from calc_sim_cor() |
title |
A string specifying the plot title |
Value
A ggplot object
Examples
topic_similarities <- list("1" = c(0.5, 0.6, 0.7), "2" = c(0.4, 0.5, 0.6))
lexical_similarities <- list("1" = c(0.6, 0.7, 0.8), "2" = c(0.5, 0.6, 0.7))
comparison_df <- compare_sim_meas(
list(topic_similarities, lexical_similarities),
c("Topic", "Lexical")
)
cor_matrix <- calc_sim_cor(comparison_df)
plot_sim_cor_heatmap(cor_matrix, "Correlation of Similarity Measures")
print(plot)
Plot Multiple Similarity Measures
Description
Plot multiple similarity measures for a single dyad
Usage
plot_sim_multi(similarities, titles)
Arguments
similarities |
A list of similarity measures for a single dyad |
titles |
A character vector of titles for each similarity measure |
Details
This function creates a faceted plot of multiple similarity measures for a single dyad.
Value
A ggplot object
Examples
sim1 <- list(sequence = c(0.5, 0.6, 0.7, 0.6, 0.8), average = 0.64)
sim2 <- list(sequence = c(0.4, 0.5, 0.6, 0.7, 0.7), average = 0.58)
similarities <- list(sim1, sim2)
titles <- c("Measure 1", "Measure 2")
# Plot multiple similarity measures
plot <- plot_sim_multi(similarities, titles)
print(plot)
Plot Similarity Sequence
Description
Plot similarity sequence for a single dyad
Usage
plot_sim_seq(similarity, title)
Arguments
similarity |
A list containing the sequence of similarities and the average similarity |
title |
A character string for the plot title |
Details
This function creates a line plot of the similarity sequence for a single dyad.
Value
A ggplot object
Examples
sim_list <- list(
sequence = c(0.5, 0.6, 0.7, 0.6, 0.8),
average = 0.64
)
# Plot the similarity sequence
plot <- plot_sim_seq(sim_list, "Dyad Similarity Sequence")
print(plot)
Plot similarity over time for multiple dyads
Description
This function creates a ggplot object showing the similarity over time for multiple dyads.
Usage
plot_sim_time(similarities, title, y_label)
Arguments
similarities |
A list of similarity sequences for each dyad |
title |
A string specifying the plot title |
y_label |
A string specifying the y-axis label |
Value
A ggplot object
Examples
similarities <- list(
"1" = c(0.5, 0.6, 0.7),
"2" = c(0.4, 0.5, 0.6)
)
plot_sim_time(similarities, "Topic Similarity", "Similarity Score")
print(plot)
Visualize similarity scores
Description
This function creates a bar plot of similarity scores.
Usage
plot_sims(similarities)
Arguments
similarities |
A named list of similarity scores |
Value
A ggplot object
Examples
sims <- list(topic = 0.8, lexical = 0.6, semantic = 0.7, structural = 0.9)
plot_sims(sims)
Plot summary statistics for similarities
Description
This function creates a ggplot object showing summary statistics for similarities of multiple dyads.
Usage
plot_sum_stats(summary_stats, title)
Arguments
summary_stats |
A data frame with summary statistics for each dyad |
title |
A string specifying the plot title |
Value
A ggplot object
Examples
similarities <- list(
"1" = c(0.5, 0.6, 0.7),
"2" = c(0.4, 0.5, 0.6)
)
stats <- calc_sum_stats(similarities)
plot_sum_stats(stats, "Summary Statistics of Similarities")
print(plot)
Preprocess multiple dyad conversations
Description
This function preprocesses conversations from multiple dyads by applying text cleaning to each utterance.
Usage
preprocess_dyads(conversations)
Arguments
conversations |
A data frame with columns 'dyad_id', 'speaker', and 'text' |
Value
A data frame with an additional 'processed_text' column, removing any rows with empty processed text
Examples
convs <- data.frame(
dyad_id = c(1, 1, 2, 2),
speaker = c("A", "B", "C", "D"),
text = c("Hello!", "Hi there!", "How are you?", "I'm fine, thanks!")
)
preprocess_dyads(convs)
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
Preprocess text for analysis
Usage
preprocess_text(text)
Arguments
text |
A character string to be preprocessed |
Details
This function preprocesses the input text by converting to lowercase, removing punctuation and digits, and trimming whitespace.
Value
A preprocessed character string
Examples
text <- "Hello, World! This is an example text (with 123 numbers)."
preprocess_text(text)
Print similarity report
Description
This function prints a formatted summary of the similarity report.
Usage
print_sim_report(report)
Arguments
report |
A similarity report generated by gen_sim_report function |
Value
NULL (invisibly). This function is called for its side effect of printing to the console.
Examples
speech1 <- "This is the first speech. It talks about important topics."
speech2 <- "This is the second speech. It covers similar subjects."
report <- gen_sim_report(speech1, speech2)
print_sim_report(report)
Create Radar Chart of Average Similarities
Description
Create a radar chart of average similarities for a single dyad
Usage
radar_sim(similarities, titles)
Arguments
similarities |
A list of similarity measures for a single dyad |
titles |
A character vector of titles for each similarity measure |
Details
This function creates a radar chart of average similarities for multiple measures of a single dyad.
Value
A ggplot object
Examples
sim1 <- list(sequence = c(0.5, 0.6, 0.7, 0.6, 0.8), average = 0.64)
sim2 <- list(sequence = c(0.4, 0.5, 0.6, 0.7, 0.7), average = 0.58)
sim3 <- list(sequence = c(0.6, 0.7, 0.8, 0.7, 0.9), average = 0.74)
sim4 <- list(sequence = c(0.3, 0.4, 0.5, 0.6, 0.6), average = 0.48)
similarities <- list(sim1, sim2, sim3, sim4)
titles <- c("Measure 1", "Measure 2", "Measure 3", "Measure 4")
# Create radar chart
radar <- radar_sim(similarities, titles)
print(radar)
Run package examples
Description
Run package examples
Usage
run_example(example_name)
Arguments
example_name |
Name of the example file to run |
Value
No return value, called for side effects.
Examples
run_example("sequence_multidyads_examples.R")
run_example("main_functions_examples.R")
Calculate semantic similarity sequence for a single dyad
Description
This function calculates semantic similarity over a sequence of conversation exchanges within a single dyad.
Usage
sem_sim_seq(conversation, method = "tfidf", window_size = 3, ...)
Arguments
conversation |
A data frame representing the conversation |
method |
A character string specifying the method to use: "tfidf", "word2vec", or "glove" |
window_size |
An integer specifying the size of the sliding window |
... |
Additional arguments passed to semantic_similarity |
Value
A list containing the sequence of similarities and the average similarity
Examples
conversation <- data.frame(
processed_text = c("The weather is nice", "It's a beautiful day",
"The sun is shining", "Perfect day for a picnic")
)
result <- sem_sim_seq(conversation, method = "tfidf", window_size = 2)
print(result)
Calculate semantic similarity for multiple dyads
Description
This function calculates semantic similarity over a sequence of conversation exchanges for multiple dyads.
Usage
semantic_sim_dyads(conversations, method = "tfidf", window_size = 3, ...)
Arguments
conversations |
A data frame with columns 'dyad_id', 'speaker', and 'processed_text' |
method |
A character string specifying the method to use: "tfidf", "word2vec", or "glove" |
window_size |
An integer specifying the size of the sliding window |
... |
Additional arguments passed to semantic_similarity |
Value
A list containing the sequence of similarities for each dyad and the overall average similarity
Examples
library(lme4)
convs <- data.frame(
dyad_id = c(1, 1, 1, 1, 2, 2, 2, 2),
speaker = c("A", "B", "A", "B", "C", "D", "C", "D"),
processed_text = c("i love pizza", "me too favorite food",
"whats your favorite topping", "enjoy pepperoni mushrooms",
"i prefer pasta", "pasta delicious like spaghetti carbonara",
"ever tried making home", "yes quite easy make")
)
semantic_sim_dyads(convs, method = "tfidf", window_size = 2)
Calculate semantic similarity between two conversations
Description
This function calculates the semantic similarity between two conversations using either TF-IDF, Word2Vec, or GloVe embeddings approach.
Usage
semantic_similarity(
conversation1,
conversation2,
method = "tfidf",
model_path = NULL,
dim = 100,
window = 5,
iter = 5
)
Arguments
conversation1 |
A character string representing the first conversation |
conversation2 |
A character string representing the second conversation |
method |
A character string specifying the method to use: "tfidf", "word2vec", or "glove" |
model_path |
A character string specifying the path to pre-trained GloVe file (required for "glove" method) |
dim |
An integer specifying the dimensionality for Word2Vec embeddings (default: 100) |
window |
An integer specifying the window size for Word2Vec (default: 5) |
iter |
An integer specifying the number of iterations for Word2Vec (default: 5) |
Value
A numeric value representing the semantic similarity (between 0 and 1)
Examples
conv1 <- "The quick brown fox jumps over the lazy dog"
conv2 <- "A fast auburn canine leaps above an idle hound"
semantic_similarity(conv1, conv2, method = "tfidf")
Calculate sentiment similarity sequence for a single dyad
Description
This function calculates sentiment similarity over a sequence of conversation exchanges within a single dyad.
Usage
sent_sim_seq(conversation, window_size = 3)
Arguments
conversation |
A data frame representing the conversation |
window_size |
An integer specifying the size of the sliding window |
Value
A list containing the sequence of similarities and the average similarity
Examples
conversation <- data.frame(
processed_text = c("I love this movie!", "It's really amazing.",
"The acting is superb.", "I couldn't agree more.")
)
result <- sent_sim_seq(conversation, window_size = 2)
print(result)
Calculate sentiment similarity for multiple dyads
Description
This function calculates sentiment similarity over a sequence of conversation exchanges for multiple dyads.
Usage
sentiment_sim_dyads(conversations, window_size = 3)
Arguments
conversations |
A data frame with columns 'dyad_id', 'speaker', and 'processed_text' |
window_size |
An integer specifying the size of the sliding window |
Value
A list containing the sequence of similarities for each dyad and the overall average similarity
Examples
library(lme4)
convs <- data.frame(
dyad_id = c(1, 1, 1, 1, 2, 2, 2, 2),
speaker = c("A", "B", "A", "B", "C", "D", "C", "D"),
processed_text = c("i love pizza", "me too favorite food",
"whats your favorite topping", "enjoy pepperoni mushrooms",
"i prefer pasta", "pasta delicious like spaghetti carbonara",
"ever tried making home", "yes quite easy make")
)
sentiment_sim_dyads(convs, window_size = 2)
Calculate sentiment similarity between two conversations
Description
This function calculates the sentiment similarity between two conversations using the sentimentr package.
Usage
sentiment_similarity(conv1, conv2)
Arguments
conv1 |
A character string representing the first conversation |
conv2 |
A character string representing the second conversation |
Value
A numeric value representing the sentiment similarity
Examples
conv1 <- "I love this product! It's amazing and works great."
conv2 <- "This item is okay. It does the job but could be better."
sentiment_similarity(conv1, conv2)
Calculate structural similarity for multiple dyads
Description
This function calculates an extended measure of structural similarity for multiple dyads.
Usage
structural_sim_dyads(conversations)
Arguments
conversations |
A data frame with columns 'dyad_id', 'speaker', and 'processed_text' |
Value
A list containing structural similarity for each dyad and the overall average similarity
Examples
convs <- data.frame(
dyad_id = c(1, 1, 1, 1, 2, 2, 2, 2),
speaker = c("A", "B", "A", "B", "C", "D", "C", "D"),
processed_text = c("i love pizza", "me too favorite food",
"whats your favorite topping", "enjoy pepperoni mushrooms",
"i prefer pasta", "pasta delicious like spaghetti carbonara",
"ever tried making home", "yes quite easy make")
)
structural_sim_dyads(convs)
Calculate structural similarity between two conversations
Description
This function calculates the structural similarity between two conversations based on their length and average turn length.
Usage
structural_similarity(conv1, conv2)
Arguments
conv1 |
A character vector representing the first conversation |
conv2 |
A character vector representing the second conversation |
Value
A numeric value representing the structural similarity
Examples
conv1 <- c("Hello", "Hi there", "How are you?", "I'm fine, thanks")
conv2 <- c("Good morning", "Hello", "Nice day, isn't it?", "Yes, indeed")
structural_similarity(conv1, conv2)
Calculate stylistic similarity sequence for a single dyad
Description
This function calculates stylistic similarity over a sequence of conversation exchanges within a single dyad.
Usage
style_sim_seq(conversation, window_size = 3)
Arguments
conversation |
A data frame representing the conversation |
window_size |
An integer specifying the size of the sliding window |
Value
A list containing the sequence of similarities and the average similarity
Examples
conversation <- data.frame(
processed_text = c("How are you doing?", "I'm doing great, thanks!",
"That's wonderful to hear.", "I'm glad you're doing well.")
)
result <- style_sim_seq(conversation, window_size = 2)
print(result)
Calculate stylistic similarity for multiple dyads
Description
This function calculates stylistic similarity over a sequence of conversation exchanges for multiple dyads.
Usage
stylistic_sim_dyads(conversations, window_size = 3)
Arguments
conversations |
A data frame with columns 'dyad_id', 'speaker', and 'processed_text' |
window_size |
An integer specifying the size of the sliding window |
Value
A list containing the sequence of similarities for each dyad and the overall average similarity
Examples
convs <- data.frame(
dyad_id = c(1, 1, 1, 1, 2, 2, 2, 2),
speaker = c("A", "B", "A", "B", "C", "D", "C", "D"),
processed_text = c("i love pizza", "me too favorite food",
"whats your favorite topping", "enjoy pepperoni mushrooms",
"i prefer pasta", "pasta delicious like spaghetti carbonara",
"ever tried making home", "yes quite easy make")
)
stylistic_sim_dyads(convs, window_size = 2)
Calculate stylistic similarity between two conversations
Description
This function calculates various stylistic features and their similarity between two conversations.
Usage
stylistic_similarity(text1, text2)
Arguments
text1 |
A character string representing the first conversation |
text2 |
A character string representing the second conversation |
Value
A list containing stylistic features and similarity measures
Examples
text1 <- "The quick brown fox jumps over the lazy dog. It's a sunny day."
text2 <- "A lazy cat sleeps on the warm windowsill. Birds chirp outside."
stylistic_similarity(text1, text2)
Calculate timing similarity for multiple dyads
Description
This function calculates an extended measure of timing similarity for multiple dyads.
Usage
timing_sim_dyads(conversations)
Arguments
conversations |
A data frame with columns 'dyad_id', 'speaker', and 'processed_text' |
Value
A list containing timing similarity for each dyad and the overall average similarity
Examples
convs <- data.frame(
dyad_id = c(1, 1, 1, 1, 2, 2, 2, 2),
speaker = c("A", "B", "A", "B", "C", "D", "C", "D"),
processed_text = c("i love pizza", "me too favorite food",
"whats your favorite topping", "enjoy pepperoni mushrooms",
"i prefer pasta", "pasta delicious like spaghetti carbonara",
"ever tried making home", "yes quite easy make")
)
timing_sim_dyads(convs)
Calculate topic similarity for multiple dyads
Description
This function calculates topic similarity over a sequence of conversation exchanges for multiple dyads. It uses the Latent Dirichlet Allocation (LDA) method for topic modeling and the "slam" package for efficient handling of sparse matrices.
Usage
topic_sim_dyads(conversations, method = "lda", num_topics = 2, window_size = 3)
Arguments
conversations |
A data frame with columns 'dyad_id', 'speaker', and 'processed_text' |
method |
A character string specifying the method to use: currently only "lda" is supported |
num_topics |
An integer specifying the number of topics to use in the LDA model |
window_size |
An integer specifying the size of the sliding window |
Value
A list containing the sequence of similarities for each dyad and the overall average similarity
Examples
convs <- data.frame(
dyad_id = c(1, 1, 1, 1, 2, 2, 2, 2),
speaker = c("A", "B", "A", "B", "C", "D", "C", "D"),
processed_text = c("i love pizza", "me too favorite food",
"whats your favorite topping", "enjoy pepperoni mushrooms",
"i prefer pasta", "pasta delicious like spaghetti carbonara",
"ever tried making home", "yes quite easy make")
)
topic_sim_dyads(convs, method = "lda", num_topics = 2, window_size = 2)
Calculate topic similarity sequence for a single dyad
Description
This function calculates topic similarity over a sequence of conversation exchanges within a single dyad.
Usage
topic_sim_seq(conversation, method = "lda", num_topics = 2, window_size = 3)
Arguments
conversation |
A data frame representing the conversation |
method |
A character string specifying the method to use: "lda" or "lsa" |
num_topics |
An integer specifying the number of topics to use in the model |
window_size |
An integer specifying the size of the sliding window |
Value
A list containing the sequence of similarities and the average similarity
Examples
conversation <- data.frame(
processed_text = c("The cat sat on the mat", "The dog chased the cat",
"The mat was comfortable", "The cat liked the mat")
)
result <- topic_sim_seq(conversation, method = "lda", num_topics = 2, window_size = 2)
print(result)
Calculate topic similarity between two conversations
Description
This function calculates the topic similarity between two conversations using either Latent Dirichlet Allocation (LDA) or Latent Semantic Analysis (LSA).
Usage
topic_similarity(conv1, conv2, method = "lda", num_topics = 2)
Arguments
conv1 |
A character vector representing the first conversation |
conv2 |
A character vector representing the second conversation |
method |
A character string specifying the method to use: "lda" or "lsa" |
num_topics |
An integer specifying the number of topics to use in the model |
Value
A numeric value representing the topic similarity
Examples
conv1 <- c("I love pizza", "Pizza is my favorite food")
conv2 <- c("I prefer pasta", "Pasta is delicious")
topic_similarity(conv1, conv2, method = "lda", num_topics = 2)
topic_similarity(conv1, conv2, method = "lsa", num_topics = 2)