Introduction

This vignette demonstrates the usage of various similarity functions for analyzing speeches. We’ll be using example data speeches_data stored in inst/extdata to showcase these functions.

First, let’s load the example data:

data_path <- system.file("extdata", "speeches_data.Rdata", package = "conversim")
load(data_path)

# Print a summary of the speeches data
print(summary(speeches_data))

##   speaker_id            text          
##  Length:2           Length:2          
##  Class :character   Class :character  
##  Mode  :character   Mode  :character

Preprocessing Text

Before we begin with the similarity functions, let’s look at the preprocess_text function:

# Example usage with our data
original_text <- substr(speeches_data$text[1], 1, 200)  # First 200 characters of speech A
preprocessed_text <- preprocess_text(original_text)
print(paste("Original:", original_text))

## [1] "Original: Ladies and Gentlemen, Distinguished Guests,\n\nToday, I stand before you to address one of the most pressing challenges of our time—climate change. What was once a distant concern is now an undeniable r"

print(paste("Preprocessed:", preprocessed_text))

## [1] "Preprocessed: ladies and gentlemen distinguished guests today i stand before you to address one of the most pressing challenges of our timeclimate change what was once a distant concern is now an undeniable r"

Topic Similarity

The topic_similarity function calculates the similarity between two speeches based on their topics:

# Example usage with our speeches data
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)

print(paste("LDA Similarity:", lda_similarity))

## [1] "LDA Similarity: 0.169419269706043"

print(paste("LSA Similarity:", lsa_similarity))

## [1] "LSA Similarity: 1"

Note: The difference between LDA (Latent Dirichlet Allocation) topic similarity (0.1694) and LSA (Latent Semantic Analysis) topic similarity (1) can be attributed to several factors:

1. Different Algorithms

LDA and LSA use fundamentally different approaches for topic modeling and semantic analysis:

LDA is a probabilistic model that assumes documents are mixtures of topics, and topics are mixtures of words. It aims to reverse-engineer the underlying topic structure that could have generated the observed documents.
LSA, by contrast, relies on singular value decomposition (SVD) of the term-document matrix, reducing its dimensionality to uncover latent semantic structures.

2. Possible Reasons for LSA’s High Similarity Score

Dimensionality: If too few topics (dimensions) were chosen for LSA, the semantic space might have been oversimplified, leading to an artificially high similarity score.
Corpus Size: LSA can be sensitive to the size of the corpus. With only two documents, there may not be enough data for LSA to create a meaningful semantic space.
Common Vocabulary: Both speeches discuss climate change, and the use of similar high-level vocabulary could lead LSA to treat them as highly similar, especially in a small corpus.
Implementation Issue: There could be a problem with how cosine similarity was calculated or normalized in the LSA implementation.

3. Sensitivity to Input Parameters

Both LDA and LSA are sensitive to the input parameters, especially the number of topics chosen. The code used five topics for both methods, which may have been more appropriate for LDA than for LSA in this particular case.

4. Nature of the Data

Although both speeches are about climate change, they focus on different aspects of the topic. LDA might be better suited to capture these nuanced differences in topic distribution, whereas LSA may oversimplify the analysis due to the shared overall theme and vocabulary.

Lexical Similarity

The lexical_similarity function calculates the similarity between two speeches based on their shared unique words:

# Example usage with our speeches data
lex_similarity <- lexical_similarity(speeches_data$text[1], speeches_data$text[2])
print(paste("Lexical Similarity:", lex_similarity))

## [1] "Lexical Similarity: 0.15180265654649"

Semantic Similarity

The semantic_similarity function calculates the semantic similarity between two speeches using different methods:

# Example usage with our speeches data
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")

print(paste("TF-IDF Similarity:", tfidf_similarity))

## [1] "TF-IDF Similarity: 0.5"

print(paste("Word2Vec Similarity:", word2vec_similarity))

## [1] "Word2Vec Similarity: 0.998952039779365"

# Note: For GloVe method, you need to provide a path to pre-trained GloVe vectors
# glove_similarity <- semantic_similarity(speeches_data$text[1], speeches_data$text[2], method = "glove", model_path = "path/to/glove/vectors.txt")

Structural Similarity

The structural_similarity function calculates the similarity between two speeches based on their structure:

# Example usage with our speeches data
struct_similarity <- structural_similarity(strsplit(speeches_data$text[1], "\n")[[1]], 
                                           strsplit(speeches_data$text[2], "\n")[[1]])
print(paste("Structural Similarity:", struct_similarity))

## [1] "Structural Similarity: 0.889420039965884"

Stylistic Similarity

The stylistic_similarity function calculates various stylistic features and their similarity between two speeches:

# Example usage with our speeches data
style_similarity <- stylistic_similarity(speeches_data$text[1], speeches_data$text[2])
print("Stylistic Similarity Results:")

## [1] "Stylistic Similarity Results:"

print(style_similarity)

## $text1_features
##                 ttr avg_sentence_length            fk_grade 
##            0.644186           23.888889           19.878760 
## 
## $text2_features
##                 ttr avg_sentence_length            fk_grade 
##           0.5490849          23.1153846          17.0446339 
## 
## $feature_differences
##                 ttr avg_sentence_length            fk_grade 
##          0.09510119          0.77350427          2.83412575 
## 
## $overall_similarity
## [1] 0.8924734
## 
## $cosine_similarity
## [1] 0.9949162

Sentiment Similarity

The sentiment_similarity function calculates the sentiment similarity between two speeches:

# Example usage with our speeches data
sent_similarity <- sentiment_similarity(speeches_data$text[1], speeches_data$text[2])
print(paste("Sentiment Similarity:", sent_similarity))

## [1] "Sentiment Similarity: 0.952602694643716"

Analyzing Similarities between Two Long Speeches