
# contentanalysis

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<p align="center">

<img src="https://raw.githubusercontent.com/massimoaria/contentanalysis/master/inst/www/ca_logo.jpg" width="400"/>

</p>

## Overview

`contentanalysis` is a comprehensive R package designed for in-depth
analysis of scientific literature. It bridges the gap between raw PDF
documents and structured, analyzable data by combining advanced text
extraction, citation analysis, and bibliometric enrichment from external
databases.

**AI-Enhanced PDF Import**: The package supports AI-assisted PDF text
extraction through Google’s Gemini API, enabling more accurate parsing
of complex document layouts. To use this feature, you need to obtain an
API key from [Google AI Studio](https://aistudio.google.com/apikey).

**Integration with bibliometrix**: This package complements the science
mapping analyses available in `bibliometrix` and its Shiny interface
`biblioshiny`. If you want to perform content analysis within a
user-friendly Shiny application with all the advantages of an
interactive interface, simply install `bibliometrix` and launch
`biblioshiny`, where you’ll find a dedicated **Content Analysis** menu
that implements all the analyses and outputs of this library.

### What Makes It Unique?

The package goes beyond simple PDF parsing by creating a multi-layered
analytical framework:

1.  **Intelligent PDF Processing**: Extracts text from multi-column PDFs
    while preserving document structure (sections, paragraphs,
    references)

2.  **Citation Intelligence**: Detects and extracts citations in
    multiple formats (numbered, author-year, narrative, parenthetical)
    and maps them to their precise locations in the document

3.  **Bibliometric Enrichment**: Automatically retrieves and integrates
    metadata from external sources:

- **CrossRef API**: Retrieves structured reference data including
  authors, publication years, journals, and DOIs
- **OpenAlex**: Enriches references with additional metadata, filling
  gaps and providing comprehensive bibliographic information

4.  **Citation-Reference Linking**: Implements sophisticated matching
    algorithms to connect in-text citations with their corresponding
    references, handling various citation styles and ambiguous cases

5.  **Context-Aware Analysis**: Extracts the textual context surrounding
    each citation, enabling semantic analysis of how references are used
    throughout the document

6.  **Network Visualization**: Creates interactive networks showing
    citation co-occurrence patterns and conceptual relationships within
    the document

### The Complete Workflow

    PDF Document → Text Extraction → Citation Detection → Reference Parsing
    ↓
    CrossRef/OpenAlex APIs
    ↓
    Citation-Reference Matching → Enriched Dataset
    ↓
    Network Analysis + Text Analytics + Bibliometric Indicators

The result is a rich, structured dataset that transforms a static PDF
into an analyzable knowledge object, ready for: - **Content analysis**:
Understanding what concepts and methods are discussed - **Citation
analysis**: Examining how knowledge is constructed and referenced -
**Temporal analysis**: Tracking the evolution of ideas through citation
patterns - **Network analysis**: Visualizing intellectual connections -
**Readability assessment**: Evaluating text complexity and accessibility

## Key Features

### PDF Import & Text Extraction

- Multi-column layout support with automatic section detection
- Structure preservation (title, abstract, introduction, methods,
  results, discussion, references)
- Handling of complex layouts and special characters
- DOI extraction from PDF metadata

### Citation Extraction & Analysis

- Comprehensive detection of citation formats:
  - **Numbered citations**: `[1]`, `[1-3]`, `[1,5,7]`
- **Author-year citations**: `(Smith, 2020)`, `(Smith et al., 2020)`
- **Narrative citations**: `Smith (2020) demonstrated...`
- **Complex citations**: `(see Smith, 2020; Jones et al., 2021)`
- Citation context extraction (surrounding text analysis)
- Citation positioning and density metrics
- Section-wise citation distribution

### Reference Management & Enrichment

- **Local parsing**: Extract references from the document’s reference
  section
- **CrossRef integration**: Retrieve structured metadata for cited works
  via DOI
- **OpenAlex integration**: Enrich references with additional
  bibliographic data
- Automatic gap-filling: Complete missing author names, years, journal
  names
- Structured reference format: Standardized author lists, publication
  years, journals

### Citation-Reference Matching

- Intelligent matching algorithms with multiple confidence levels:
  - **High confidence**: Exact author-year matches
  - **Medium confidence**: Fuzzy matching for variant author names
  - **Disambiguation**: Handles multiple works by the same author
- Support for various citation styles (APA, Chicago, Vancouver, etc.)
- Handles complex cases: multiple authors, “et al.”, year suffixes
  (2020a, 2020b)

### Network Analysis

- Interactive citation co-occurrence networks
- Distance-based edge weighting (closer citations = stronger
  connections)
- Section-aware visualization (color-coded by document section)
- Multi-section citation detection (citations appearing in multiple
  sections)
- Network statistics: centrality, clustering, community detection
  potential
- Citation cluster descriptions: TF-IDF analysis of reference titles by
  section
- Interactive plotly visualizations: bar charts, heatmaps, and reference
  density plots

### Text Analysis

- Word frequency analysis with stopword removal
- N-gram extraction (bigrams, trigrams)
- Lexical diversity metrics
- Readability indices (Flesch, Gunning Fog, SMOG, Coleman-Liau)
- Word distribution tracking across document sections
- Methodological term tracking

### Bibliometric Indicators

- Citation density (citations per 1000 words)
- Citation type distribution (narrative vs. parenthetical)
- Co-citation analysis
- Reference age distribution
- Journal diversity metrics

## Installation

You can install the development version from GitHub:

``` r
# install.packages("devtools")
devtools::install_github("massimoaria/contentanalysis")
```

## Example

Complete workflow analyzing a real scientific paper:

``` r
library(contentanalysis)
```

### Download example paper

The paper is an open access article by Aria et al.:

Aria, M., Cuccurullo, C., & Gnasso, A. (2021). A comparison among
interpretative proposals for Random Forests. Machine Learning with
Applications, 6, 100094.

``` r
paper_url <- "https://raw.githubusercontent.com/massimoaria/contentanalysis/master/inst/examples/example_paper.pdf"
download.file(paper_url, destfile = "example_paper.pdf", mode = "wb")
```

### Import PDF with automatic section detection

``` r
doc <- pdf2txt_auto("example_paper.pdf",
                    n_columns = 2,
                    citation_type = "author_year")
#> Stripped running header (6 occurrences, 40 chars)
#> Using 17 sections from PDF table of contents
#> Found 16 sections: Preface, Introduction, Related work, Internal processing approaches, Random forest extra information, Visualization toolkits, Post-Hoc approaches, Size reduction, Rule extraction, Local explanation, Comparison study, Experimental design, Analysis, Conclusion, Acknowledgment, References
#> Normalized 77 references with consistent \n\n separators

# Check detected sections
names(doc)
#>  [1] "Full_text"                       "Preface"                        
#>  [3] "Introduction"                    "Related work"                   
#>  [5] "Internal processing approaches"  "Random forest extra information"
#>  [7] "Visualization toolkits"          "Post-Hoc approaches"            
#>  [9] "Size reduction"                  "Rule extraction"                
#> [11] "Local explanation"               "Comparison study"               
#> [13] "Experimental design"             "Analysis"                       
#> [15] "Conclusion"                      "Acknowledgment"                 
#> [17] "References"
```

### Perform comprehensive content analysis with CrossRef and OpenAlex enrichment

``` r
analysis <- analyze_scientific_content(
  text = doc,
  doi = "10.1016/j.mlwa.2021.100094",
  mailto = "your@email.com",
  citation_type = "author_year"
)
#> Extracting author-year citations only
#> Attempting to retrieve references from CrossRef...
#> Successfully retrieved 33 references from CrossRef
#> Fetching Open Access metadata for 14 DOIs from OpenAlex...
#> Successfully retrieved metadata for 13 references from OpenAlex
```

This single function call:

1.  Extracts all citations from the document
2.  Retrieves reference metadata from CrossRef using the paper’s DOI
3.  Enriches references with additional data from OpenAlex
4.  Matches citations to references with confidence scoring
5.  Performs text analysis and computes bibliometric indicators

### View summary statistics

``` r
analysis$summary
#> $total_words_analyzed
#> [1] 3453
#> 
#> $unique_words
#> [1] 1310
#> 
#> $citations_extracted
#> [1] 49
#> 
#> $narrative_citations
#> [1] 15
#> 
#> $parenthetical_citations
#> [1] 34
#> 
#> $complex_citations_parsed
#> [1] 12
#> 
#> $lexical_diversity
#> [1] 0.3793802
#> 
#> $average_citation_context_length
#> [1] 3230.429
#> 
#> $citation_density_per_1000_words
#> [1] 6.47
#> 
#> $references_parsed
#> [1] 33
#> 
#> $citations_matched_to_refs
#> [1] 42
#> 
#> $match_quality
#> # A tibble: 3 × 3
#>   match_confidence     n percentage
#>   <chr>            <int>      <dbl>
#> 1 high                42       85.7
#> 2 no_match_author      6       12.2
#> 3 no_match_year        1        2  
#> 
#> $citation_type_used
#> [1] "author_year"
```

### Readability indices

``` r
readability <- calculate_readability_indices(doc$Full_text, detailed = TRUE)
readability
#> # A tibble: 1 × 12
#>   flesch_kincaid_grade flesch_reading_ease automated_readability_index
#>                  <dbl>               <dbl>                       <dbl>
#> 1                 12.7                33.3                        12.1
#> # ℹ 9 more variables: gunning_fog_index <dbl>, n_sentences <int>,
#> #   n_words <int>, n_syllables <dbl>, n_characters <int>,
#> #   n_complex_words <int>, avg_sentence_length <dbl>,
#> #   avg_syllables_per_word <dbl>, pct_complex_words <dbl>
```

### Examine citations by type

``` r
analysis$citation_metrics$type_distribution
#> # A tibble: 11 × 3
#>    citation_type                   n percentage
#>    <chr>                       <int>      <dbl>
#>  1 parsed_from_multiple           12      24.5 
#>  2 author_year_basic               9      18.4 
#>  3 narrative_etal                  7      14.3 
#>  4 author_year_and                 6      12.2 
#>  5 author_year_etal                3       6.12
#>  6 narrative_three_authors_and     3       6.12
#>  7 narrative_two_authors_and       3       6.12
#>  8 narrative_four_authors_and      2       4.08
#>  9 see_citations                   2       4.08
#> 10 author_year_ampersand           1       2.04
#> 11 doi_pattern                     1       2.04
```

### Analyze citation contexts

``` r
head(analysis$citation_contexts[, c("citation_text_clean", "section", "full_context")])
#> # A tibble: 6 × 3
#>   citation_text_clean                                       section full_context
#>   <chr>                                                     <chr>   <chr>       
#> 1 (Mitchell, 1997)                                          Introd… systems ide…
#> 2 https://doi.org/10.1016/j.mlwa.2021.100094                Introd… interpretat…
#> 3 (Breiman, 2001)                                           Introd… random subs…
#> 4 (see Breiman, 1996)                                       Introd… model that …
#> 5 (Hastie, supervised learning (Breiman, Friedman, Tibshir… Introd… by calculat…
#> 6 (Hastie et al., 2009)                                     Introd… but it is n…
```

## Citation Network Visualization

Create interactive network visualizations showing how citations co-occur
within your document:

``` r
# Create citation network
network <- create_citation_network(
  citation_analysis_results = analysis,
  max_distance = 800,          # Max distance between citations (characters)
  min_connections = 2,          # Minimum connections to include a node
  show_labels = TRUE
)

# Display interactive network
network
```

<img src="man/figures/README-unnamed-chunk-10-1.png" width="100%" />

### Access network statistics

``` r
stats <- attr(network, "stats")

# Network size
cat("Nodes:", stats$n_nodes, "\n")
#> Nodes: 28
cat("Edges:", stats$n_edges, "\n")
#> Edges: 49
cat("Average distance:", stats$avg_distance, "characters\n")
#> Average distance: 245.4 characters

# Citations by section
print(stats$section_distribution)
#>                   primary_section n
#> 1                    Related work 6
#> 2                    Introduction 4
#> 3                  Size reduction 4
#> 4             Experimental design 3
#> 5 Random forest extra information 3
#> 6          Visualization toolkits 3
#> 7               Local explanation 2
#> 8                 Rule extraction 2
#> 9                        Analysis 1

# Multi-section citations
if (nrow(stats$multi_section_citations) > 0) {
  print(stats$multi_section_citations)
}
#>                 citation_text
#> 1             (Breiman, 2001)
#> 2 (Haddouchi & Berrado, 2019)
#> 3         (Meinshausen, 2010)
#> 4                (Deng, 2019)
#>                                                         sections n_sections
#> 1                  Introduction, Random forest extra information          2
#> 2 Related work, Random forest extra information, Rule extraction          3
#> 3                    Rule extraction, Comparison study, Analysis          3
#> 4                    Rule extraction, Comparison study, Analysis          3
```

### Network Features

The citation network visualization includes:

- **Node size**: Proportional to number of connections
- **Node color**: Indicates the primary section where citations appear
- **Node border**: Thicker border (3px) for citations appearing in
  multiple sections
- **Edge thickness**: Decreases with distance (closer citations =
  thicker edges)
- **Edge color**:
  - Red: Very close citations (≤300 characters)
- Blue: Moderate distance (≤600 characters)
- Gray: Distant citations (\>600 characters)
- **Interactive features**: Zoom, pan, drag nodes, highlight neighbors
  on hover

### Customizing the Network

``` r
# Focus on very close citations only
network_close <- create_citation_network(
  analysis,
  max_distance = 300,
  min_connections = 1
)

# Show only highly connected citations
network_hubs <- create_citation_network(
  analysis,
  max_distance = 1000,
  min_connections = 5
)

# Hide labels for cleaner visualization
network_clean <- create_citation_network(
  analysis,
  show_labels = FALSE
)
```

## Citation Cluster Description

Describe the thematic focus of each section’s bibliography using TF-IDF
analysis of reference titles:

``` r
# Generate cluster descriptions
cluster_desc <- describe_citation_clusters(analysis, top_n = 10)

# View summary: top terms per section
cluster_desc$cluster_summary
#> # A tibble: 10 × 3
#>    section                         n_references top_terms                       
#>    <chr>                                  <int> <chr>                           
#>  1 Introduction                               4 learning, machine, machine lear…
#>  2 Related work                               8 black, black box, box, acm, sur…
#>  3 Random forest extra information            5 forests, random forests, annals…
#>  4 Visualization toolkits                     4 analytics, analytics ieee, comp…
#>  5 Size reduction                             4 adaptive, adaptive diagnostic, …
#>  6 Rule extraction                            2 annals applied, applied, applie…
#>  7 Local explanation                          3 models, classification, classif…
#>  8 Comparison study                           1 annals applied, applied, applie…
#>  9 Experimental design                        3 bell, bell laboratories, labora…
#> 10 Analysis                                   3 domains, domains acm, imbalance…

# View detailed TF-IDF scores
cluster_desc$cluster_descriptions
#> # A tibble: 83 × 7
#>    section      ngram              ngram_size     n     tf   idf tf_idf
#>    <chr>        <chr>                   <int> <int>  <dbl> <dbl>  <dbl>
#>  1 Introduction learning                    1     2 0.143   1.20 0.172 
#>  2 Introduction machine                     1     2 0.143   1.20 0.172 
#>  3 Introduction machine learning            2     2 0.143   1.20 0.172 
#>  4 Introduction bagging                     1     1 0.0714  2.30 0.164 
#>  5 Introduction bagging predictors          2     1 0.0714  2.30 0.164 
#>  6 Introduction predictors                  1     1 0.0714  2.30 0.164 
#>  7 Introduction predictors machine          2     1 0.0714  2.30 0.164 
#>  8 Introduction forests machine             2     1 0.0714  1.61 0.115 
#>  9 Introduction forests                     1     1 0.0714  1.20 0.0860
#> 10 Introduction random forests              2     1 0.0714  1.20 0.0860
#> # ℹ 73 more rows
```

### Visualize Citation Clusters

Create interactive plotly visualizations that complement the citation
network:

**1. TF-IDF terms per section (2-column grid layout)**

<img src="man/figures/README-tfidf-bars.png" width="100%" />

**2. Heatmap: terms vs sections (unique vs shared terms)**

<img src="man/figures/README-tfidf-heatmap.png" width="100%" />

**3. References per section**

<img src="man/figures/README-refs-per-section.png" width="100%" />

The TF-IDF bar chart uses a 2-column grid layout with color-coded
section titles for a compact, readable overview. All plots display
sections in the order they appear in the paper, use consistent styling,
and include interactive hover information. Colors match the section
colors used in the citation network.

## Text Analysis

### Track methodological terms across sections

``` r
method_terms <- c("machine learning", "regression", "validation", "dataset")
word_dist <- calculate_word_distribution(doc, method_terms)
```

### Create interactive visualization

<img src="man/figures/README-word-distribution.png" width="100%" />

### Examine most frequent words

``` r
head(analysis$word_frequencies, 10)
#> # A tibble: 10 × 4
#>    word         n frequency  rank
#>    <chr>    <int>     <dbl> <int>
#>  1 model       45   0.0130      1
#>  2 forest      42   0.0122      2
#>  3 accuracy    40   0.0116      3
#>  4 trees       38   0.0110      4
#>  5 random      34   0.00985     5
#>  6 learning    27   0.00782     6
#>  7 set         27   0.00782     7
#>  8 variable    26   0.00753     8
#>  9 data        25   0.00724     9
#> 10 rule        25   0.00724    10
```

### Citation co-occurrence data

``` r
head(analysis$network_data)
#> # A tibble: 6 × 5
#>   citation1                                       citation2 distance type1 type2
#>   <chr>                                           <chr>        <int> <chr> <chr>
#> 1 (Mitchell, 1997)                                https://…      734 auth… doi_…
#> 2 (Breiman, 2001)                                 (see Bre…      318 auth… see_…
#> 3 (Breiman, 2001)                                 (Hastie,…      734 auth… auth…
#> 4 (see Breiman, 1996)                             (Hastie,…      398 see_… auth…
#> 5 (see Breiman, 1996)                             (Hastie …      685 see_… auth…
#> 6 (Hastie, supervised learning (Breiman, Friedma… (Hastie …      210 auth… auth…
```

## Working with References

### Exploring enriched reference data

``` r
# View parsed references (enriched with CrossRef and OpenAlex)
head(analysis$parsed_references[, c("ref_first_author", "ref_year", "ref_journal", "ref_source")])
#>   ref_first_author ref_year           ref_journal ref_source
#> 1            Adadi     2018           IEEE Access   crossref
#> 2             <NA>     <NA>                  <NA>   crossref
#> 3           Branco     2016 ACM Computing Surveys   crossref
#> 4          Breiman     1996      Machine Learning   crossref
#> 5          Breiman     2001      Machine Learning   crossref
#> 6          Breiman     1984   International Group   crossref

# Check data sources
table(analysis$parsed_references$ref_source)
#> 
#> crossref 
#>       33
```

The `ref_source` column indicates where the data came from:

- `"crossref"`: Retrieved from CrossRef API
- `"parsed"`: Extracted from document’s reference section
- References may be enriched with OpenAlex data even if originally from
  CrossRef

### Analyzing citation-reference links

``` r
# View citation-reference matches with confidence levels
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
head(analysis$citation_references_mapping[, c("citation_text_clean", "ref_authors",
                                               "ref_year", "match_confidence")])
#> # A tibble: 6 × 4
#>   citation_text_clean                      ref_authors ref_year match_confidence
#>   <chr>                                    <chr>       <chr>    <chr>           
#> 1 (Mitchell, 1997)                         Mitchell    1997     high            
#> 2 https://doi.org/10.1016/j.mlwa.2021.100… <NA>        <NA>     no_match_year   
#> 3 (Breiman, 2001)                          Breiman, L. 2001     high            
#> 4 (see Breiman, 1996)                      Breiman, L. 1996     high            
#> 5 (Hastie, supervised learning (Breiman, … Hastie      2009     high            
#> 6 (Hastie et al., 2009)                    Hastie      2009     high

# Match quality distribution
table(analysis$citation_references_mapping$match_confidence)
#> 
#>            high no_match_author   no_match_year 
#>              42               6               1
```

### Finding citations to specific authors

``` r
# Find all citations to works by Smith
analysis$citation_references_mapping %>%
  filter(grepl("Smith", ref_authors, ignore.case = TRUE)) %>%
  select(citation_text_clean, ref_full_text, match_confidence)
#> # A tibble: 0 × 3
#> # ℹ 3 variables: citation_text_clean <chr>, ref_full_text <chr>,
#> #   match_confidence <chr>
```

### Accessing OpenAlex metadata

``` r
# If OpenAlex data was retrieved
if (!is.null(analysis$references_oa)) {
  # View enriched metadata
  head(analysis$references_oa[, c("title", "publication_year", "cited_by_count",
                                   "type", "oa_status")])

  # Analyze citation impact
  summary(analysis$references_oa$cited_by_count)
}
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>     100     205    1056   12429    5399  119748
```

### Citations by section

``` r
analysis$citation_metrics$section_distribution
#> # A tibble: 15 × 3
#>    section                             n percentage
#>    <fct>                           <int>      <dbl>
#>  1 Preface                             0       0   
#>  2 Introduction                        6      12.2 
#>  3 Related work                        9      18.4 
#>  4 Internal processing approaches      0       0   
#>  5 Random forest extra information     6      12.2 
#>  6 Visualization toolkits              4       8.16
#>  7 Post-Hoc approaches                 0       0   
#>  8 Size reduction                      6      12.2 
#>  9 Rule extraction                     3       6.12
#> 10 Local explanation                   5      10.2 
#> 11 Comparison study                    2       4.08
#> 12 Experimental design                 4       8.16
#> 13 Analysis                            4       8.16
#> 14 Conclusion                          0       0   
#> 15 Acknowledgment                      0       0
```

## Advanced: Word Distribution Analysis

``` r
# Track disease-related terms
disease_terms <- c("covid", "pandemic", "health", "policy", "vaccination")
dist <- calculate_word_distribution(doc, disease_terms, use_sections = TRUE)

# View frequencies by section
dist %>%
  select(segment_name, word, count, percentage) %>%
  arrange(segment_name, desc(percentage))
#> # A tibble: 1 × 4
#>   segment_name word   count percentage
#>   <chr>        <chr>  <int>      <dbl>
#> 1 Conclusion   health     1      0.331

# Visualize trends
#plot_word_distribution(dist, plot_type = "area", smooth = FALSE)
```

## Main Functions

### PDF Import

- `pdf2txt_auto()`: Import PDF with automatic section detection
- `reconstruct_text_structured()`: Advanced text reconstruction
- `extract_doi_from_pdf()`: Extract DOI from PDF metadata

### Content Analysis

- `analyze_scientific_content()`: Comprehensive content and citation
  analysis with API enrichment
- `parse_references_section()`: Parse reference list from text
- `match_citations_to_references()`: Match citations to references with
  confidence scoring
- `get_crossref_references()`: Retrieve references from CrossRef API

### Network Analysis

- `create_citation_network()`: Create interactive citation co-occurrence
  network
- `describe_citation_clusters()`: Describe citation clusters by section
  using TF-IDF on reference titles
- `plot_citation_clusters()`: Create interactive plotly visualizations
  of citation cluster descriptions (TF-IDF bars, heatmap, references per
  section)

### Text Analysis

- `calculate_readability_indices()`: Compute readability scores (Flesch,
  Gunning Fog, SMOG, Coleman-Liau)
- `calculate_word_distribution()`: Track word frequencies across
  document sections
- `readability_multiple()`: Batch readability analysis for multiple
  documents

### Visualization

- `plot_word_distribution()`: Interactive visualization of word
  distribution across sections
- `plot_citation_clusters()`: Interactive TF-IDF bar charts, heatmaps,
  and reference density plots

### Utilities

- `get_example_paper()`: Download example paper for testing
- `map_citations_to_segments()`: Map citations to document
  sections/segments

## External Data Sources

### CrossRef API

The package integrates with CrossRef’s REST API to retrieve structured
bibliographic data:

- **Endpoint**: `https://api.crossref.org/works/{doi}/references`
- **Data retrieved**: Authors, publication year, journal/source, article
  title, DOI
- **Rate limits**: Polite pool requires email (use `mailto` parameter)
- **More info**: <https://api.crossref.org>

### OpenAlex API

OpenAlex provides comprehensive scholarly metadata:

- **Endpoint**: Via `openalexR` package
- **Data retrieved**: Complete author lists, citation counts, open
  access status, institutional affiliations
- **Rate limits**: 100,000 requests/day (polite pool with email), 10
  requests/second
- **API key**: Optional, increases rate limits. Set with
  `openalexR::oa_apikey()`
- **More info**: <https://openalex.org>

### Setting Up API Access

#### Email for polite pool (recommended)

Both CrossRef and OpenAlex offer a **polite pool** for users who provide
an email address. This gives you faster and more reliable access
compared to anonymous requests. Simply pass your email via the `mailto`
parameter:

``` r
analysis <- analyze_scientific_content(
  text = doc,
  doi = "10.xxxx/xxxxx",
  mailto = "your@email.com"  # Your email for polite pool access
)
```

The `mailto` parameter is used by both CrossRef and OpenAlex to route
your requests through their polite pool, which provides higher rate
limits and priority access.

#### OpenAlex API key (optional, for extended use)

For heavier usage, OpenAlex offers a free API key that further increases
rate limits (from 10 to 100 requests/second). This is recommended if you
plan to analyze many documents in batch.

1.  Get your free API key at: <https://openalex.org/users>
2.  Set it in your R session before running the analysis:

``` r
openalexR::oa_apikey("your-api-key-here")
```

You can also add this to your `.Rprofile` so it’s automatically set at
startup:

``` r
# Add to ~/.Rprofile
openalexR::oa_apikey("your-api-key-here")
```

## Dependencies

**Core**: pdftools, dplyr, tidyr, stringr, tidytext, tibble, httr2,
visNetwork, openalexR

**Suggested**: plotly, RColorBrewer, scales (for visualization)

## Citation

If you use this package in your research, please cite:

    Massimo Aria & Corrado Cuccurullo (2025). contentanalysis: Scientific Content and Citation Analysis from PDF Documents.
    R package version 0.2.0.
    https://doi.org/10.32614/CRAN.package.contentanalysis

## License

GPL (\>= 3)

## Issues and Contributions

Please report issues at:
<https://github.com/massimoaria/contentanalysis/issues>

Contributions are welcome! Please feel free to submit a Pull Request.
