# Emoji Analysis in YouTube Comments

Emojis are increasingly important in social media communication. This vignette demonstrates how to analyze emoji usage patterns in YouTube comments using `tuber`’s built-in emoji functions.

## Setup

    library(tuber)
    library(dplyr)
    library(ggplot2)

## Collecting Comments

    yt_oauth("your_app_id", "your_app_secret")

    comments <- get_all_comments(video_id = "your_video_id", max_results = 500)

## Basic Emoji Analysis

### Emoji Presence and Counts

    comments <- comments |>
      mutate(
        has_emoji = has_emoji(textDisplay),
        emoji_count = count_emojis(textDisplay)
      )

    summary(comments$emoji_count)

    emoji_rate <- mean(comments$has_emoji, na.rm = TRUE) * 100
    cat("Comments with emojis:", round(emoji_rate, 1), "%\n")

### Distribution of Emoji Usage

    comments |>
      filter(emoji_count > 0) |>
      ggplot(aes(x = emoji_count)) +
      geom_histogram(binwidth = 1, fill = "steelblue", color = "white") +
      labs(
        title = "Distribution of Emojis per Comment",
        x = "Number of Emojis",
        y = "Number of Comments"
      ) +
      theme_minimal()

## Emoji Frequency Analysis

### Top Emojis

    all_emojis <- unlist(extract_emojis(comments$textDisplay))

    emoji_freq <- as.data.frame(table(all_emojis), stringsAsFactors = FALSE)
    names(emoji_freq) <- c("emoji", "count")
    emoji_freq <- emoji_freq[order(-emoji_freq$count), ]

    head(emoji_freq, 15)

    emoji_freq |>
      head(10) |>
      ggplot(aes(x = reorder(emoji, count), y = count)) +
      geom_col(fill = "steelblue") +
      coord_flip() +
      labs(
        title = "Top 10 Most Used Emojis",
        x = "Emoji",
        y = "Count"
      ) +
      theme_minimal()

## Temporal Analysis

### Emoji Usage Over Time

    comments <- comments |>
      mutate(
        date = as.Date(publishedAt),
        emoji_count = count_emojis(textDisplay)
      )

    daily_emoji <- comments |>
      group_by(date) |>
      summarise(
        total_comments = n(),
        comments_with_emoji = sum(has_emoji, na.rm = TRUE),
        total_emojis = sum(emoji_count, na.rm = TRUE),
        emoji_rate = comments_with_emoji / total_comments * 100,
        avg_emojis = total_emojis / total_comments
      )

    ggplot(daily_emoji, aes(x = date, y = emoji_rate)) +
      geom_line(color = "steelblue") +
      geom_smooth(method = "loess", se = TRUE, alpha = 0.2) +
      labs(
        title = "Emoji Usage Rate Over Time",
        x = "Date",
        y = "% of Comments with Emojis"
      ) +
      theme_minimal()

## Sentiment Categories

Emojis can indicate sentiment. Here’s a simple categorization approach:

    positive_emojis <- c(
      "\U0001F600", "\U0001F601", "\U0001F602", "\U0001F603", "\U0001F604",
      "\U0001F605", "\U0001F606", "\U0001F60A", "\U0001F60D", "\U0001F618",
      "\U0001F44D", "\U0001F44F", "\U00002764", "\U0001F389", "\U0001F38A"
    )

    negative_emojis <- c(
      "\U0001F620", "\U0001F621", "\U0001F622", "\U0001F623", "\U0001F624",
      "\U0001F625", "\U0001F62D", "\U0001F44E", "\U0001F4A9", "\U0001F61E"
    )

    comments <- comments |>
      mutate(
        emojis = extract_emojis(textDisplay),
        pos_emoji = sapply(emojis, function(e) sum(e %in% positive_emojis)),
        neg_emoji = sapply(emojis, function(e) sum(e %in% negative_emojis)),
        emoji_sentiment = case_when(
          pos_emoji > neg_emoji ~ "positive",
          neg_emoji > pos_emoji ~ "negative",
          pos_emoji == 0 & neg_emoji == 0 ~ "none",
          TRUE ~ "neutral"
        )
      )

    table(comments$emoji_sentiment)

## Engagement Correlation

### Do emoji comments get more likes?

    engagement_summary <- comments |>
      group_by(has_emoji) |>
      summarise(
        n = n(),
        mean_likes = mean(likeCount, na.rm = TRUE),
        median_likes = median(likeCount, na.rm = TRUE)
      )

    print(engagement_summary)

    ggplot(comments, aes(x = has_emoji, y = likeCount + 1)) +
      geom_boxplot(fill = "steelblue", alpha = 0.7) +
      scale_y_log10() +
      labs(
        title = "Like Counts: Emoji vs Non-Emoji Comments",
        x = "Contains Emoji",
        y = "Likes (log scale)"
      ) +
      theme_minimal()

## Cross-Video Comparison

### Compare emoji usage across videos

    video_ids <- c("video_id_1", "video_id_2", "video_id_3")

    all_comments <- lapply(video_ids, function(vid) {
      comments <- get_all_comments(video_id = vid, max_results = 200)
      comments$video_id <- vid
      comments
    })
    all_comments <- bind_rows(all_comments)

    video_emoji_stats <- all_comments |>
      mutate(emoji_count = count_emojis(textDisplay)) |>
      group_by(video_id) |>
      summarise(
        total_comments = n(),
        emoji_rate = mean(emoji_count > 0) * 100,
        avg_emojis = mean(emoji_count)
      )

    print(video_emoji_stats)

## Working with Clean Text

For text analysis that should exclude emojis:

    comments <- comments |>
      mutate(
        clean_text = remove_emojis(textDisplay),
        clean_text = trimws(gsub("\\s+", " ", clean_text))
      )

    head(comments$clean_text[comments$has_emoji], 3)

## Performance Tips

For large datasets:

    comments_sample <- comments[sample(nrow(comments), min(1000, nrow(comments))), ]

    comments_sample <- comments_sample |>
      mutate(emoji_count = count_emojis(textDisplay))

    emoji_rate_estimate <- mean(comments_sample$emoji_count > 0) * 100

## Summary

Key functions used in this analysis:

Function | Purpose
--- | ---
`has_emoji()` | Check if text contains emojis
`count_emojis()` | Count emojis in text
`extract_emojis()` | Get list of emojis from text
`remove_emojis()` | Strip emojis from text
`replace_emojis()` | Replace emojis with custom text

These functions work directly on character vectors, making them easy to use with `dplyr::mutate()` and other tidyverse workflows.