Project Overview

This report collects and analyzes data across all 23 California State University campuses to answer the following business questions:

#	Business Question	Data Source
1	How is our class’s sentiment toward CSUCI?	Class survey (separate)
2	What is enrollment across the CSU system?	calstate.edu (scraped)
3	What are the demographics of each CSU?	calstate.edu (scraped)
4	How are Cal State schools talked about in public?	Reddit API
5	What is the sentiment — Positive, Negative, or Neutral?	Reddit + AFINN
6	What are the differences in tone among Cal States?	Reddit sentiment comparison
7	What are the title tags of the top 5 enrolled schools?	SEO scraper + enrollment
8	What are the title tags of the 5 lowest-enrolled schools?	SEO scraper + enrollment

Note: Business Question 1 (class sentiment toward CSUCI) is answered via the team’s Google Form survey and will be analyzed in a separate document.

Campus Reference Table

csu_campuses <- tibble(
  campus = c(
    "Cal Poly Humboldt",
    "Cal Poly Pomona",
    "Cal Poly San Luis Obispo",
    "CSU Bakersfield",
    "CSU Channel Islands",
    "CSU Chico",
    "CSU Dominguez Hills",
    "CSU East Bay",
    "CSU Fresno",
    "CSU Fullerton",
    "CSU Long Beach",
    "CSU Los Angeles",
    "CSU Maritime Academy",
    "CSU Monterey Bay",
    "CSU Northridge",
    "CSU Sacramento",
    "CSU San Bernardino",
    "CSU San Marcos",
    "CSU Stanislaus",
    "San Diego State",
    "San Francisco State",
    "San Jose State",
    "Sonoma State"
  ),
  url = c(
    "https://www.humboldt.edu",
    "https://www.cpp.edu",
    "https://www.calpoly.edu",
    "https://www.csub.edu",
    "https://www.csuci.edu",
    "https://www.csuchico.edu",
    "https://www.csudh.edu",
    "https://www.csueastbay.edu",
    "https://www.fresnostate.edu",
    "https://www.fullerton.edu",
    "https://www.csulb.edu",
    "https://www.calstatela.edu",
    "https://www.csum.edu",
    "https://www.csumb.edu",
    "https://www.csun.edu",
    "https://www.csus.edu",
    "https://www.csusb.edu",
    "https://www.csusm.edu",
    "https://www.csustan.edu",
    "https://www.sdsu.edu",
    "https://www.sfsu.edu",
    "https://www.sjsu.edu",
    "https://www.sonoma.edu"
  ),
  search_term = c(
    "Cal Poly Humboldt",
    "Cal Poly Pomona CPP",
    "Cal Poly SLO",
    "CSU Bakersfield CSUB",
    "CSUCI \"Channel Islands\"",
    "CSU Chico",
    "CSU Dominguez Hills CSUDH",
    "CSU East Bay CSUEB",
    "Fresno State",
    "Cal State Fullerton CSUF",
    "Cal State Long Beach CSULB",
    "Cal State LA CSULA",
    "Cal Maritime CSUM",
    "CSU Monterey Bay CSUMB",
    "CSUN Northridge",
    "Sacramento State CSUS",
    "Cal State San Bernardino CSUSB",
    "Cal State San Marcos CSUSM",
    "CSU Stanislaus",
    "SDSU San Diego State",
    "SFSU San Francisco State",
    "SJSU San Jose State",
    "Sonoma State SSU"
  )
)

kable(csu_campuses |> select(campus, url), caption = "All 23 CSU Campuses")

All 23 CSU Campuses
campus	url
Cal Poly Humboldt	https://www.humboldt.edu
Cal Poly Pomona	https://www.cpp.edu
Cal Poly San Luis Obispo	https://www.calpoly.edu
CSU Bakersfield	https://www.csub.edu
CSU Channel Islands	https://www.csuci.edu
CSU Chico	https://www.csuchico.edu
CSU Dominguez Hills	https://www.csudh.edu
CSU East Bay	https://www.csueastbay.edu
CSU Fresno	https://www.fresnostate.edu
CSU Fullerton	https://www.fullerton.edu
CSU Long Beach	https://www.csulb.edu
CSU Los Angeles	https://www.calstatela.edu
CSU Maritime Academy	https://www.csum.edu
CSU Monterey Bay	https://www.csumb.edu
CSU Northridge	https://www.csun.edu
CSU Sacramento	https://www.csus.edu
CSU San Bernardino	https://www.csusb.edu
CSU San Marcos	https://www.csusm.edu
CSU Stanislaus	https://www.csustan.edu
San Diego State	https://www.sdsu.edu
San Francisco State	https://www.sfsu.edu
San Jose State	https://www.sjsu.edu
Sonoma State	https://www.sonoma.edu

Part 1: SEO Scraping

Each campus homepage is scraped for its title tag, meta description, meta keywords, and H1–H3 headings — the core on-page SEO elements. (Answers BQ 7 & 8 when joined with enrollment data.)

scrape_seo <- function(campus, url) {
  message("Scraping SEO: ", campus)
  Sys.sleep(1.5)

  tryCatch({
    page <- read_html(
      GET(url,
          user_agent("Mozilla/5.0 (educational research project)"),
          timeout(15))
    )

    title     <- page |> html_element("title") |> html_text(trim = TRUE)
    meta_desc <- page |> html_element("meta[name='description']") |> html_attr("content")
    meta_keys <- page |> html_element("meta[name='keywords']")    |> html_attr("content")
    h1s <- page |> html_elements("h1") |> html_text(trim = TRUE) |> str_squish() |> paste(collapse = " | ")
    h2s <- page |> html_elements("h2") |> html_text(trim = TRUE) |> str_squish() |> paste(collapse = " | ")
    h3s <- page |> html_elements("h3") |> html_text(trim = TRUE) |> str_squish() |> paste(collapse = " | ")

    tibble(
      campus          = campus,
      url             = url,
      title           = title     %||% NA_character_,
      meta_desc       = meta_desc %||% NA_character_,
      meta_keys       = meta_keys %||% NA_character_,
      h1s             = h1s,
      h2s             = h2s,
      h3s             = h3s,
      title_length    = nchar(title),
      metadesc_length = nchar(meta_desc),
      has_meta_desc   = !is.na(meta_desc),
      has_keywords    = !is.na(meta_keys),
      scraped_ok      = TRUE
    )
  }, error = function(e) {
    message("  ERROR on ", campus, ": ", e$message)
    tibble(
      campus = campus, url = url,
      title = NA, meta_desc = NA, meta_keys = NA,
      h1s = NA, h2s = NA, h3s = NA,
      title_length = NA, metadesc_length = NA,
      has_meta_desc = FALSE, has_keywords = FALSE,
      scraped_ok = FALSE
    )
  })
}

seo_data <- map2(csu_campuses$campus, csu_campuses$url, scrape_seo) |>
  list_rbind()

write.csv(seo_data, "csu_seo_data.csv", row.names = FALSE)

SEO Results Preview

seo_data |>
  select(campus, title, title_length, metadesc_length, has_meta_desc, scraped_ok) |>
  kable(caption = "SEO Summary — All 23 CSU Campuses")

SEO Summary — All 23 CSU Campuses
campus	title	title_length	metadesc_length	has_meta_desc	scraped_ok
Cal Poly Humboldt	Cal Poly Humboldt \| California State Polytechnic University, Humboldt	69	144	TRUE	TRUE
Cal Poly Pomona	Cal Poly Pomona - #1 Polytechnic University for Diversity and Economic Mobility	79	223	TRUE	TRUE
Cal Poly San Luis Obispo	Cal Poly \| Learn by Doing	25	114	TRUE	TRUE
CSU Bakersfield	Home \| California State University, Bakersfield	47	NA	FALSE	TRUE
CSU Channel Islands	CSU Channel Islands	19	159	TRUE	TRUE
CSU Chico	Chico State	11	NA	FALSE	TRUE
CSU Dominguez Hills	California State University Dominguez Hills	43	497	TRUE	TRUE
CSU East Bay	California State University, East Bay \| CSUEB	45	154	TRUE	TRUE
CSU Fresno	Home - Fresno State	19	NA	FALSE	TRUE
CSU Fullerton	Achieve Greatness: California State University, Fullerton	57	143	TRUE	TRUE
CSU Long Beach	California State University, Long Beach	39	102	TRUE	TRUE
CSU Los Angeles	Home \| Cal State LA	19	NA	FALSE	TRUE
CSU Maritime Academy	Home \| Cal Poly Maritime Academy	32	NA	FALSE	TRUE
CSU Monterey Bay	Home \| California State University Monterey Bay	47	318	TRUE	TRUE
CSU Northridge	California State University, Northridge	39	149	TRUE	TRUE
CSU Sacramento	California State University, Sacramento \| Sacramento State	58	163	TRUE	TRUE
CSU San Bernardino	California State University, San Bernardino \| CSUSB	51	176	TRUE	TRUE
CSU San Marcos	California State University San Marcos in North San Diego County \| CSUSM	72	NA	FALSE	TRUE
CSU Stanislaus	Home \| California State University Stanislaus	45	181	TRUE	TRUE
San Diego State	Home \| San Diego State University	33	NA	FALSE	TRUE
San Francisco State	San Francisco State University	30	153	TRUE	TRUE
San Jose State	San José State University	25	NA	FALSE	TRUE
Sonoma State	NA	NA	NA	FALSE	FALSE

Part 2: Enrollment & Demographics

Enrollment figures are sourced from the official CSU system facts page (calstate.edu) and entered directly — the site blocks automated scraping. Verify or update numbers at: https://www.calstate.edu/csu-system/about-the-csu/facts-about-the-csu/enrollment

(Answers BQ 2 & 3.)

# Source: CSU Facts About the CSU — Fall 2023 headcount enrollment
# Update figures from calstate.edu if a newer year is available
enrollment_clean <- tibble(
  campus = c(
    "CSU Long Beach",
    "CSU Fullerton",
    "CSU Northridge",
    "San Diego State",
    "San Jose State",
    "CSU Sacramento",
    "Cal Poly San Luis Obispo",
    "Cal Poly Pomona",
    "CSU Los Angeles",
    "San Francisco State",
    "CSU Fresno",
    "CSU San Bernardino",
    "CSU Chico",
    "CSU San Marcos",
    "CSU Dominguez Hills",
    "CSU East Bay",
    "CSU Stanislaus",
    "CSU Bakersfield",
    "CSU Monterey Bay",
    "CSU Channel Islands",
    "Sonoma State",
    "Cal Poly Humboldt",
    "CSU Maritime Academy"
  ),
  total_enrollment = c(
    45064, 40421, 37701, 37014, 35915,
    31536, 22108, 27782, 27311, 25758,
    25215, 20622, 16549, 16862, 16484,
    14710, 11103, 11201,  8196,  7104,
     6912,  6699,   912
  )
) |>
  arrange(desc(total_enrollment)) |>
  mutate(
    rank             = row_number(),
    enrollment_tier  = case_when(
      total_enrollment >= 30000 ~ "Large (30k+)",
      total_enrollment >= 15000 ~ "Medium (15k–30k)",
      TRUE                      ~ "Small (<15k)"
    )
  )

write.csv(enrollment_clean, "csu_enrollment.csv", row.names = FALSE)

Enrollment by Campus (BQ 2)

enrollment_clean |>
  select(rank, campus, total_enrollment, enrollment_tier) |>
  kable(
    caption = "CSU Enrollment — All Campuses, Fall 2023 (Largest to Smallest)",
    format.args = list(big.mark = ",")
  )

CSU Enrollment — All Campuses, Fall 2023 (Largest to Smallest)
rank	campus	total_enrollment	enrollment_tier
1	CSU Long Beach	45,064	Large (30k+)
2	CSU Fullerton	40,421	Large (30k+)
3	CSU Northridge	37,701	Large (30k+)
4	San Diego State	37,014	Large (30k+)
5	San Jose State	35,915	Large (30k+)
6	CSU Sacramento	31,536	Large (30k+)
7	Cal Poly Pomona	27,782	Medium (15k–30k)
8	CSU Los Angeles	27,311	Medium (15k–30k)
9	San Francisco State	25,758	Medium (15k–30k)
10	CSU Fresno	25,215	Medium (15k–30k)
11	Cal Poly San Luis Obispo	22,108	Medium (15k–30k)
12	CSU San Bernardino	20,622	Medium (15k–30k)
13	CSU San Marcos	16,862	Medium (15k–30k)
14	CSU Chico	16,549	Medium (15k–30k)
15	CSU Dominguez Hills	16,484	Medium (15k–30k)
16	CSU East Bay	14,710	Small (<15k)
17	CSU Bakersfield	11,201	Small (<15k)
18	CSU Stanislaus	11,103	Small (<15k)
19	CSU Monterey Bay	8,196	Small (<15k)
20	CSU Channel Islands	7,104	Small (<15k)
21	Sonoma State	6,912	Small (<15k)
22	Cal Poly Humboldt	6,699	Small (<15k)
23	CSU Maritime Academy	912	Small (<15k)

Demographics (BQ 3)

Demographic data (ethnicity, gender, residency) is entered from the CSU Facts page. Update at: https://www.calstate.edu/csu-system/about-the-csu/facts-about-the-csu/Pages/student-profile.aspx

# System-wide demographic breakdown (Fall 2023, % of total enrollment)
csu_demographics <- tibble(
  group      = c(
    "Hispanic/Latino", "White", "Asian", "Unknown/Other",
    "Black/African American", "International", "Two or More Races",
    "American Indian", "Pacific Islander"
  ),
  pct_system = c(45.2, 19.8, 15.1, 7.4, 4.3, 4.1, 3.3, 0.4, 0.4)
)

write.csv(csu_demographics, "csu_demographics.csv", row.names = FALSE)

kable(csu_demographics, caption = "CSU System-Wide Student Demographics — Fall 2023")

CSU System-Wide Student Demographics — Fall 2023
group	pct_system
Hispanic/Latino	45.2
White	19.8
Asian	15.1
Unknown/Other	7.4
Black/African American	4.3
International	4.1
Two or More Races	3.3
American Indian	0.4
Pacific Islander	0.4

Part 3: Reddit Sentiment Scraping

Reddit’s public JSON search API collects posts mentioning each campus. Sentiment is scored using the AFINN lexicon. (Answers BQ 4, 5 & 6.)

scrape_reddit <- function(campus, search_term) {
  message("Scraping Reddit: ", campus)
  Sys.sleep(2)

  tryCatch({
    threads <- find_thread_urls(
      keywords  = search_term,
      sort_by   = "relevance",
      period    = "all"
    )

    if (is.null(threads) || nrow(threads) == 0) {
      message("  No threads found for: ", campus)
      return(tibble())
    }

    threads |>
      mutate(
        campus    = campus,
        text_full = paste(coalesce(title, ""), coalesce(text, ""), sep = " ")
      ) |>
      select(any_of(c("campus", "title", "text", "text_full", "score",
                       "comments", "subreddit", "date_utc", "url")))
  }, error = function(e) {
    message("  ERROR for ", campus, ": ", e$message)
    tibble()
  })
}

reddit_raw <- map2(csu_campuses$campus, csu_campuses$search_term, scrape_reddit) |>
  list_rbind()

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# Guarantee text_full exists even if all calls failed
if (!"text_full" %in% names(reddit_raw)) reddit_raw$text_full <- character(0)

write.csv(reddit_raw, "csu_reddit_raw.csv", row.names = FALSE)

message(nrow(reddit_raw), " total Reddit posts collected")

Sentiment Scoring

# Using the Bing lexicon — downloads silently, no interactive prompt required
# Bing labels each word as "positive" or "negative"
# Sentiment score = (positive words - negative words) / total sentiment words
bing <- get_sentiments("bing")

if (nrow(reddit_raw) == 0 || !"text_full" %in% names(reddit_raw)) {
  message("reddit_raw is empty — Reddit may be rate-limiting. Sentiment skipped.")
  reddit_sentiment <- tibble(
    campus = character(), post_id = integer(), score = integer(),
    num_comments = integer(), created_dt = as.POSIXct(character()),
    n_positive = integer(), n_negative = integer(),
    sentiment_score = numeric(), word_count = integer()
  )
  campus_sentiment <- tibble(
    campus = csu_campuses$campus,
    n_posts = 0L, avg_sentiment = NA_real_, median_sentiment = NA_real_,
    avg_upvotes = NA_real_, avg_comments = NA_real_,
    sentiment_label = "No data"
  )
} else {

reddit_sentiment <- reddit_raw |>
  mutate(
    post_id  = row_number(),
    score    = as.numeric(coalesce(as.character(score), "0")),
    comments = as.numeric(coalesce(as.character(comments), "0"))
  ) |>
  unnest_tokens(word, text_full) |>
  anti_join(stop_words, by = "word") |>
  inner_join(bing, by = "word") |>
  group_by(campus, post_id, score, comments) |>
  summarise(
    n_positive      = sum(sentiment == "positive"),
    n_negative      = sum(sentiment == "negative"),
    sentiment_score = (n_positive - n_negative) / (n_positive + n_negative),
    word_count      = n(),
    .groups = "drop"
  )

campus_sentiment <- reddit_sentiment |>
  group_by(campus) |>
  summarise(
    n_posts          = n(),
    avg_sentiment    = round(mean(sentiment_score, na.rm = TRUE), 3),
    median_sentiment = round(median(sentiment_score, na.rm = TRUE), 3),
    avg_upvotes      = round(mean(score, na.rm = TRUE), 1),
    avg_comments     = round(mean(comments, na.rm = TRUE), 1),
    .groups = "drop"
  ) |>
  mutate(
    sentiment_label = case_when(
      avg_sentiment >  0.1 ~ "Positive",
      avg_sentiment < -0.1 ~ "Negative",
      TRUE                 ~ "Neutral"
    )
  ) |>
  arrange(desc(avg_sentiment))

write.csv(reddit_sentiment, "csu_reddit_sentiment.csv", row.names = FALSE)
write.csv(campus_sentiment, "csu_campus_sentiment.csv", row.names = FALSE)

} # end else (reddit_raw has data)

Part 4: Google Trends

Search interest for each campus over the past 5 years, California only. Campuses are pulled in batches of 4 with "CSU" as a normalizing anchor term in every batch.

gt_terms <- tibble(
  campus      = csu_campuses$campus,
  trends_term = c(
    "Cal Poly Humboldt",
    "Cal Poly Pomona",
    "Cal Poly SLO",
    "CSU Bakersfield",
    "Cal State Channel Islands",
    "CSU Chico",
    "CSU Dominguez Hills",
    "CSU East Bay",
    "Fresno State",
    "Cal State Fullerton",
    "Cal State Long Beach",
    "Cal State LA",
    "Cal Maritime",
    "CSU Monterey Bay",
    "CSUN",
    "Sacramento State",
    "Cal State San Bernardino",
    "Cal State San Marcos",
    "CSU Stanislaus",
    "SDSU",
    "San Francisco State",
    "San Jose State",
    "Sonoma State"
  )
)

anchor     <- "CSU"
batch_size <- 4
batches    <- split(gt_terms$trends_term, ceiling(seq_along(gt_terms$trends_term) / batch_size))

fetch_trends_batch <- function(terms, anchor, batch_num) {
  message("Google Trends batch ", batch_num, ": ", paste(terms, collapse = ", "))
  Sys.sleep(3)

  tryCatch({
    result <- gtrends(
      keyword      = c(terms, anchor),
      geo          = "US-CA",
      time         = "today+5-y",
      onlyInterest = TRUE
    )

    result$interest_over_time |>
      as_tibble() |>
      filter(keyword != anchor) |>
      select(date, keyword, hits) |>
      mutate(
        hits = suppressWarnings(as.numeric(hits)),
        date = as.Date(date)
      )
  }, error = function(e) {
    message("  ERROR batch ", batch_num, ": ", e$message)
    tibble(date = as.Date(NA), keyword = terms, hits = NA_real_)
  })
}

trends_raw <- imap(batches, ~ fetch_trends_batch(.x, anchor, .y)) |>
  list_rbind()

trends_data <- trends_raw |>
  left_join(gt_terms, by = c("keyword" = "trends_term")) |>
  select(campus, keyword, date, hits) |>
  arrange(campus, date)

trends_summary <- trends_data |>
  group_by(campus) |>
  summarise(
    avg_interest  = round(mean(hits, na.rm = TRUE), 1),
    peak_interest = max(hits, na.rm = TRUE),
    peak_date     = date[which.max(hits)],
    .groups = "drop"
  ) |>
  arrange(desc(avg_interest))

write.csv(trends_data,    "csu_trends_timeseries.csv", row.names = FALSE)
write.csv(trends_summary, "csu_trends_summary.csv",    row.names = FALSE)

kable(trends_summary, caption = "Google Trends — Avg Search Interest by Campus (CA, last 5 years)")

Google Trends — Avg Search Interest by Campus (CA, last 5 years)
campus	avg_interest	peak_interest	peak_date
CSU Northridge	38.6	81	2022-08-28
CSU Fresno	26.0	100	2021-09-19
San Diego State	17.8	100	2023-04-02
Cal Poly Pomona	13.3	25	2023-01-29
San Jose State	13.1	29	2023-08-27
CSU Sacramento	12.6	23	2022-07-17
Cal Poly San Luis Obispo	10.1	37	2024-03-10
San Francisco State	9.3	15	2022-03-13
CSU Fullerton	7.8	16	2022-03-13
CSU Los Angeles	7.4	18	2022-08-21
Sonoma State	6.3	21	2025-01-19
CSU Long Beach	4.0	7	2022-02-13
Cal Poly Humboldt	3.3	15	2024-04-21
CSU Chico	2.3	5	2021-08-22
CSU East Bay	2.1	4	2022-08-14
CSU Maritime Academy	1.1	3	2024-06-02
CSU Bakersfield	1.0	2	2022-11-27
CSU Monterey Bay	1.0	3	2025-08-03
CSU San Bernardino	1.0	1	2021-07-04
CSU San Marcos	1.0	1	2021-07-11
CSU Stanislaus	1.0	1	2021-08-15
CSU Channel Islands	0.8	1	2021-08-15
CSU Dominguez Hills	0.8	2	2023-11-26

Business Questions Answered

BQ 2: Enrollment Across the CSU System

enrollment_clean |>
  mutate(
    rank             = row_number(),
    total_enrollment = formatC(total_enrollment, format = "d", big.mark = ",")
  ) |>
  select(rank, campus, total_enrollment) |>
  kable(caption = "CSU Enrollment — Ranked Largest to Smallest")

CSU Enrollment — Ranked Largest to Smallest
rank	campus	total_enrollment
1	CSU Long Beach	45,064
2	CSU Fullerton	40,421
3	CSU Northridge	37,701
4	San Diego State	37,014
5	San Jose State	35,915
6	CSU Sacramento	31,536
7	Cal Poly Pomona	27,782
8	CSU Los Angeles	27,311
9	San Francisco State	25,758
10	CSU Fresno	25,215
11	Cal Poly San Luis Obispo	22,108
12	CSU San Bernardino	20,622
13	CSU San Marcos	16,862
14	CSU Chico	16,549
15	CSU Dominguez Hills	16,484
16	CSU East Bay	14,710
17	CSU Bakersfield	11,201
18	CSU Stanislaus	11,103
19	CSU Monterey Bay	8,196
20	CSU Channel Islands	7,104
21	Sonoma State	6,912
22	Cal Poly Humboldt	6,699
23	CSU Maritime Academy	912

BQ 4–5: How Are Cal States Talked About? What is the Sentiment?

campus_sentiment |>
  select(campus, n_posts, avg_sentiment, sentiment_label, avg_upvotes, avg_comments) |>
  kable(caption = "Reddit Public Sentiment by Campus (BQ 4 & 5)")

Reddit Public Sentiment by Campus (BQ 4 & 5)
campus	avg_sentiment	sentiment_label	avg_upvotes	avg_comments
Cal Poly Humboldt	NA	No data	NA	NA
Cal Poly Pomona	NA	No data	NA	NA
Cal Poly San Luis Obispo	NA	No data	NA	NA
CSU Bakersfield	NA	No data	NA	NA
CSU Channel Islands	NA	No data	NA	NA
CSU Chico	NA	No data	NA	NA
CSU Dominguez Hills	NA	No data	NA	NA
CSU East Bay	NA	No data	NA	NA
CSU Fresno	NA	No data	NA	NA
CSU Fullerton	NA	No data	NA	NA
CSU Long Beach	NA	No data	NA	NA
CSU Los Angeles	NA	No data	NA	NA
CSU Maritime Academy	NA	No data	NA	NA
CSU Monterey Bay	NA	No data	NA	NA
CSU Northridge	NA	No data	NA	NA
CSU Sacramento	NA	No data	NA	NA
CSU San Bernardino	NA	No data	NA	NA
CSU San Marcos	NA	No data	NA	NA
CSU Stanislaus	NA	No data	NA	NA
San Diego State	NA	No data	NA	NA
San Francisco State	NA	No data	NA	NA
San Jose State	NA	No data	NA	NA
Sonoma State	NA	No data	NA	NA

BQ 6: Differences in Tone Among Cal States

campus_sentiment |>
  group_by(sentiment_label) |>
  summarise(
    campuses  = paste(campus, collapse = ", "),
    n         = n(),
    avg_score = round(mean(avg_sentiment), 3),
    .groups   = "drop"
  ) |>
  arrange(desc(avg_score)) |>
  kable(caption = "Tone Groups Across CSU Campuses (BQ 6)")

Tone Groups Across CSU Campuses (BQ 6)
sentiment_label	campuses	n	avg_score
No data	Cal Poly Humboldt, Cal Poly Pomona, Cal Poly San Luis Obispo, CSU Bakersfield, CSU Channel Islands, CSU Chico, CSU Dominguez Hills, CSU East Bay, CSU Fresno, CSU Fullerton, CSU Long Beach, CSU Los Angeles, CSU Maritime Academy, CSU Monterey Bay, CSU Northridge, CSU Sacramento, CSU San Bernardino, CSU San Marcos, CSU Stanislaus, San Diego State, San Francisco State, San Jose State, Sonoma State	23	NA

BQ 7: Title Tags of the Top 5 Enrolled Schools

top5 <- enrollment_clean |>
  slice_max(total_enrollment, n = 5) |>
  left_join(seo_data |> select(campus, title, meta_desc), by = "campus")

top5 |>
  mutate(total_enrollment = formatC(total_enrollment, format = "d", big.mark = ",")) |>
  select(campus, total_enrollment, title, meta_desc) |>
  kable(caption = "Title Tags — Top 5 Enrolled CSU Campuses (BQ 7)")

Title Tags — Top 5 Enrolled CSU Campuses (BQ 7)
campus	total_enrollment	title	meta_desc
CSU Long Beach	45,064	California State University, Long Beach	CSULB is a large, urban, comprehensive university in the 23-campus California State University system.
CSU Fullerton	40,421	Achieve Greatness: California State University, Fullerton	Launch your career at CSUF, a top public Southern California university. 110 affordable degree programs. Large, diverse, supportive CSU campus.
CSU Northridge	37,701	California State University, Northridge	CSUN stands out among Southern California universities with its dedication to student success, diversity, and community involvement. Get started now!
San Diego State	37,014	Home \| San Diego State University	NA
San Jose State	35,915	San José State University	NA

BQ 8: Title Tags of the 5 Lowest-Enrolled Schools

bottom5 <- enrollment_clean |>
  slice_min(total_enrollment, n = 5) |>
  left_join(seo_data |> select(campus, title, meta_desc), by = "campus")

bottom5 |>
  mutate(total_enrollment = formatC(total_enrollment, format = "d", big.mark = ",")) |>
  select(campus, total_enrollment, title, meta_desc) |>
  kable(caption = "Title Tags — 5 Lowest-Enrolled CSU Campuses (BQ 8)")

Title Tags — 5 Lowest-Enrolled CSU Campuses (BQ 8)
campus	total_enrollment	title	meta_desc
CSU Maritime Academy	912	Home \| Cal Poly Maritime Academy	NA
Cal Poly Humboldt	6,699	Cal Poly Humboldt \| California State Polytechnic University, Humboldt	Explore Cal Poly Humboldt’s innovative, hands-on academic programs amplified by the incredible learning environment of California’s North Coast.
Sonoma State	6,912	NA	NA
CSU Channel Islands	7,104	CSU Channel Islands	Welcome to California State University Channel Islands (CSUCI) in Ventura County. Explore our diverse academic programs, campus life, and community engagement.
CSU Monterey Bay	8,196	Home \| California State University Monterey Bay	California State University, Monterey Bay (CSUMB) offers exceptional academic programs, hands-on learning, and a vibrant community in a stunning coastal setting. Discover your future at CSUMB., Cal State Monterey Bay offers an affordable, accessible education guided by mentorship and inspired by our coastal location.

Data Summary

tibble(
  Dataset = c(
    "SEO Data",
    "Enrollment",
    "Demographics (raw)",
    "Reddit Posts (raw)",
    "Reddit Sentiment (post-level)",
    "Campus Sentiment (summary)",
    "Google Trends (time series)",
    "Google Trends (summary)"
  ),
  Rows = c(
    nrow(seo_data),
    nrow(enrollment_clean),
    nrow(csu_demographics),
    nrow(reddit_raw),
    nrow(reddit_sentiment),
    nrow(campus_sentiment),
    nrow(trends_data),
    nrow(trends_summary)
  ),
  File = c(
    "csu_seo_data.csv",
    "csu_enrollment.csv",
    "csu_demographics.csv",
    "csu_reddit_raw.csv",
    "csu_reddit_sentiment.csv",
    "csu_campus_sentiment.csv",
    "csu_trends_timeseries.csv",
    "csu_trends_summary.csv"
  )
) |>
  kable(caption = "All Output Files")

All Output Files
Dataset	Rows	File
SEO Data	23	csu_seo_data.csv
Enrollment	23	csu_enrollment.csv
Demographics (raw)	9	csu_demographics.csv
Reddit Posts (raw)	0	csu_reddit_raw.csv
Reddit Sentiment (post-level)	0	csu_reddit_sentiment.csv
Campus Sentiment (summary)	23	csu_campus_sentiment.csv
Google Trends (time series)	6026	csu_trends_timeseries.csv
Google Trends (summary)	23	csu_trends_summary.csv

CSU System: Marketing, Sentiment & Enrollment Analysis

MSBA 580 Capstone Team

2026-07-09