Lab 5 — Text Analysis & NLP: Word Co-occurrence Analysis
Special Topics in Business Analytics
Jeffrey Deems
August 01, 2026
library(tidyverse)
library(tidytext)
library(widyr)
library(igraph)
library(ggraph)
library(Matrix)Part 1: Reading Reflection (5 Sentences)
Word co-occurrence analysis is useful in SEO and brand analytics
because search engines and customers both infer meaning from which terms
appear near each other, not from isolated keywords alone (Chiericato,
2015; Silge & Robinson, 2017). When brands such as specialty coffee
companies repeatedly place words like “fresh,” “roasted,” and “espresso”
near the brand name, those pairings help build topical relevance in the
same way Google uses co-occurrence signals across the web. The tidytext
workflow—tokenizeizing text, removing stop words, counting pairs with
pairwise_count(), and visualizing the strongest links—makes
those associations measurable instead of intuitive guesses. Network
graphs then turn the co-occurrence matrix into a business story: hub
words, clusters, and surprising edges become actionable clues about how
people talk about a product or ticker. The main caution is sample size
and preprocessing choices (custom stopwords, thresholds for
n), because a sparse comment set can create fragile edges
that look insightful but are not yet stable enough for confident
decisions.
Part 2: Word Co-occurrence Analysis
This analysis follows the class tutorial (RPubs — Word Co-occurrence
Demo) using investor comments about SpaceX / $SPCX from
spacex_cooccurence.txt.
What is Word Co-occurrence Analysis?
Word co-occurrence analysis measures which words tend to appear together in the same unit of text (here, each comment/line). Frequent pairs can reveal how investors frame buying, waiting, valuation, and risk (Miller-style text analytics; Silge & Robinson, 2017).
Warm-Up: Fruit Example
fruit_sentences <- tibble(
id = 1:5,
text = c(
"The apple was crisp, sweet, and red.",
"Bananas are soft, sweet, and yellow.",
"The lemon was sour, bright, and yellow.",
"Strawberries are sweet, red, and juicy.",
"The lime was sour, small, and green."
)
)
fruit_sentences## # A tibble: 5 × 2
## id text
## <int> <chr>
## 1 1 The apple was crisp, sweet, and red.
## 2 2 Bananas are soft, sweet, and yellow.
## 3 3 The lemon was sour, bright, and yellow.
## 4 4 Strawberries are sweet, red, and juicy.
## 5 5 The lime was sour, small, and green.fruit_words <- fruit_sentences %>%
unnest_tokens(word, text) %>%
anti_join(stop_words, by = "word")
fruit_words## # A tibble: 19 × 2
## id word
## <int> <chr>
## 1 1 apple
## 2 1 crisp
## 3 1 sweet
## 4 1 red
## 5 2 bananas
## 6 2 soft
## 7 2 sweet
## 8 2 yellow
## 9 3 lemon
## 10 3 sour
## 11 3 bright
## 12 3 yellow
## 13 4 strawberries
## 14 4 sweet
## 15 4 red
## 16 4 juicy
## 17 5 lime
## 18 5 sour
## 19 5 greenfruit_pairs <- fruit_words %>%
pairwise_count(word, id, sort = TRUE, upper = FALSE)
fruit_pairs## # A tibble: 26 × 3
## item1 item2 n
## <chr> <chr> <dbl>
## 1 sweet red 2
## 2 apple crisp 1
## 3 apple sweet 1
## 4 crisp sweet 1
## 5 apple red 1
## 6 crisp red 1
## 7 sweet bananas 1
## 8 sweet soft 1
## 9 bananas soft 1
## 10 sweet yellow 1
## # ℹ 16 more rowsfruit_matrix <- fruit_pairs %>%
bind_rows(fruit_pairs %>% rename(item1 = item2, item2 = item1)) %>%
cast_sparse(item1, item2, n) %>%
as.matrix()
fruit_matrix## red crisp sweet bananas soft yellow lemon sour bright strawberries
## sweet 2 1 0 1 1 1 0 0 0 1
## apple 1 1 1 0 0 0 0 0 0 0
## crisp 1 0 1 0 0 0 0 0 0 0
## bananas 0 0 1 0 1 1 0 0 0 0
## soft 0 0 1 1 0 1 0 0 0 0
## yellow 0 0 1 1 1 0 1 1 1 0
## lemon 0 0 0 0 0 1 0 1 1 0
## sour 0 0 0 0 0 1 1 0 1 0
## red 0 1 2 0 0 0 0 0 0 1
## strawberries 1 0 1 0 0 0 0 0 0 0
## lime 0 0 0 0 0 0 0 1 0 0
## bright 0 0 0 0 0 1 1 1 0 0
## juicy 1 0 1 0 0 0 0 0 0 1
## green 0 0 0 0 0 0 0 1 0 0
## juicy lime green apple
## sweet 1 0 0 1
## apple 0 0 0 0
## crisp 0 0 0 1
## bananas 0 0 0 0
## soft 0 0 0 0
## yellow 0 0 0 0
## lemon 0 0 0 0
## sour 0 1 1 0
## red 1 0 0 1
## strawberries 1 0 0 0
## lime 0 0 1 0
## bright 0 0 0 0
## juicy 0 0 0 0
## green 0 1 0 0set.seed(580)
fruit_pairs %>%
filter(n >= 1) %>%
graph_from_data_frame() %>%
ggraph(layout = "fr") +
geom_edge_link(aes(edge_alpha = n, edge_width = n), color = "steelblue") +
geom_node_point(size = 5, color = "darkorange") +
geom_node_text(aes(label = name), repel = TRUE, size = 4) +
theme_void() +
labs(title = "Word Co-occurrence Network: Fruit Descriptions")
Real-World Example: SpaceX Investor Comments
Step 1: Load the Raw Text
spacex_lines <- readLines("spacex_cooccurence.txt", encoding = "UTF-8")
spacex_lines <- gsub("^\\s+|\\s+$", "", spacex_lines, useBytes = TRUE)
spacex_text <- tibble(
id = seq_along(spacex_lines),
text = spacex_lines
)
spacex_text## # A tibble: 13 × 2
## id text
## <int> <chr>
## 1 1 My buy level for spacex is 50-60
## 2 2 Everyone of these experts said don't buy. I did. I bought and sold. It…
## 3 3 Below 100 i buy
## 4 4 Typical lockups don’t apply to SpaceX. It has a unique, multi-phase…
## 5 5 I sold my $SPCX shares at $197. Once it crashes to ~ $75, I will buy a…
## 6 6 My personal high-conviction targets for the TOP 15 Digital Assets of 2…
## 7 7 I'd rather miss the move than invest at current valuations lol
## 8 8 Buy one to two shares in the beginning while all the hype continues so…
## 9 9 I want to invest in spacex but not at this valuation. Will wait 6 mont…
## 10 10 Nothing is new here. I expect the SpaceX price to drop significantly i…
## 11 11 TSLA and SPCX Merger in a couple of years.
## 12 12 I bought 5 Calls yesterdays dip. FOMO is real. I wouldnt be surprise i…
## 13 13 I will be DCAing into this stock for the next 15 years! Its already be…Step 2: Tokenize, Clean, and Add Custom Stopwords
Task: After inspecting the frequent-word tibble, I added two extra stopwords that are common in this corpus mainly because every comment is about investing in equity, not because they signal a distinct theme.
# Added "stock" and "term" as custom stopwords (see Question 1)
custom_stop_words <- bind_rows(
stop_words,
tibble(
word = c(
"im", "ive", "dont", "didnt", "isnt", "lol",
"stock", "term" # <-- my additional stopwords
),
lexicon = "custom"
)
)
spacex_words <- spacex_text %>%
mutate(text = str_replace_all(text, "[^[:alnum:][:space:]$]", " ")) %>%
unnest_tokens(word, text, token = "words") %>%
filter(!str_detect(word, "^[0-9]+$")) %>%
anti_join(custom_stop_words, by = "word")
spacex_words %>% count(word, sort = TRUE) %>% head(15)## # A tibble: 15 × 2
## word n
## <chr> <int>
## 1 buy 6
## 2 shares 6
## 3 spacex 6
## 4 price 3
## 5 spcx 3
## 6 apply 2
## 7 bought 2
## 8 invest 2
## 9 massive 2
## 10 months 2
## 11 move 2
## 12 significantly 2
## 13 sold 2
## 14 wait 2
## 15 absolute 1Step 3: Count Word Pairs
spacex_pairs <- spacex_words %>%
pairwise_count(word, id, sort = TRUE, upper = FALSE)
spacex_pairs %>% head(15)## # A tibble: 15 × 3
## item1 item2 n
## <chr> <chr> <dbl>
## 1 shares price 3
## 2 buy sold 2
## 3 buy shares 2
## 4 spacex shares 2
## 5 spacex price 2
## 6 shares significantly 2
## 7 price significantly 2
## 8 spacex wait 2
## 9 spacex months 2
## 10 wait months 2
## 11 buy level 1
## 12 buy spacex 1
## 13 level spacex 1
## 14 buy experts 1
## 15 buy don 1Step 4: Build the Co-occurrence Matrix (Top 15 Words)
top_words <- spacex_words %>%
count(word, sort = TRUE) %>%
slice_max(n, n = 15) %>%
pull(word)
spacex_matrix <- spacex_pairs %>%
filter(item1 %in% top_words, item2 %in% top_words) %>%
bind_rows(
spacex_pairs %>%
rename(item1 = item2, item2 = item1) %>%
filter(item1 %in% top_words, item2 %in% top_words)
) %>%
cast_sparse(item1, item2, n) %>%
as.matrix()
spacex_matrix## price sold shares significantly wait months level spacex experts
## shares 3 1 0 2 1 1 0 2 0
## buy 1 2 2 1 0 0 1 1 1
## spacex 2 0 2 1 2 2 1 0 0
## price 0 0 3 2 1 1 0 2 0
## wait 1 0 1 1 0 2 0 2 0
## level 0 0 0 0 0 0 0 1 0
## experts 0 1 0 0 0 0 0 0 0
## don 0 1 0 0 0 0 0 0 1
## bought 0 1 0 0 0 0 0 0 1
## sold 0 0 1 0 0 0 0 0 1
## typical 1 0 1 0 0 0 0 1 0
## lockups 1 0 1 0 0 0 0 1 0
## donâ 1 0 1 0 0 0 0 1 0
## apply 1 0 1 0 0 0 0 1 0
## unique 1 0 1 0 0 0 0 1 0
## multi 1 0 1 0 0 0 0 1 0
## phase 1 0 1 0 0 0 0 1 0
## lock 1 0 1 0 0 0 0 1 0
## release 1 0 1 0 0 0 0 1 0
## schedule 1 0 1 0 0 0 0 1 0
## intended 1 0 1 0 0 0 0 1 0
## facilitate 1 0 1 0 0 0 0 1 0
## trickle 1 0 1 0 0 0 0 1 0
## selling 1 0 1 0 0 0 0 1 0
## massive 1 0 1 0 0 0 0 1 0
## dump 1 0 1 0 0 0 0 1 0
## expiration 1 0 1 0 0 0 0 1 0
## single 1 0 1 0 0 0 0 1 0
## lockup 1 0 1 0 0 0 0 1 0
## date 1 0 1 0 0 0 0 1 0
## historical 1 0 1 0 0 0 0 1 0
## model 1 0 1 0 0 0 0 1 0
## doesnâ 1 0 1 0 0 0 0 1 0
## importantly 1 0 1 0 0 0 0 1 0
## itâ 1 0 1 0 0 0 0 1 0
## floated 1 0 1 0 0 0 0 1 0
## profound 1 0 1 0 0 0 0 1 0
## impact 1 0 1 0 0 0 0 1 0
## future 1 0 1 0 0 0 0 1 0
## prospects 1 0 1 0 0 0 0 1 0
## driving 1 0 1 0 0 0 0 1 0
## extreme 1 0 1 0 0 0 0 1 0
## volatility 1 0 1 0 0 0 0 1 0
## delaying 1 0 1 0 0 0 0 1 0
## inclusion 1 0 1 0 0 0 0 1 0
## major 1 0 1 0 0 0 0 1 0
## passive 1 0 1 0 0 0 0 1 0
## indexes 1 0 1 0 0 0 0 1 0
## keeping 1 0 1 0 0 0 0 1 0
## absolute 1 0 1 0 0 0 0 1 0
## strategic 1 0 1 0 0 0 0 1 0
## control 1 0 1 0 0 0 0 1 0
## firmly 1 0 1 0 0 0 0 1 0
## hands 1 0 1 0 0 0 0 1 0
## elon 1 0 1 0 0 0 0 1 0
## musk 1 0 1 0 0 0 0 1 0
## reserving 1 0 1 0 0 0 0 1 0
## insiders 1 0 1 0 0 0 0 1 0
## investors 1 0 1 0 0 0 0 1 0
## created 1 0 1 0 0 0 0 1 0
## ecosystem 1 0 1 0 0 0 0 1 0
## trades 1 0 1 0 0 0 0 1 0
## scarcity 1 0 1 0 0 0 0 1 0
## fundamental 1 0 1 0 0 0 0 1 0
## performance 1 0 1 0 0 0 0 1 0
## spcx 0 1 1 0 0 0 0 0 0
## personal 0 0 0 0 0 0 0 0 0
## conviction 0 0 0 0 0 0 0 0 0
## targets 0 0 0 0 0 0 0 0 0
## top 0 0 0 0 0 0 0 0 0
## digital 0 0 0 0 0 0 0 0 0
## assets 0 0 0 0 0 0 0 0 0
## solana 0 0 0 0 0 0 0 0 0
## spcx55k 0 0 0 0 0 0 0 0 0
## xrp 0 0 0 0 0 0 0 0 0
## move 0 1 0 0 0 0 0 0 1
## miss 0 0 0 0 0 0 0 0 0
## invest 0 0 0 0 1 1 0 1 0
## current 0 0 0 0 0 0 0 0 0
## beginning 1 0 1 1 0 0 0 0 0
## hype 1 0 1 1 0 0 0 0 0
## continues 1 0 1 1 0 0 0 0 0
## keast 1 0 1 1 0 0 0 0 0
## follow 1 0 1 1 0 0 0 0 0
## portfolio 1 0 1 1 0 0 0 0 0
## drops 1 0 1 1 0 0 0 0 0
## significantly 2 0 2 0 1 1 0 1 0
## start 1 0 1 1 0 0 0 0 0
## dollar 1 0 1 1 0 0 0 0 0
## cost 1 0 1 1 0 0 0 0 0
## averaging 1 0 1 1 0 0 0 0 0
## valuation 0 0 0 0 1 1 0 1 0
## months 1 0 1 1 2 0 0 2 0
## expect 1 0 1 1 1 1 0 1 0
## drop 1 0 1 1 1 1 0 1 0
## panics 1 0 1 1 1 1 0 1 0
## sells 1 0 1 1 1 1 0 1 0
## opportunity 1 0 1 1 1 1 0 1 0
## arises 1 0 1 1 1 1 0 1 0
## collect 1 0 1 1 1 1 0 1 0
## accumulate 1 0 1 1 1 1 0 1 0
## capital 1 0 1 1 1 1 0 1 0
## grandchildren 1 0 1 1 1 1 0 1 0
## remember 1 0 1 1 1 1 0 1 0
## decision 1 0 1 1 1 1 0 1 0
## pre 1 0 1 1 1 1 0 1 0
## ipo 1 0 1 1 1 1 0 1 0
## locked 1 0 1 1 1 1 0 1 0
## days 1 0 1 1 1 1 0 1 0
## sell 1 0 1 1 1 1 0 1 0
## tsla 0 0 0 0 0 0 0 0 0
## merger 0 0 0 0 0 0 0 0 0
## calls 0 0 0 0 0 0 0 0 0
## yesterdays 0 0 0 0 0 0 0 0 0
## dip 0 0 0 0 0 0 0 0 0
## fomo 0 0 0 0 0 0 0 0 0
## real 0 0 0 0 0 0 0 0 0
## wouldnt 0 0 0 0 0 0 0 0 0
## surprise 0 0 0 0 0 0 0 0 0
## dcaing 0 0 0 0 0 0 0 0 0
## strategy 0 0 0 0 0 0 0 0 0
## applies 0 0 0 0 0 0 0 0 0
## metrics 1 0 1 0 0 0 0 1 0
## crashes 0 1 1 0 0 0 0 0 0
## btc 0 0 0 0 0 0 0 0 0
## valuations 0 0 0 0 0 0 0 0 0
## chunk 1 0 1 1 0 0 0 0 0
## reassess 0 0 0 0 1 1 0 1 0
## day 1 0 1 1 1 1 0 1 0
## couple 0 0 0 0 0 0 0 0 0
## runs 0 0 0 0 0 0 0 0 0
## stocks 0 0 0 0 0 0 0 0 0
## don bought move typical lockups donâ apply unique multi phase
## shares 0 0 0 1 1 1 1 1 1 1
## buy 1 1 1 0 0 0 0 0 0 0
## spacex 0 0 0 1 1 1 1 1 1 1
## price 0 0 0 1 1 1 1 1 1 1
## wait 0 0 0 0 0 0 0 0 0 0
## level 0 0 0 0 0 0 0 0 0 0
## experts 1 1 1 0 0 0 0 0 0 0
## don 0 1 1 0 0 0 0 0 0 0
## bought 1 0 1 0 0 0 0 0 0 0
## sold 1 1 1 0 0 0 0 0 0 0
## typical 0 0 0 0 1 1 1 1 1 1
## lockups 0 0 0 1 0 1 1 1 1 1
## donâ 0 0 0 1 1 0 1 1 1 1
## apply 0 0 0 1 1 1 0 1 1 1
## unique 0 0 0 1 1 1 1 0 1 1
## multi 0 0 0 1 1 1 1 1 0 1
## phase 0 0 0 1 1 1 1 1 1 0
## lock 0 0 0 1 1 1 1 1 1 1
## release 0 0 0 1 1 1 1 1 1 1
## schedule 0 0 0 1 1 1 1 1 1 1
## intended 0 0 0 1 1 1 1 1 1 1
## facilitate 0 0 0 1 1 1 1 1 1 1
## trickle 0 0 0 1 1 1 1 1 1 1
## selling 0 0 0 1 1 1 1 1 1 1
## massive 0 0 0 1 1 1 1 1 1 1
## dump 0 0 0 1 1 1 1 1 1 1
## expiration 0 0 0 1 1 1 1 1 1 1
## single 0 0 0 1 1 1 1 1 1 1
## lockup 0 0 0 1 1 1 1 1 1 1
## date 0 0 0 1 1 1 1 1 1 1
## historical 0 0 0 1 1 1 1 1 1 1
## model 0 0 0 1 1 1 1 1 1 1
## doesnâ 0 0 0 1 1 1 1 1 1 1
## importantly 0 0 0 1 1 1 1 1 1 1
## itâ 0 0 0 1 1 1 1 1 1 1
## floated 0 0 0 1 1 1 1 1 1 1
## profound 0 0 0 1 1 1 1 1 1 1
## impact 0 0 0 1 1 1 1 1 1 1
## future 0 0 0 1 1 1 1 1 1 1
## prospects 0 0 0 1 1 1 1 1 1 1
## driving 0 0 0 1 1 1 1 1 1 1
## extreme 0 0 0 1 1 1 1 1 1 1
## volatility 0 0 0 1 1 1 1 1 1 1
## delaying 0 0 0 1 1 1 1 1 1 1
## inclusion 0 0 0 1 1 1 1 1 1 1
## major 0 0 0 1 1 1 1 1 1 1
## passive 0 0 0 1 1 1 1 1 1 1
## indexes 0 0 0 1 1 1 1 1 1 1
## keeping 0 0 0 1 1 1 1 1 1 1
## absolute 0 0 0 1 1 1 1 1 1 1
## strategic 0 0 0 1 1 1 1 1 1 1
## control 0 0 0 1 1 1 1 1 1 1
## firmly 0 0 0 1 1 1 1 1 1 1
## hands 0 0 0 1 1 1 1 1 1 1
## elon 0 0 0 1 1 1 1 1 1 1
## musk 0 0 0 1 1 1 1 1 1 1
## reserving 0 0 0 1 1 1 1 1 1 1
## insiders 0 0 0 1 1 1 1 1 1 1
## investors 0 0 0 1 1 1 1 1 1 1
## created 0 0 0 1 1 1 1 1 1 1
## ecosystem 0 0 0 1 1 1 1 1 1 1
## trades 0 0 0 1 1 1 1 1 1 1
## scarcity 0 0 0 1 1 1 1 1 1 1
## fundamental 0 0 0 1 1 1 1 1 1 1
## performance 0 0 0 1 1 1 1 1 1 1
## spcx 0 1 0 0 0 0 0 0 0 0
## personal 0 0 0 0 0 0 0 0 0 0
## conviction 0 0 0 0 0 0 0 0 0 0
## targets 0 0 0 0 0 0 0 0 0 0
## top 0 0 0 0 0 0 0 0 0 0
## digital 0 0 0 0 0 0 0 0 0 0
## assets 0 0 0 0 0 0 0 0 0 0
## solana 0 0 0 0 0 0 0 0 0 0
## spcx55k 0 0 0 0 0 0 0 0 0 0
## xrp 0 0 0 0 0 0 0 0 0 0
## move 1 1 0 0 0 0 0 0 0 0
## miss 0 0 1 0 0 0 0 0 0 0
## invest 0 0 1 0 0 0 0 0 0 0
## current 0 0 1 0 0 0 0 0 0 0
## beginning 0 0 0 0 0 0 0 0 0 0
## hype 0 0 0 0 0 0 0 0 0 0
## continues 0 0 0 0 0 0 0 0 0 0
## keast 0 0 0 0 0 0 0 0 0 0
## follow 0 0 0 0 0 0 0 0 0 0
## portfolio 0 0 0 0 0 0 0 0 0 0
## drops 0 0 0 0 0 0 0 0 0 0
## significantly 0 0 0 0 0 0 0 0 0 0
## start 0 0 0 0 0 0 0 0 0 0
## dollar 0 0 0 0 0 0 0 0 0 0
## cost 0 0 0 0 0 0 0 0 0 0
## averaging 0 0 0 0 0 0 0 0 0 0
## valuation 0 0 0 0 0 0 0 0 0 0
## months 0 0 0 0 0 0 0 0 0 0
## expect 0 0 0 0 0 0 0 0 0 0
## drop 0 0 0 0 0 0 0 0 0 0
## panics 0 0 0 0 0 0 0 0 0 0
## sells 0 0 0 0 0 0 0 0 0 0
## opportunity 0 0 0 0 0 0 0 0 0 0
## arises 0 0 0 0 0 0 0 0 0 0
## collect 0 0 0 0 0 0 0 0 0 0
## accumulate 0 0 0 0 0 0 0 0 0 0
## capital 0 0 0 0 0 0 0 0 0 0
## grandchildren 0 0 0 0 0 0 0 0 0 0
## remember 0 0 0 0 0 0 0 0 0 0
## decision 0 0 0 0 0 0 0 0 0 0
## pre 0 0 0 0 0 0 0 0 0 0
## ipo 0 0 0 0 0 0 0 0 0 0
## locked 0 0 0 0 0 0 0 0 0 0
## days 0 0 0 0 0 0 0 0 0 0
## sell 0 0 0 0 0 0 0 0 0 0
## tsla 0 0 0 0 0 0 0 0 0 0
## merger 0 0 0 0 0 0 0 0 0 0
## calls 0 1 0 0 0 0 0 0 0 0
## yesterdays 0 1 0 0 0 0 0 0 0 0
## dip 0 1 0 0 0 0 0 0 0 0
## fomo 0 1 0 0 0 0 0 0 0 0
## real 0 1 0 0 0 0 0 0 0 0
## wouldnt 0 1 0 0 0 0 0 0 0 0
## surprise 0 1 0 0 0 0 0 0 0 0
## dcaing 0 0 0 0 0 0 0 0 0 0
## strategy 0 0 0 0 0 0 0 0 0 0
## applies 0 0 0 0 0 0 0 0 0 0
## metrics 0 0 0 1 1 1 1 1 1 1
## crashes 0 0 0 0 0 0 0 0 0 0
## btc 0 0 0 0 0 0 0 0 0 0
## valuations 0 0 1 0 0 0 0 0 0 0
## chunk 0 0 0 0 0 0 0 0 0 0
## reassess 0 0 0 0 0 0 0 0 0 0
## day 0 0 0 0 0 0 0 0 0 0
## couple 0 0 0 0 0 0 0 0 0 0
## runs 0 1 0 0 0 0 0 0 0 0
## stocks 0 0 0 0 0 0 0 0 0 0
## lock release schedule intended facilitate trickle selling massive
## shares 1 1 1 1 1 1 1 1
## buy 0 0 0 0 0 0 0 0
## spacex 1 1 1 1 1 1 1 1
## price 1 1 1 1 1 1 1 1
## wait 0 0 0 0 0 0 0 0
## level 0 0 0 0 0 0 0 0
## experts 0 0 0 0 0 0 0 0
## don 0 0 0 0 0 0 0 0
## bought 0 0 0 0 0 0 0 0
## sold 0 0 0 0 0 0 0 0
## typical 1 1 1 1 1 1 1 1
## lockups 1 1 1 1 1 1 1 1
## donâ 1 1 1 1 1 1 1 1
## apply 1 1 1 1 1 1 1 1
## unique 1 1 1 1 1 1 1 1
## multi 1 1 1 1 1 1 1 1
## phase 1 1 1 1 1 1 1 1
## lock 0 1 1 1 1 1 1 1
## release 1 0 1 1 1 1 1 1
## schedule 1 1 0 1 1 1 1 1
## intended 1 1 1 0 1 1 1 1
## facilitate 1 1 1 1 0 1 1 1
## trickle 1 1 1 1 1 0 1 1
## selling 1 1 1 1 1 1 0 1
## massive 1 1 1 1 1 1 1 0
## dump 1 1 1 1 1 1 1 1
## expiration 1 1 1 1 1 1 1 1
## single 1 1 1 1 1 1 1 1
## lockup 1 1 1 1 1 1 1 1
## date 1 1 1 1 1 1 1 1
## historical 1 1 1 1 1 1 1 1
## model 1 1 1 1 1 1 1 1
## doesnâ 1 1 1 1 1 1 1 1
## importantly 1 1 1 1 1 1 1 1
## itâ 1 1 1 1 1 1 1 1
## floated 1 1 1 1 1 1 1 1
## profound 1 1 1 1 1 1 1 1
## impact 1 1 1 1 1 1 1 1
## future 1 1 1 1 1 1 1 1
## prospects 1 1 1 1 1 1 1 1
## driving 1 1 1 1 1 1 1 1
## extreme 1 1 1 1 1 1 1 1
## volatility 1 1 1 1 1 1 1 1
## delaying 1 1 1 1 1 1 1 1
## inclusion 1 1 1 1 1 1 1 1
## major 1 1 1 1 1 1 1 1
## passive 1 1 1 1 1 1 1 1
## indexes 1 1 1 1 1 1 1 1
## keeping 1 1 1 1 1 1 1 1
## absolute 1 1 1 1 1 1 1 1
## strategic 1 1 1 1 1 1 1 1
## control 1 1 1 1 1 1 1 1
## firmly 1 1 1 1 1 1 1 1
## hands 1 1 1 1 1 1 1 1
## elon 1 1 1 1 1 1 1 1
## musk 1 1 1 1 1 1 1 1
## reserving 1 1 1 1 1 1 1 1
## insiders 1 1 1 1 1 1 1 1
## investors 1 1 1 1 1 1 1 1
## created 1 1 1 1 1 1 1 1
## ecosystem 1 1 1 1 1 1 1 1
## trades 1 1 1 1 1 1 1 1
## scarcity 1 1 1 1 1 1 1 1
## fundamental 1 1 1 1 1 1 1 1
## performance 1 1 1 1 1 1 1 1
## spcx 0 0 0 0 0 0 0 0
## personal 0 0 0 0 0 0 0 0
## conviction 0 0 0 0 0 0 0 0
## targets 0 0 0 0 0 0 0 0
## top 0 0 0 0 0 0 0 0
## digital 0 0 0 0 0 0 0 0
## assets 0 0 0 0 0 0 0 0
## solana 0 0 0 0 0 0 0 0
## spcx55k 0 0 0 0 0 0 0 0
## xrp 0 0 0 0 0 0 0 0
## move 0 0 0 0 0 0 0 0
## miss 0 0 0 0 0 0 0 0
## invest 0 0 0 0 0 0 0 0
## current 0 0 0 0 0 0 0 0
## beginning 0 0 0 0 0 0 0 0
## hype 0 0 0 0 0 0 0 0
## continues 0 0 0 0 0 0 0 0
## keast 0 0 0 0 0 0 0 0
## follow 0 0 0 0 0 0 0 0
## portfolio 0 0 0 0 0 0 0 0
## drops 0 0 0 0 0 0 0 0
## significantly 0 0 0 0 0 0 0 0
## start 0 0 0 0 0 0 0 0
## dollar 0 0 0 0 0 0 0 0
## cost 0 0 0 0 0 0 0 0
## averaging 0 0 0 0 0 0 0 0
## valuation 0 0 0 0 0 0 0 0
## months 0 0 0 0 0 0 0 0
## expect 0 0 0 0 0 0 0 0
## drop 0 0 0 0 0 0 0 0
## panics 0 0 0 0 0 0 0 0
## sells 0 0 0 0 0 0 0 0
## opportunity 0 0 0 0 0 0 0 0
## arises 0 0 0 0 0 0 0 0
## collect 0 0 0 0 0 0 0 0
## accumulate 0 0 0 0 0 0 0 0
## capital 0 0 0 0 0 0 0 0
## grandchildren 0 0 0 0 0 0 0 0
## remember 0 0 0 0 0 0 0 0
## decision 0 0 0 0 0 0 0 0
## pre 0 0 0 0 0 0 0 0
## ipo 0 0 0 0 0 0 0 0
## locked 0 0 0 0 0 0 0 0
## days 0 0 0 0 0 0 0 0
## sell 0 0 0 0 0 0 0 0
## tsla 0 0 0 0 0 0 0 0
## merger 0 0 0 0 0 0 0 0
## calls 0 0 0 0 0 0 0 0
## yesterdays 0 0 0 0 0 0 0 0
## dip 0 0 0 0 0 0 0 0
## fomo 0 0 0 0 0 0 0 0
## real 0 0 0 0 0 0 0 0
## wouldnt 0 0 0 0 0 0 0 0
## surprise 0 0 0 0 0 0 0 0
## dcaing 0 0 0 0 0 0 0 0
## strategy 0 0 0 0 0 0 0 0
## applies 0 0 0 0 0 0 0 0
## metrics 1 1 1 1 1 1 1 1
## crashes 0 0 0 0 0 0 0 0
## btc 0 0 0 0 0 0 0 0
## valuations 0 0 0 0 0 0 0 0
## chunk 0 0 0 0 0 0 0 0
## reassess 0 0 0 0 0 0 0 0
## day 0 0 0 0 0 0 0 0
## couple 0 0 0 0 0 0 0 0
## runs 0 0 0 0 0 0 0 0
## stocks 0 0 0 0 0 0 0 0
## dump expiration single lockup date historical model doesnâ
## shares 1 1 1 1 1 1 1 1
## buy 0 0 0 0 0 0 0 0
## spacex 1 1 1 1 1 1 1 1
## price 1 1 1 1 1 1 1 1
## wait 0 0 0 0 0 0 0 0
## level 0 0 0 0 0 0 0 0
## experts 0 0 0 0 0 0 0 0
## don 0 0 0 0 0 0 0 0
## bought 0 0 0 0 0 0 0 0
## sold 0 0 0 0 0 0 0 0
## typical 1 1 1 1 1 1 1 1
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## selling 0 0 0 0 0 0 0 0 0 0
## massive 0 0 0 0 0 0 0 0 0 0
## dump 0 0 0 0 0 0 0 0 0 0
## expiration 0 0 0 0 0 0 0 0 0 0
## single 0 0 0 0 0 0 0 0 0 0
## lockup 0 0 0 0 0 0 0 0 0 0
## date 0 0 0 0 0 0 0 0 0 0
## historical 0 0 0 0 0 0 0 0 0 0
## model 0 0 0 0 0 0 0 0 0 0
## doesnâ 0 0 0 0 0 0 0 0 0 0
## importantly 0 0 0 0 0 0 0 0 0 0
## itâ 0 0 0 0 0 0 0 0 0 0
## floated 0 0 0 0 0 0 0 0 0 0
## profound 0 0 0 0 0 0 0 0 0 0
## impact 0 0 0 0 0 0 0 0 0 0
## future 0 0 0 0 0 0 0 0 0 0
## prospects 0 0 0 0 0 0 0 0 0 0
## driving 0 0 0 0 0 0 0 0 0 0
## extreme 0 0 0 0 0 0 0 0 0 0
## volatility 0 0 0 0 0 0 0 0 0 0
## delaying 0 0 0 0 0 0 0 0 0 0
## inclusion 0 0 0 0 0 0 0 0 0 0
## major 0 0 0 0 0 0 0 0 0 0
## passive 0 0 0 0 0 0 0 0 0 0
## indexes 0 0 0 0 0 0 0 0 0 0
## keeping 0 0 0 0 0 0 0 0 0 0
## absolute 0 0 0 0 0 0 0 0 0 0
## strategic 0 0 0 0 0 0 0 0 0 0
## control 0 0 0 0 0 0 0 0 0 0
## firmly 0 0 0 0 0 0 0 0 0 0
## hands 0 0 0 0 0 0 0 0 0 0
## elon 0 0 0 0 0 0 0 0 0 0
## musk 0 0 0 0 0 0 0 0 0 0
## reserving 0 0 0 0 0 0 0 0 0 0
## insiders 0 0 0 0 0 0 0 0 0 0
## investors 0 0 0 0 0 0 0 0 0 0
## created 0 0 0 0 0 0 0 0 0 0
## ecosystem 0 0 0 0 0 0 0 0 0 0
## trades 0 0 0 0 0 0 0 0 0 0
## scarcity 0 0 0 0 0 0 0 0 0 0
## fundamental 0 0 0 0 0 0 0 0 0 0
## performance 0 0 0 0 0 0 0 0 0 0
## spcx 0 0 0 0 0 0 0 0 0 1
## personal 0 0 0 0 0 0 0 0 0 0
## conviction 0 0 0 0 0 0 0 0 0 0
## targets 0 0 0 0 0 0 0 0 0 0
## top 0 0 0 0 0 0 0 0 0 0
## digital 0 0 0 0 0 0 0 0 0 0
## assets 0 0 0 0 0 0 0 0 0 0
## solana 0 0 0 0 0 0 0 0 0 0
## spcx55k 0 0 0 0 0 0 0 0 0 0
## xrp 0 0 0 0 0 0 0 0 0 0
## move 0 0 0 0 0 0 0 0 0 0
## miss 0 0 0 0 0 0 0 0 0 0
## invest 0 0 0 0 0 0 0 0 0 0
## current 0 0 0 0 0 0 0 0 0 0
## beginning 0 0 0 0 0 0 0 0 0 0
## hype 0 0 0 0 0 0 0 0 0 0
## continues 0 0 0 0 0 0 0 0 0 0
## keast 0 0 0 0 0 0 0 0 0 0
## follow 0 0 0 0 0 0 0 0 0 0
## portfolio 0 0 0 0 0 0 0 0 0 0
## drops 0 0 0 0 0 0 0 0 0 0
## significantly 1 1 1 1 1 1 1 1 1 0
## start 0 0 0 0 0 0 0 0 0 0
## dollar 0 0 0 0 0 0 0 0 0 0
## cost 0 0 0 0 0 0 0 0 0 0
## averaging 0 0 0 0 0 0 0 0 0 0
## valuation 0 0 0 0 0 0 0 0 0 0
## months 1 1 1 1 1 1 1 1 1 0
## expect 1 1 1 1 1 1 1 1 1 0
## drop 1 1 1 1 1 1 1 1 1 0
## panics 1 1 1 1 1 1 1 1 1 0
## sells 1 1 1 1 1 1 1 1 1 0
## opportunity 1 1 1 1 1 1 1 1 1 0
## arises 1 1 1 1 1 1 1 1 1 0
## collect 1 1 1 1 1 1 1 1 1 0
## accumulate 1 1 1 1 1 1 1 1 1 0
## capital 1 1 1 1 1 1 1 1 1 0
## grandchildren 0 1 1 1 1 1 1 1 1 0
## remember 1 0 1 1 1 1 1 1 1 0
## decision 1 1 0 1 1 1 1 1 1 0
## pre 1 1 1 0 1 1 1 1 1 0
## ipo 1 1 1 1 0 1 1 1 1 0
## locked 1 1 1 1 1 0 1 1 1 0
## days 1 1 1 1 1 1 0 1 1 0
## sell 1 1 1 1 1 1 1 0 1 0
## tsla 0 0 0 0 0 0 0 0 0 0
## merger 0 0 0 0 0 0 0 0 0 1
## calls 0 0 0 0 0 0 0 0 0 0
## yesterdays 0 0 0 0 0 0 0 0 0 0
## dip 0 0 0 0 0 0 0 0 0 0
## fomo 0 0 0 0 0 0 0 0 0 0
## real 0 0 0 0 0 0 0 0 0 0
## wouldnt 0 0 0 0 0 0 0 0 0 0
## surprise 0 0 0 0 0 0 0 0 0 0
## dcaing 0 0 0 0 0 0 0 0 0 0
## strategy 0 0 0 0 0 0 0 0 0 0
## applies 0 0 0 0 0 0 0 0 0 0
## metrics 0 0 0 0 0 0 0 0 0 0
## crashes 0 0 0 0 0 0 0 0 0 0
## btc 0 0 0 0 0 0 0 0 0 0
## valuations 0 0 0 0 0 0 0 0 0 0
## chunk 0 0 0 0 0 0 0 0 0 0
## reassess 0 0 0 0 0 0 0 0 0 0
## day 1 1 1 1 1 1 1 1 0 0
## couple 0 0 0 0 0 0 0 0 0 1
## runs 0 0 0 0 0 0 0 0 0 0
## stocks 0 0 0 0 0 0 0 0 0 0
## merger couple calls yesterdays dip fomo real wouldnt surprise
## shares 0 0 0 0 0 0 0 0 0
## buy 0 0 0 0 0 0 0 0 0
## spacex 0 0 0 0 0 0 0 0 0
## price 0 0 0 0 0 0 0 0 0
## wait 0 0 0 0 0 0 0 0 0
## level 0 0 0 0 0 0 0 0 0
## experts 0 0 0 0 0 0 0 0 0
## don 0 0 0 0 0 0 0 0 0
## bought 0 0 1 1 1 1 1 1 1
## sold 0 0 0 0 0 0 0 0 0
## typical 0 0 0 0 0 0 0 0 0
## lockups 0 0 0 0 0 0 0 0 0
## donâ 0 0 0 0 0 0 0 0 0
## apply 0 0 0 0 0 0 0 0 0
## unique 0 0 0 0 0 0 0 0 0
## multi 0 0 0 0 0 0 0 0 0
## phase 0 0 0 0 0 0 0 0 0
## lock 0 0 0 0 0 0 0 0 0
## release 0 0 0 0 0 0 0 0 0
## schedule 0 0 0 0 0 0 0 0 0
## intended 0 0 0 0 0 0 0 0 0
## facilitate 0 0 0 0 0 0 0 0 0
## trickle 0 0 0 0 0 0 0 0 0
## selling 0 0 0 0 0 0 0 0 0
## massive 0 0 0 0 0 0 0 0 0
## dump 0 0 0 0 0 0 0 0 0
## expiration 0 0 0 0 0 0 0 0 0
## single 0 0 0 0 0 0 0 0 0
## lockup 0 0 0 0 0 0 0 0 0
## date 0 0 0 0 0 0 0 0 0
## historical 0 0 0 0 0 0 0 0 0
## model 0 0 0 0 0 0 0 0 0
## doesnâ 0 0 0 0 0 0 0 0 0
## importantly 0 0 0 0 0 0 0 0 0
## itâ 0 0 0 0 0 0 0 0 0
## floated 0 0 0 0 0 0 0 0 0
## profound 0 0 0 0 0 0 0 0 0
## impact 0 0 0 0 0 0 0 0 0
## future 0 0 0 0 0 0 0 0 0
## prospects 0 0 0 0 0 0 0 0 0
## driving 0 0 0 0 0 0 0 0 0
## extreme 0 0 0 0 0 0 0 0 0
## volatility 0 0 0 0 0 0 0 0 0
## delaying 0 0 0 0 0 0 0 0 0
## inclusion 0 0 0 0 0 0 0 0 0
## major 0 0 0 0 0 0 0 0 0
## passive 0 0 0 0 0 0 0 0 0
## indexes 0 0 0 0 0 0 0 0 0
## keeping 0 0 0 0 0 0 0 0 0
## absolute 0 0 0 0 0 0 0 0 0
## strategic 0 0 0 0 0 0 0 0 0
## control 0 0 0 0 0 0 0 0 0
## firmly 0 0 0 0 0 0 0 0 0
## hands 0 0 0 0 0 0 0 0 0
## elon 0 0 0 0 0 0 0 0 0
## musk 0 0 0 0 0 0 0 0 0
## reserving 0 0 0 0 0 0 0 0 0
## insiders 0 0 0 0 0 0 0 0 0
## investors 0 0 0 0 0 0 0 0 0
## created 0 0 0 0 0 0 0 0 0
## ecosystem 0 0 0 0 0 0 0 0 0
## trades 0 0 0 0 0 0 0 0 0
## scarcity 0 0 0 0 0 0 0 0 0
## fundamental 0 0 0 0 0 0 0 0 0
## performance 0 0 0 0 0 0 0 0 0
## spcx 1 1 1 1 1 1 1 1 1
## personal 0 0 0 0 0 0 0 0 0
## conviction 0 0 0 0 0 0 0 0 0
## targets 0 0 0 0 0 0 0 0 0
## top 0 0 0 0 0 0 0 0 0
## digital 0 0 0 0 0 0 0 0 0
## assets 0 0 0 0 0 0 0 0 0
## solana 0 0 0 0 0 0 0 0 0
## spcx55k 0 0 0 0 0 0 0 0 0
## xrp 0 0 0 0 0 0 0 0 0
## move 0 0 0 0 0 0 0 0 0
## miss 0 0 0 0 0 0 0 0 0
## invest 0 0 0 0 0 0 0 0 0
## current 0 0 0 0 0 0 0 0 0
## beginning 0 0 0 0 0 0 0 0 0
## hype 0 0 0 0 0 0 0 0 0
## continues 0 0 0 0 0 0 0 0 0
## keast 0 0 0 0 0 0 0 0 0
## follow 0 0 0 0 0 0 0 0 0
## portfolio 0 0 0 0 0 0 0 0 0
## drops 0 0 0 0 0 0 0 0 0
## significantly 0 0 0 0 0 0 0 0 0
## start 0 0 0 0 0 0 0 0 0
## dollar 0 0 0 0 0 0 0 0 0
## cost 0 0 0 0 0 0 0 0 0
## averaging 0 0 0 0 0 0 0 0 0
## valuation 0 0 0 0 0 0 0 0 0
## months 0 0 0 0 0 0 0 0 0
## expect 0 0 0 0 0 0 0 0 0
## drop 0 0 0 0 0 0 0 0 0
## panics 0 0 0 0 0 0 0 0 0
## sells 0 0 0 0 0 0 0 0 0
## opportunity 0 0 0 0 0 0 0 0 0
## arises 0 0 0 0 0 0 0 0 0
## collect 0 0 0 0 0 0 0 0 0
## accumulate 0 0 0 0 0 0 0 0 0
## capital 0 0 0 0 0 0 0 0 0
## grandchildren 0 0 0 0 0 0 0 0 0
## remember 0 0 0 0 0 0 0 0 0
## decision 0 0 0 0 0 0 0 0 0
## pre 0 0 0 0 0 0 0 0 0
## ipo 0 0 0 0 0 0 0 0 0
## locked 0 0 0 0 0 0 0 0 0
## days 0 0 0 0 0 0 0 0 0
## sell 0 0 0 0 0 0 0 0 0
## tsla 1 1 0 0 0 0 0 0 0
## merger 0 1 0 0 0 0 0 0 0
## calls 0 0 0 1 1 1 1 1 1
## yesterdays 0 0 1 0 1 1 1 1 1
## dip 0 0 1 1 0 1 1 1 1
## fomo 0 0 1 1 1 0 1 1 1
## real 0 0 1 1 1 1 0 1 1
## wouldnt 0 0 1 1 1 1 1 0 1
## surprise 0 0 1 1 1 1 1 1 0
## dcaing 0 0 0 0 0 0 0 0 0
## strategy 0 0 0 0 0 0 0 0 0
## applies 0 0 0 0 0 0 0 0 0
## metrics 0 0 0 0 0 0 0 0 0
## crashes 0 0 0 0 0 0 0 0 0
## btc 0 0 0 0 0 0 0 0 0
## valuations 0 0 0 0 0 0 0 0 0
## chunk 0 0 0 0 0 0 0 0 0
## reassess 0 0 0 0 0 0 0 0 0
## day 0 0 0 0 0 0 0 0 0
## couple 1 0 0 0 0 0 0 0 0
## runs 0 0 1 1 1 1 1 1 1
## stocks 0 0 0 0 0 0 0 0 0
## runs strategy applies stocks buy personal dcaing
## shares 0 0 0 0 2 0 0
## buy 0 0 0 0 0 0 0
## spacex 0 0 0 0 1 0 0
## price 0 0 0 0 1 0 0
## wait 0 0 0 0 0 0 0
## level 0 0 0 0 1 0 0
## experts 0 0 0 0 1 0 0
## don 0 0 0 0 1 0 0
## bought 1 0 0 0 1 0 0
## sold 0 0 0 0 2 0 0
## typical 0 0 0 0 0 0 0
## lockups 0 0 0 0 0 0 0
## donâ 0 0 0 0 0 0 0
## apply 0 0 0 0 0 0 0
## unique 0 0 0 0 0 0 0
## multi 0 0 0 0 0 0 0
## phase 0 0 0 0 0 0 0
## lock 0 0 0 0 0 0 0
## release 0 0 0 0 0 0 0
## schedule 0 0 0 0 0 0 0
## intended 0 0 0 0 0 0 0
## facilitate 0 0 0 0 0 0 0
## trickle 0 0 0 0 0 0 0
## selling 0 0 0 0 0 0 0
## massive 0 0 0 0 0 0 0
## dump 0 0 0 0 0 0 0
## expiration 0 0 0 0 0 0 0
## single 0 0 0 0 0 0 0
## lockup 0 0 0 0 0 0 0
## date 0 0 0 0 0 0 0
## historical 0 0 0 0 0 0 0
## model 0 0 0 0 0 0 0
## doesnâ 0 0 0 0 0 0 0
## importantly 0 0 0 0 0 0 0
## itâ 0 0 0 0 0 0 0
## floated 0 0 0 0 0 0 0
## profound 0 0 0 0 0 0 0
## impact 0 0 0 0 0 0 0
## future 0 0 0 0 0 0 0
## prospects 0 0 0 0 0 0 0
## driving 0 0 0 0 0 0 0
## extreme 0 0 0 0 0 0 0
## volatility 0 0 0 0 0 0 0
## delaying 0 0 0 0 0 0 0
## inclusion 0 0 0 0 0 0 0
## major 0 0 0 0 0 0 0
## passive 0 0 0 0 0 0 0
## indexes 0 0 0 0 0 0 0
## keeping 0 0 0 0 0 0 0
## absolute 0 0 0 0 0 0 0
## strategic 0 0 0 0 0 0 0
## control 0 0 0 0 0 0 0
## firmly 0 0 0 0 0 0 0
## hands 0 0 0 0 0 0 0
## elon 0 0 0 0 0 0 0
## musk 0 0 0 0 0 0 0
## reserving 0 0 0 0 0 0 0
## insiders 0 0 0 0 0 0 0
## investors 0 0 0 0 0 0 0
## created 0 0 0 0 0 0 0
## ecosystem 0 0 0 0 0 0 0
## trades 0 0 0 0 0 0 0
## scarcity 0 0 0 0 0 0 0
## fundamental 0 0 0 0 0 0 0
## performance 0 0 0 0 0 0 0
## spcx 1 0 0 0 1 0 0
## personal 0 0 0 0 0 0 0
## conviction 0 0 0 0 0 1 0
## targets 0 0 0 0 0 1 0
## top 0 0 0 0 0 1 0
## digital 0 0 0 0 0 1 0
## assets 0 0 0 0 0 1 0
## solana 0 0 0 0 0 1 0
## spcx55k 0 0 0 0 0 1 0
## xrp 0 0 0 0 0 1 0
## move 0 0 0 0 1 0 0
## miss 0 0 0 0 0 0 0
## invest 0 0 0 0 0 0 0
## current 0 0 0 0 0 0 0
## beginning 0 0 0 0 1 0 0
## hype 0 0 0 0 1 0 0
## continues 0 0 0 0 1 0 0
## keast 0 0 0 0 1 0 0
## follow 0 0 0 0 1 0 0
## portfolio 0 0 0 0 1 0 0
## drops 0 0 0 0 1 0 0
## significantly 0 0 0 0 1 0 0
## start 0 0 0 0 1 0 0
## dollar 0 0 0 0 1 0 0
## cost 0 0 0 0 1 0 0
## averaging 0 0 0 0 1 0 0
## valuation 0 0 0 0 0 0 0
## months 0 0 0 0 0 0 0
## expect 0 0 0 0 0 0 0
## drop 0 0 0 0 0 0 0
## panics 0 0 0 0 0 0 0
## sells 0 0 0 0 0 0 0
## opportunity 0 0 0 0 0 0 0
## arises 0 0 0 0 0 0 0
## collect 0 0 0 0 0 0 0
## accumulate 0 0 0 0 0 0 0
## capital 0 0 0 0 0 0 0
## grandchildren 0 0 0 0 0 0 0
## remember 0 0 0 0 0 0 0
## decision 0 0 0 0 0 0 0
## pre 0 0 0 0 0 0 0
## ipo 0 0 0 0 0 0 0
## locked 0 0 0 0 0 0 0
## days 0 0 0 0 0 0 0
## sell 0 0 0 0 0 0 0
## tsla 0 0 0 0 0 0 0
## merger 0 0 0 0 0 0 0
## calls 1 0 0 0 0 0 0
## yesterdays 1 0 0 0 0 0 0
## dip 1 0 0 0 0 0 0
## fomo 1 0 0 0 0 0 0
## real 1 0 0 0 0 0 0
## wouldnt 1 0 0 0 0 0 0
## surprise 1 0 0 0 0 0 0
## dcaing 0 1 1 1 0 0 0
## strategy 0 0 1 1 0 0 1
## applies 0 1 0 1 0 0 1
## metrics 0 0 0 0 0 0 0
## crashes 0 0 0 0 1 0 0
## btc 0 0 0 0 0 1 0
## valuations 0 0 0 0 0 0 0
## chunk 0 0 0 0 1 0 0
## reassess 0 0 0 0 0 0 0
## day 0 0 0 0 0 0 0
## couple 0 0 0 0 0 0 0
## runs 0 0 0 0 0 0 0
## stocks 0 1 1 0 0 0 1Step 5: Visualize Strongest Co-occurrences
Original tutorial threshold (n > 1), then a stricter
threshold for comparison.
set.seed(580)
spacex_pairs %>%
filter(n > 1) %>%
graph_from_data_frame() %>%
ggraph(layout = "fr") +
geom_edge_link(aes(edge_alpha = n, edge_width = n), color = "firebrick") +
geom_node_point(size = 4, color = "steelblue") +
geom_node_text(aes(label = name), repel = TRUE, size = 3.5) +
theme_void() +
labs(
title = "SpaceX Co-occurrence Network (n > 1)",
subtitle = "Edge thickness = number of comments containing both words"
)
# Changed threshold for Question 2
n_threshold <- 2 # final chosen value used in filter(n > n_threshold)
set.seed(580)
spacex_pairs %>%
filter(n > n_threshold) %>%
graph_from_data_frame() %>%
ggraph(layout = "fr") +
geom_edge_link(aes(edge_alpha = n, edge_width = n), color = "firebrick") +
geom_node_point(size = 4, color = "steelblue") +
geom_node_text(aes(label = name), repel = TRUE, size = 3.5) +
theme_void() +
labs(
title = paste0("SpaceX Co-occurrence Network (n > ", n_threshold, ")"),
subtitle = "Stricter filter keeps only the strongest pairs"
)
# Helper for Question 4: what co-occurs with "buy"?
spacex_pairs %>%
filter(item1 == "buy" | item2 == "buy") %>%
arrange(desc(n))## # A tibble: 24 × 3
## item1 item2 n
## <chr> <chr> <dbl>
## 1 buy sold 2
## 2 buy shares 2
## 3 buy level 1
## 4 buy spacex 1
## 5 buy experts 1
## 6 buy don 1
## 7 buy bought 1
## 8 buy move 1
## 9 buy price 1
## 10 buy spcx 1
## # ℹ 14 more rowsReflection & Discussion Questions
Question 1 — Custom stopwords and network change
I manually added stock and
term to the stopword list. Both are
frequent in this investor corpus mostly because every comment is about
equity investing (“stock,” “long term”), not because they mark a
distinctive stance such as bullish timing or caution. Removing them
follows standard tidy-text guidance: drop high-frequency words that
inflate co-occurrence counts without adding interpretive value (Silge
& Robinson, 2017). After removal, the network becomes slightly
cleaner—edges that only tied words together through generic “stock/term”
hub language weaken or disappear—so clusters around action words like
buy, shares, price,
wait, and spacex stand out more
clearly.
Question 2 — Changing filter(n > 1)
My final value is n > 2 (threshold =
2). With only 13 comments, raising the threshold from 1 to 2 keeps
mainly the strongest pair(s) (notably shares–price) and
drops most of the exploratory structure. That is “cleaner,” but not
better for insight here: the graph becomes too sparse to discuss
investor themes. For this small sample,
n > 1 is the more useful default; a
higher n would make more sense with hundreds of comments
(Xu tutorial; Silge & Robinson, 2017).
Question 3 — Which Bluesky co-occurrence graph is better?
Professor Xu’s Bluesky post shows two coffee SEO networks built from the same metadata (Bluesky post):
Left/top: denser network; right/bottom: stricter, hub-focused network.
Left/top: denser network; right/bottom: stricter, hub-focused network.
I prefer the denser first graph. It still centers on
“coffee,” but it also surfaces semantic neighborhoods
(specialty/sourcing, cold brew, roast/espresso) that matter for SEO
co-occurrence strategy—showing Google-relevant context beyond the single
strongest hub edges (Chiericato, 2015). The second graph is easier to
present, but it oversimplifies the topical map by keeping only the
highest-n links.
Question 4 — Surprising pairs; where does “buy” cluster?
A mildly surprising pattern is how often shares and
price travel together, and how wait
pairs with spacex/months, which feels like a
patience/valuation frame rather than FOMO. Looking at neighbors of
buy, the word clusters more with
transaction/exit language (sold,
shares) than with pure optimism (dip appears
only once in the corpus). Caution language (wait,
valuation) does appear in the broader network, but it
is not the dominant immediate neighbor of buy in this
tiny sample—so “buy” here looks more like trading activity than a clean
optimism signal.
Question 5 — Should $SPCX and SpaceX be
the same token?
Usually yes, for this analysis. They refer to the
same entity; keeping them separate splits co-occurrence counts and
weakens the brand/ticker hub artificially (similar to leaving “coffee”
and “coffees” unstemmed in the SEO example). A practical approach is to
normalize both to one canonical token (e.g., spacex) during
cleaning, while optionally retaining a flag if ticker-versus-brand
wording itself is a research question.
Question 6 — Small-sample risk and how to communicate it
Risks include unstable edges, overfitting to a few verbose comments, and mistaking one author’s phrasing for “market sentiment.” Co-occurrence estimates get more trustworthy as the number of independent documents grows; for exploratory classroom work, dozens of comments can illustrate the method, but business claims typically need hundreds to thousands of relevant posts/reviews depending on vocabulary diversity (Silge & Robinson, 2017; general sampling caution in text mining). In a business report I would write: “Findings are directional only; the network is based on N = 13 comments and should be validated on a larger, time-bounded sample before guiding investment-communication or product decisions.”
Question 7 — Event-centered comment/review analysis
Yes. Pick a significant event with a clear timestamp (for SpaceX, an
IPO filing rumor, a major launch success/failure, or a lockup-expiration
window), then collect comments in pre/post windows around that date
using platform search or APIs. The event matters because it shocks
expectations—liquidity, valuation narratives, and risk language often
shift immediately afterward—so co-occurrence networks can be compared
before vs. after to see whether “buy” moves toward optimism words
(dip, accumulate) or caution words
(wait, valuation, sell). That
design turns a static word graph into an event-study style text analysis
useful for marketing, IR, or brand crisis monitoring.
References
Chiericato, E. (2015). Co Citations and Co Occurrences in SEO: The Complete Guide. Web Marketing Academy. https://webmarketing.academy/en/co-citazioni-co-occorrenze-guida-definitiva/
Silge, J., & Robinson, D. (2017). Text Mining with R: A Tidy Approach. O’Reilly Media. https://www.tidytextmining.com/
Xu, Z. (2026). Text Analysis & NLP — Word Co-occurrence Analysis Demo. https://rpubs.com/JimmyXu/1445260
Xu, Z. (2026). Bluesky post on SEO co-occurrence network graphs. https://bsky.app/profile/did:plc:jbmmqoxfdgpoavycm7fz4k3r/post/3mphyccr2vc2g
Robinson, D. (2021). widyr R package. https://CRAN.R-project.org/package=widyr
Pedersen, T. L. (2024). ggraph R package. https://CRAN.R-project.org/package=ggraph