The document discusses a method for predicting Twitter authors based on their tweet content using a Bayesian network. It outlines the process of training the network using specific preprocessing techniques and the implementation details in R and Java, achieving high accuracy in author prediction. Initial results indicate that writing styles, topics, and word distributions are significant factors influencing prediction accuracy.

1. Twitter Author Prediction
from Tweets using
Bayesian Network
Hendy Irawan
23214344
TMDG 9 – Electrical Engineering - STEI ITB

2. Can We Predict the Author from a
Tweet?
 Most authors have a distinct writing style
 ... And unique topics to talk about
 ... And signature distribution of words used to tweet
 Can we train Bayesian Network so that occurrence of words in a tweet can be
used to infer the author of that tweet?
 In summary: YES!
 Disclaimer: Accuracy varies
 In a test suite with @dakwatuna vs @farhatabbaslaw (very different tweet topics)
– 100% prediction accuracy is achieved

3. Analysis & Implementation Plan
 Visualize Word Distribution in Tweets with Word Clouds
 Using R Statistical Language in RStudio
 Implement in Java
 Natural Language Preprocessing
 Train Bayesian Network
 Predict Tweet Author

4. Visualize Word Distribution in Tweets
with Word Clouds
Using R Statistical Language in RStudio
All documentation and sources (open
source) available at:
http://ceefour.github.io/r-tutorials/
 Install R Packages
 libcurl4-openssl-dev, TwitteR,
httpuv, tm, wordcloud,
RColorBrewer
 Setup Twitter Oauth
 Grab Data
 Prepare Stop Words
 Make A Corpus
 Word Cloud

5. 1. Install R Packages

6. 2. Setup Twitter OAuth

7. 3. Grab Data

8. 4. Prepare Stop Words

9. 5. Make A Corpus

10. 6. Visualize Word Cloud: @dakwatuna

11. Word Clouds (2)
@suaradotcom @kompascom

12. Word Clouds (3)
@VIVAnews @liputan6dotcom

13. Word Clouds (3)
@pkspiyungan @MTlovenhoney

14. Word Clouds (4)
@hidcom @farhatabbaslaw

15. Java Implementation
 Natural Language Preprocessing
 Read tweets from CSV
 Lower case
 Remove http(s) links
 Remove punctuation symbols
 Remove numbers
 Canonicalize different word forms
 Remove stop words
 Train Bayesian Network
 Predict Tweet Author
 Initial experiments and dataset
validation available at:
http://ceefour.github.io/r-
tutorials/
 Java application source code (open
source) available on GitHub at:
https://github.com/lumenitb/nlu-
sentiment

16. 1. Read Tweets from CSV
/**
* Read CSV file {@code f} and put its contents into {@link #rows},
* {@link #texts}, and {@link #origTexts}.
* @param f
*/
public void readCsv(File f) {
try (final CSVReader csv = new CSVReader(new FileReader(f))) {
headerNames = csv.readNext(); // header
rows = csv.readAll();
texts = rows.stream().map(it -> Maps.immutableEntry(it[0], it[1]))
.collect(Collectors.toMap(Map.Entry::getKey, Map.Entry::getValue));
origTexts = ImmutableMap.copyOf(texts);
} catch (Exception e) {
throw new RuntimeException("Cannot read " + f, e);
}
}

17. 2. Lower Case
/**
* Lower case all texts.
*/
public void lowerCaseAll() {
texts = Maps.transformValues(texts, String::toLowerCase);
}

18. 3. Remove Links
/**
* Remove http(s) links from texts.
*/
public void removeLinks() {
texts = Maps.transformValues(texts, it -> it.replaceAll("http(s?)://(S+)", " "));
}

19. 4. Remove Punctuation Symbols
/**
* Remove punctuation symbols from texts.
*/
public void removePunctuation() {
texts = Maps.transformValues(texts, it -> it.replaceAll("[^a-zA-Z0-9]+", " "));
}

20. 5. Remove Numbers
/**
* Remove numbers from texts.
*/
public void removeNumbers() {
texts = Maps.transformValues(texts, it -> it.replaceAll("[0-9]+", ""));
}

21. 6. Canonicalize Words
/**
* Canonicalize different word forms using {@link #CANONICAL_WORDS}.
*/
public void canonicalizeWords() {
log.info("Canonicalize {} words for {} texts: {}",
CANONICAL_WORDS.size(), texts.size(), CANONICAL_WORDS);
CANONICAL_WORDS.entries().forEach(entry ->
texts = Maps.transformValues(texts,
it -> it.replaceAll("(W|^)" + Pattern.quote(entry.getValue()) +
"(W|$)", "1" + entry.getKey() + "2"))
);
}
// Define contents of CANONICAL_WORDS
final ImmutableMultimap.Builder<String, String> mmb =
ImmutableMultimap.builder();
mmb.putAll("yang", "yg", "yng");
mmb.putAll("dengan", "dg", "dgn");
mmb.putAll("saya", "sy");
mmb.putAll("punya", "pny");
mmb.putAll("ya", "iya");
mmb.putAll("tidak", "tak", "tdk");
mmb.putAll("jangan", "jgn", "jngn");
mmb.putAll("jika", "jika", "bila");
mmb.putAll("sudah", "udah", "sdh", "dah", "telah", "tlh");
mmb.putAll("hanya", "hny");
mmb.putAll("banyak", "byk", "bnyk");
mmb.putAll("juga", "jg");
mmb.putAll("mereka", "mrk", "mereka");
mmb.putAll("gue", "gw", "gwe", "gua", "gwa");
mmb.putAll("sebagai", "sbg", "sbgai");
mmb.putAll("silaturahim", "silaturrahim", "silaturahmi",
"silaturrahmi");
mmb.putAll("shalat", "sholat", "salat", "solat");
mmb.putAll("harus", "hrs");
mmb.putAll("oleh", "olh");
mmb.putAll("tentang", "ttg", "tntg");
mmb.putAll("dalam", "dlm");
mmb.putAll("banget", "bngt", "bgt", "bingit", "bingits");
CANONICAL_WORDS = mmb.build();

22. 7. Remove Stop Words
/**
* Remove stop words using {@link #STOP_WORDS_ID} and {@code additions}.
* @param additions
*/
public void removeStopWords(String... additions) {
final Sets.SetView<String> stopWords = Sets.union(
STOP_WORDS_ID, ImmutableSet.copyOf(additions));
log.info("Removing {} stop words for {} texts: {}",
stopWords.size(), texts.size(), stopWords);
stopWords.forEach(stopWord ->
texts = Maps.transformValues(texts, it ->
it.replaceAll("(W|^)" + Pattern.quote(stopWord) +
"(W|$)", "12"))
);
}
/**
* Indonesian stop words.
*/
public static final Set<String> STOP_WORDS_ID = ImmutableSet.of(
"di", "ke", "ini", "dengan", "untuk", "yang", "tak", "tidak",
"gak",
"dari", "dan", "atau", "bisa", "kita", "ada", "itu",
"akan", "jadi", "menjadi", "tetap", "per", "bagi", "saat",
"tapi", "bukan", "adalah", "pula", "aja", "saja",
"kalo", "kalau", "karena", "pada", "kepada", "terhadap",
"amp", // &amp;
"rt" // RT:
);

23. 8. Split Text into Words
/**
* Split texts into {@link #words}.
*/
public void splitWords() {
Splitter whitespace = Splitter.on(
Pattern.compile("s+")).omitEmptyStrings().trimResults();
words = Maps.transformValues(texts,
it -> whitespace.splitToList(it));
}

24. Train Bayesian Network
BN Graph model Prior probabilities

25. Train Bayesian Network: Java (1)
/**
* Creates a {@link SentimentAnalyzer} then analyzes the
file {@code f},
* with limiting words to {@code wordLimit} (based on top
word frequency),
* and additional stop words of {@code moreStopWords}
(base stop words
* are {@link SentimentAnalyzer#STOP_WORDS_ID}.
* @param f
* @param wordLimit
* @param moreStopWords
* @return
*/
protected SentimentAnalyzer analyze(File f, int wordLimit,
Set<String> moreStopWords) {
final SentimentAnalyzer sentimentAnalyzer = new
SentimentAnalyzer();
sentimentAnalyzer.readCsv(f);
sentimentAnalyzer.lowerCaseAll();
sentimentAnalyzer.removeLinks();
sentimentAnalyzer.removePunctuation();
sentimentAnalyzer.removeNumbers();
sentimentAnalyzer.canonicalizeWords();
sentimentAnalyzer.removeStopWords(moreStopWords.toArray(ne
w String[] {}));
log.info("Preprocessed text: {}",
sentimentAnalyzer.texts.entrySet().stream().limit(10)
.collect(Collectors.toMap(Map.Entry::getKey,
Map.Entry::getValue)));
sentimentAnalyzer.splitWords();
log.info("Words: {}",
sentimentAnalyzer.words.entrySet().stream().limit(10)
.collect(Collectors.toMap(Map.Entry::getKey,
Map.Entry::getValue)));
final ImmutableMultiset<String> wordMultiset =
Multisets.copyHighestCountFirst(HashMultiset.create(
sentimentAnalyzer.words.values().stream().flatMap(it
-> it.stream()).collect(Collectors.toList())) );
final Map<String, Integer> wordCounts = new
LinkedHashMap<>();
// only the N most used words
wordMultiset.elementSet().stream().limit(wordLimit).
forEach( it -> wordCounts.put(it,
wordMultiset.count(it)) );
log.info("Word counts (orig): {}", wordCounts);
// Normalize the twitterUser "vector" to length
1.0
// Note that this "vector" is actually user-
specific, i.e. it's not a user-independent vector
long origSumSqrs = 0;
for (final Integer it : wordCounts.values()) {
origSumSqrs += it * it;
}
double origLength = Math.sqrt(origSumSqrs);
final Map<String, Double> normWordCounts =
Maps.transformValues(wordCounts, it -> it /
origLength);
log.info("Word counts (normalized): {}",
normWordCounts);
sentimentAnalyzer.normWordCounts =
normWordCounts;
return sentimentAnalyzer;
}

26. Train Bayesian Network: Java (2)
/**
* Train Bayesian network {@code bn}, with help of {@link #analyze(File, int, Set)}.
* @param bn
* @param f
* @param screenName
* @return
*/
protected SentimentAnalyzer train(BayesianNetwork bn, File f, String screenName) {
final SentimentAnalyzer analyzer = analyze(f, 100, ImmutableSet.of(screenName));
allWords.addAll(analyzer.normWordCounts.keySet());
for (final Map.Entry<String, Double> entry : analyzer.normWordCounts.entrySet()) {
wordNormLengthByScreenName.put(screenName + "/" + entry.getKey(), entry.getValue());
}
return analyzer;
}

27. Predict Twitter Author:
“nasional” found
“nasional” found ->
85.37% probability of @dakwatuna
“nasional” found, “olga” missing ->
89.29% probability of @dakwatuna

28. Predict Twitter author:
“olga” found
 @dakwatuna never tweets
about “olga”
 Not even once
 Therefore, BN assumes
100% probability that
@farhatabbaslaw is the
author

29. Predict Twitter Author
 Initial corpus:
 @dakwatuna: 3200 tweets
 @farhatabbaslaw: 3172 tweets
 Split into:
 @dakwatuna
 1000 training tweets
 2200 test tweets
 @farhatabbaslaw:
 1000 training tweets
 2172 test tweets

30. Twitter Author Prediction Test:
@dakwatuna
Classification of 2200 tweets took 7855 ms
~ 3.57 ms per tweet classification
100% accuracy of prediction

31. Twitter Author Prediction Test:
@farhatabbaslaw
Classification of 2172 tweets took 7353 ms
~ 3.38 ms per tweet classification
100% accuracy of prediction

32. Conclusion
 Initial results is promising
 Bayesian Networks is able to predict tweet author with “very good” accuracy
 Note that accuracy depends largely of:
 Twitter author’s writing style
 Twitter author’s topics of interest
 Twitter author’s distribution of words
 In other words, two different authors with similar writing style or topics will
have greater chance of “false positive” prediction