Pemodelan topik pada tweet

Menganalisis Data Media Sosial dengan R

Vivek Vijayaraghavan

Data Science Coach

Ikhtisar Pelajaran

Dasar pemodelan topik
Buat document term matrix (DTM)
Bangun model topik dari DTM

Topik dan Dokumen

Definisi topik dan contoh

Topik dan Dokumen

Definisi dokumen dan contoh

Pemodelan topik

Tugas menemukan topik secara otomatis
Ekstrak topik inti dari dataset besar
Ringkas informasi luas menjadi topik

Cara kerja LDA

Algoritme Latent Dirichlet Allocation untuk pemodelan topik

Cara kerja LDA

Document term matrix (DTM)

Buat document term matrix
DTM adalah representasi korpus dalam bentuk matriks
Dokumen sebagai baris, kata/term sebagai kolom

Document term matrix atau DTM

Buat document term matrix

# Create a document term matrix
dtm <- DocumentTermMatrix(twt_corpus_refined)

Buat document term matrix

# Inspect the DTM
inspect(dtm)

Buat document term matrix

<<DocumentTermMatrix (documents: 1000, terms: 5079)>>
Non-/sparse entries: 12862/5066138
Sparsity           : 100%
Maximal term length: 29
Weighting          : term frequency (tf)
Sample             :
     Terms
Docs    california child diabetes fat food health people ranks rates weight
  131          0     0        0   0    0      0      0     0     0      0
  161          0     0        0   2    0      0      0     0     0      1
  295          0     0        0   0    1      0      1     0     0      0
  418          0     0        0   0    0      0      0     0     1      0
  604          0     0        1   0    0      1      0     0     0      0

Menyiapkan DTM

Saring DTM untuk baris dengan jumlah baris > 0

# Find the sum of word counts in each Document 
rowTotals <- apply(dtm , 1, sum)

# Select rows from DTM with row totals greater than zero
tweet_dtm_new <- dtm[rowTotals> 0, ]

Bangun model topik

Buat model topik dengan fungsi LDA()

# Build the topic model
library(topicmodels)
lda_5 <- LDA(tweet_dtm_new, k = 5)

Bangun model topik

Mengekstrak 5 topik dari korpus tweet

# View top 10 terms in the topic model
top_10terms <- terms(lda_5,10)
top_10terms

Lihat 10 term teratas dalam model topik

     Topic 1        Topic 2        Topic 3     Topic 4      Topic 5   
 [1,] "disease"      "people"       "black"     "child"      "weight"  
 [2,] "health"       "health"       "fat"       "rates"      "diet"    
 [3,] "cancer"       "diabetes"     "trump"     "ranks"      "food"    
 [4,] "meghanmccain" "overweight"   "childhood" "california" "diabetes"
 [5,] "realcandaceo" "fat"          "health"    "fat"        "health"  
 [6,] "food"         "meghanmccain" "professor" "eat"        "bmi"     
 [7,] "risk"         "realcandaceo" "gender"    "people"     "problem" 
 [8,] "heart"        "body"         "studies"   "epidemic"   "eating"  
 [9,] "weight"       "weight"       "healthy"   "health"     "disease" 
[10,] "diabetes"     "obese"        "problem"   "healthy"    "family"

Program penanganan obesitas dapat berfokus pada topik inti

Ayo berlatih!

Menganalisis Data Media Sosial dengan R