As a part of Twitter Data Analysis, So far I have
completed Movie
review using R & Document
Classification using R

A statistical approach for discovering “abstracts/topics” from a collection of text documents based on statistics of each word. In simple terms, the process of looking into a large collection of documents, identifying clusters of words and grouping them together based on similarity and identifying patterns in the clusters appearing in multitude.

Topic Modeling can be achieved by using Latent Dirichlet Allocation algorithm. Not going into the nuts & bolts of the Algorithm, LDA automatically learns itself to assign probabilities to each & every word in the Corpus and classify into Topics. A simple explanation for LDA could be found here:
Topic modeling using LDA is a very good method
of discovering topics underlying. The analysis will give good results if and
only if we have large set of Corpus.In the above
analysis using tweets from top 5 Airlines, I could find that one of the topics
which people are talking about is about

library("tm")
library("wordcloud")

library("slam")

library("topicmodels")

#Load Text

con <- file("tweets.txt", "rt")

tweets = readLines(con)

#Clean Text

tweets = gsub("(RT|via)((?:\\b\\W*@\\w+)+)","",tweets)

tweets = gsub("http[^[:blank:]]+", "", tweets)

tweets = gsub("@\\w+", "", tweets)

tweets = gsub("[ \t]{2,}", "", tweets)

tweets = gsub("^\\s+|\\s+$", "", tweets)

tweets <- gsub('\\d+', '', tweets)

tweets = gsub("[[:punct:]]", " ", tweets)

corpus = Corpus(VectorSource(tweets))

corpus = tm_map(corpus,removePunctuation)

corpus = tm_map(corpus,stripWhitespace)

corpus = tm_map(corpus,tolower)

corpus = tm_map(corpus,removeWords,stopwords("english"))

tdm = DocumentTermMatrix(corpus) # Creating a Term document Matrix

# create tf-idf matrix

term_tfidf <- tapply(tdm$v/row_sums(tdm)[tdm$i], tdm$j, mean) * log2(nDocs(tdm)/col_sums(tdm > 0))

summary(term_tfidf)

tdm <- tdm[,term_tfidf >= 0.1]

tdm <- tdm[row_sums(tdm) > 0,]

summary(col_sums(tdm))

#Deciding best K value using Log-likelihood method

best.model <- lapply(seq(2, 50, by = 1), function(d){LDA(tdm, d)})

best.model.logLik <- as.data.frame(as.matrix(lapply(best.model, logLik)))

#calculating LDA

k = 50;#number of topics

SEED = 786; # number of tweets used

CSC_TM <-list(VEM = LDA(tdm, k = k, control = list(seed = SEED)),VEM_fixed = LDA(tdm, k = k,control = list(estimate.alpha = FALSE, seed = SEED)),Gibbs = LDA(tdm, k = k, method = "Gibbs",control = list(seed = SEED, burnin = 1000,thin = 100, iter = 1000)),CTM = CTM(tdm, k = k,control = list(seed = SEED,var = list(tol = 10^-4), em = list(tol = 10^-3))))

#To compare the fitted models we first investigate the values of the models fitted with VEM and estimated and with VEM and fixed

sapply(CSC_TM[1:2], slot, "alpha")

sapply(CSC_TM, function(x) mean(apply(posterior(x)$topics, 1, function(z) - sum(z * log(z)))))

Topic <- topics(CSC_TM[["VEM"]], 1)

Terms <- terms(CSC_TM[["VEM"]], 8)

Terms

**.**Today we will be dealing with discovering topics in Tweets, i.e. to mine the tweets data to discover underlying topics– approach known as Topic Modeling.**What is Topic Modeling?**

A statistical approach for discovering “abstracts/topics” from a collection of text documents based on statistics of each word. In simple terms, the process of looking into a large collection of documents, identifying clusters of words and grouping them together based on similarity and identifying patterns in the clusters appearing in multitude.

Consider the below Statements:

- I love
playing cricket.
- Sachin is my
favorite cricketer.
- Titanic is
heart touching movie.
- Data
Analytics is next Future in IT.
- Data
Analytics & Big Data complements each other.

When we apply Topic Modeling to the above statements,
we will be able to group statement

**1&2**as**Topic-1**(later we can identify that the topic is**Sport**)**,**statement**3**as**Topic-2**(topic is**Movies**)**,**statement**4&5**as**Topic-3**(topic is**data Analytics**).
fig:
Identifying topics in Documents and classifying as Topic 1 & Topic 2

**Latent Dirichlet Allocation algorithm (LDA):**

Topic Modeling can be achieved by using Latent Dirichlet Allocation algorithm. Not going into the nuts & bolts of the Algorithm, LDA automatically learns itself to assign probabilities to each & every word in the Corpus and classify into Topics. A simple explanation for LDA could be found here:

**Twitter Data Analysis Using LDA:**

Steps Involved:

- Fetch tweets
data using ‘
**twitteR**’ package. - Load the data
into the R environment.
- Clean the
Data to remove: re-tweet information, links, special characters,
emoticons, frequent words like is, as, this etc.
- Create a Term
Document Matrix (TDM) using ‘
**tm’**Package. - Calculate TF-IDF i.e. Term
Frequency Inverse Document Frequency for all the words in word matrix
created in Step 4.
- Exclude all
the words with tf-idf <= 0.1, to remove all the words which are less
frequent.
- Calculate the
optimal Number of topics (K) in the Corpus using log-likelihood method for
the TDM calculated in Step6.
- Apply LDA
method using
**‘topicmodels’**Package to discover topics. - Evaluate the model.

**Conclusion:**

**FOOD**being served. We can Sentiment Analysis techniques to mine what people thinks about, talks about products/companies etc.

**SourceCode:**

library("tm")

library("slam")

library("topicmodels")

#Load Text

con <- file("tweets.txt", "rt")

tweets = readLines(con)

#Clean Text

tweets = gsub("(RT|via)((?:\\b\\W*@\\w+)+)","",tweets)

tweets = gsub("http[^[:blank:]]+", "", tweets)

tweets = gsub("@\\w+", "", tweets)

tweets = gsub("[ \t]{2,}", "", tweets)

tweets = gsub("^\\s+|\\s+$", "", tweets)

tweets <- gsub('\\d+', '', tweets)

tweets = gsub("[[:punct:]]", " ", tweets)

corpus = Corpus(VectorSource(tweets))

corpus = tm_map(corpus,removePunctuation)

corpus = tm_map(corpus,stripWhitespace)

corpus = tm_map(corpus,tolower)

corpus = tm_map(corpus,removeWords,stopwords("english"))

tdm = DocumentTermMatrix(corpus) # Creating a Term document Matrix

# create tf-idf matrix

term_tfidf <- tapply(tdm$v/row_sums(tdm)[tdm$i], tdm$j, mean) * log2(nDocs(tdm)/col_sums(tdm > 0))

summary(term_tfidf)

tdm <- tdm[,term_tfidf >= 0.1]

tdm <- tdm[row_sums(tdm) > 0,]

summary(col_sums(tdm))

#Deciding best K value using Log-likelihood method

best.model <- lapply(seq(2, 50, by = 1), function(d){LDA(tdm, d)})

best.model.logLik <- as.data.frame(as.matrix(lapply(best.model, logLik)))

#calculating LDA

k = 50;#number of topics

SEED = 786; # number of tweets used

CSC_TM <-list(VEM = LDA(tdm, k = k, control = list(seed = SEED)),VEM_fixed = LDA(tdm, k = k,control = list(estimate.alpha = FALSE, seed = SEED)),Gibbs = LDA(tdm, k = k, method = "Gibbs",control = list(seed = SEED, burnin = 1000,thin = 100, iter = 1000)),CTM = CTM(tdm, k = k,control = list(seed = SEED,var = list(tol = 10^-4), em = list(tol = 10^-3))))

#To compare the fitted models we first investigate the values of the models fitted with VEM and estimated and with VEM and fixed

sapply(CSC_TM[1:2], slot, "alpha")

sapply(CSC_TM, function(x) mean(apply(posterior(x)$topics, 1, function(z) - sum(z * log(z)))))

Topic <- topics(CSC_TM[["VEM"]], 1)

Terms <- terms(CSC_TM[["VEM"]], 8)

Terms

Your site may be amazing and furthermore require awesome open on your blog bit of paper. Not too bad introduction keep engraving. I totally cherished the manner in which you reviewed this put. The substance are written positively and all the wordings are extremely straightforward. This blog is one in my top choice. Continue sharing extra supportive and useful posts. Feel free to visit site Cheap essay writing service...

ReplyDeleteThis comment has been removed by the author.

ReplyDeleteThank you so much for this article!

ReplyDeleteI am an absolute newbie to R and topic modeling, I would like to do a LDA analysis on a corpus of 7000+ articles containing a certain term in order to understand the topics associated with said term. I dowloaded the articles and now I have a folder with 53 .html files... from here, I really don't know what to do. I have been looking for manuals, tutorial and explanations but they're all too "basic" (beginner guides to R) or too complex for me (in-depth insights on topic modeling).

I know the theory behind, i.e. what steps are involved in such a topic modeling, but I am having a hard time coding.

Could you help me out?

Some of those taxes and rules are federally regulated and they are therefore consistent across Canada, but a majority of are specific Alberta. canadian mortgage calculator If you do not choose to get CMA PRO, you are able to continue while using the Canadian Mortgage App free of charge forever. mortgage calculator canada

ReplyDeleteHi! I know this is kind of off topic but I was wondering which blog platform are you using for this website? I'm getting sick and tired of Wordpress because I've had issues with hackers and I'm looking at options for another platform. I would be great if you could point me in the direction of a good platform. Stretch mark removal Singapore

ReplyDeletekarabük

ReplyDeletesiirt

niğde

düzce

karaman

0Y4HJF

yurtdışı kargo

ReplyDeleteresimli magnet

instagram takipçi satın al

yurtdışı kargo

sms onay

dijital kartvizit

dijital kartvizit

https://nobetci-eczane.org/

HL350A

Eskişehir

ReplyDeleteDenizli

Malatya

Diyarbakır

Kocaeli

H5İF

görüntülü.show

ReplyDeletewhatsapp ücretli show

MV2G

https://titandijital.com.tr/

ReplyDeletenevşehir parça eşya taşıma

bolu parça eşya taşıma

batman parça eşya taşıma

bayburt parça eşya taşıma

FNEİ5D

antalya evden eve nakliyat

ReplyDeleteankara evden eve nakliyat

bursa evden eve nakliyat

yalova evden eve nakliyat

gümüşhane evden eve nakliyat

YEY1

DDF39

ReplyDeleteRize Şehirler Arası Nakliyat

Ankara Boya Ustası

Bilecik Şehir İçi Nakliyat

Çorum Evden Eve Nakliyat

Eryaman Boya Ustası

Mamak Boya Ustası

Karapürçek Fayans Ustası

Gölbaşı Parke Ustası

Kocaeli Şehirler Arası Nakliyat

CB9F3

ReplyDeleteAdana Evden Eve Nakliyat

Niğde Evden Eve Nakliyat

Gümüşhane Evden Eve Nakliyat

Kütahya Evden Eve Nakliyat

Afyon Evden Eve Nakliyat

buy trenbolone enanthate

Kırşehir Evden Eve Nakliyat

Bayburt Evden Eve Nakliyat

Silivri Parke Ustası

96C4B

ReplyDeleteAfyon Evden Eve Nakliyat

Sinop Şehir İçi Nakliyat

Karapürçek Boya Ustası

Çerkezköy Oto Elektrik

Kırklareli Şehirler Arası Nakliyat

Maraş Şehirler Arası Nakliyat

Elazığ Parça Eşya Taşıma

Ankara Boya Ustası

Tekirdağ Şehir İçi Nakliyat

243FE

ReplyDelete%20 indirim kodu

A6DE3

ReplyDeletemersin ücretsiz sohbet sitesi

sesli sohbet sesli chat

sohbet chat

malatya telefonda kızlarla sohbet

sohbet sitesi

niğde goruntulu sohbet

tunceli canlı sohbet odası

Bartın En İyi Ücretsiz Sohbet Siteleri

rastgele sohbet odaları

CC26D

ReplyDeleteAğın

Çemişgezek

Çelebi

Mazgirt

Hadim

Bayramören

Yayladere

Hozat

Pülmür

59CBB

ReplyDeletepapatya sabunu

en az komisyon alan kripto borsası

rastgele canlı sohbet

paribu

huobi

toptan mum

okex

referans kod

bitcoin ne zaman çıktı

7A61E

ReplyDeletebinance

bitcoin nasıl oynanır

kripto para telegram

4g mobil proxy

binance

btcturk

telegram kripto para

bitcoin nasıl üretilir

bibox

3E588

ReplyDeletebinance 100 dolar

bybit

4g mobil

paribu

kucoin

kraken

bitcoin ne zaman çıktı

mexc

coin nereden alınır

42AF2

ReplyDeletebitcoin nasıl üretilir

binance

poloniex

bitget

bybit

bingx

gate io

February 2024 Calendar

March 2024 Calendar

8BA0F

ReplyDeletewhatsapp ucretli show

Very informative post ! There is a lot of information there. thanks for sharing. Delhi to Kainchi Dham Bus

ReplyDelete