Step by Step Sentiment analysis on Twitter data using R with Airtel Tweets: Part – III

After lot of difficulties my 3rd post on this topic in this weekend. In my first post we saw what is sentiment analysis and what are the steps involved in it. In my previous post we saw how to retrieve the tweets and store it in the File step by step. Now we will move on to the step of Sentiment analysis.

Goal: To do sentiment analysis on Airtel Customer support via Twitter in India.

In this Post: We will retrieve the Tweets which are retrieved and stored in the previous post and start doing the analysis. In this post I’m going to use the simple algorithm as used by Jeffrey Breen to determine the scores/moods of the particular brand in twitter.

We will use the opinion lexicon provided by him which is primarily based on Hu and Liu papers. You can visit their site for lot of useful information on sentiment analysis. We can determine the positive and negative words in the tweets, based on which scoring will happen.

Step 1: We will import the CSV file into R using read.csv and you can use the summary to display the summary of the dataframe.

Step 2: We can load the Positive words and Negative words and store it locally and can import using Scan function as given below:


Step 3:

Now we will look at the code for evaluating the Sentiment. This has been taken from http://jeffreybreen.wordpress.com/2011/07/04/twitter-text-mining-r-slides/. Thanks for the source code by Jeffrey.


Step 4:

We will test this sentiment.score function with some sample data.


In this step we have created test and added 3 sentences to it. This contains different words which may be positive or negative. Pass this “Test” to the score.sentiment function with pos_words and neg_words which we have loaded in the previous tests. Now you get the result score from the score.sentiment function against each sentence.

we will also try understand little more about this function and what it does:

a. Two libraries are loaded they are plyr and stringr. Both written by Hadley Wickham one of the great contributor to R. You can also learn more about plyr using this page or tutorial. You can also get more insights on split-apply-combine details here best place to start according to Hadley Wickham. You can think of it on analogy with Map-Reduce algorithm by Google which is used more in terms of Parallelism. stringr makes the string handling easier.

b. Next laply being used. You can learn more on what apply functions do here. In our case we pass on the sentences vector to the laply method. In simple terms this method takes each tweet and pass on to the function along with Positive and negative words and combines the result.

c. Next gsub helps to handle the replacements with the help using gsub(pattern, replacement, x).

d. Then convert the sentence to lowercase

e. Convert the sentences to words using the split methods and retrieve the appropriate scores using score methods.

Step 5: Now we will give the tweetsofaritel from airteltweetdata$text to the sentiment function to retrieve the score.

Step 6: We will see the summary of the scores and its histogram:

The histogram outcome:

It shows the most of the response out of 1499 is negative about airtel.

Disclaimer: Please note that this is only sample data which is analyzed only for the purpose of educational and learning purpose. It’s not to target any brand or influence any brand.

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Step by Step Sentiment analysis on Twitter data using R with Airtel Tweets: Part – II

In the previous post we saw what is sentiment analysis and what are the steps involved in it. In this post we will go through step by step instruction on doing Sentiment Analysis on the Micro blogging site “Twitter”. We will have specific objective to do so. I came across an interesting post by Chetan S on the DTH operators involvement in using Social Media for providing customer support. It triggered me the idea for this post.

Goal: To do sentiment analysis on Airtel Customer support via Twitter in India.

In this Post: We will retrieve the Tweets, look at how to access the Twitter API and make best use of the TwitteR R package and write these tweets to a file.

Important Note:

1. when you would like to use the searchTwitter, go to dev.twitter.com and your application go to the “Settings” tab and select “Read, Write and Access direct messages”. Make sure to click on the save button after doing this.”

Refer to this link http://stackoverflow.com/questions/15713073/twitter-help-unable-to-authorize-even-with-registering

2. When you are trying to search using searchTwitter after the above step if you get ssl problem make sure you have enable rCurl and do the steps outline here: http://stackoverflow.com/questions/15347233/ssl-certificate-failed-for-twitter-in-r.

options(RCurlOptions = list(cainfo = system.file(“CurlSSL”, “cacert.pem”, package = “RCurl”))) also make sure you have loaded the Necessary Packages like ROAuth,

Step 1: Make sure you have done the OAuth authentication with Twitter using the Previous post and the steps outlined above, you can also check the library loaded with sessionInfo(). Step 2: Make sure you load the tweets from the Twitter from the Twitter Handle accordingly > airtel.tweets=searchTwitter(“@airtel_presence”,n=1500) Now we have loaded the 1499 tweets which was responded by the Twitter API in to airtel.tweets. Now what we will do is to save these to a file for future processing. Step 3: Before we write these tweets to a file, for better understanding we will try to look at some of the tweets and data collected so far. head(airtel.tweets) provides the top 6 tweets. Further to our analysis, we try to get the length of the tweets, what kind of class it is and how can we access the tweets. Look at the below given screenshot. Step 4: We will look at some examples of How to access the twitter data in a better fashion with respect to the Twitter API using TwitteR library by accessing one tweets from the 1499 available. In this above given example we have selected the 3rd item from the list and we have tried to get till the user information, how many friends he has and how many followers he has, etc., These are the things which are vital to understand as these factors can become viral and impact the image of a particular brand. Now will go to the next step of identifying the steps to store these tweets for further analysis. Step 5: We will store these tweets we collected in airtel.tweets to a file for future analysis and reference. We are going to convert the list of tweets to separate data using apply functions and write to a file. We are going to use the library plyr for the same. Plyr allows the user to split a data set apart into smaller subsets, apply methods to the subsets, and combine the results. Please click here for detailed introduction on plyr. So we are converting the list to data frame for preparing it to be written to a file. Now the tweets and all the necessary information is available in the tweets.df data frame. You can look at the below screenshots for its summary. Step 6: Setup the Working directory and write the tweets.df data frame to the file airteltweets.csv. You can verify the data available in this file using Notepad++ or Excel In the next post we will look at how to do sentiment analysis with this file data.

Sentiment Analysis on Twitter data using R: Part – I

Now, the past posts we have understood the importance of using Twitter API, Basics of Twitter API and how we can access the Twitter API using R. Now we will get into analytics of how to do sentiment analysis with R with the library TwitteR. Before we do that we will try to do little understanding of Sentiment Analysis(some times also called as opinion mining) in a Q & A Format.

What is Sentiment Analysis ?

In simple words, Sentiment analysis is the task of identifying whether the opinion expressed in a text is positive or negative in general about a particular topic or context.

Can we have some examples?

a. I’m in a happy mood today, I go to beach. – Positive

b. I very much like R and its capabilities – Positive

c. I don’t like SPSS, its very complex to use – Negative

d. I feel Rapid Miner is easy to use and has good interface – Positive

Where it is being used or what are its applications?

With the lot of micro-blogging platforms available and business are well placed there, it’s important to understand that sentiment analysis on those platforms help understand the problems and feel of the customers.

  • Understanding customer feedback received
  • To arrive at happiness index of the customers
  • Determining product recommendations
  • Predicting Stock market moods
  • As a competitive marketing tool

Steps for Sentiment Analysis?

How do we do the Practical implementation?

http://www.slideshare.net/jeffreybreen/r-by-example-mining-twitter-for (The one I Like the most).

http://stackoverflow.com/questions/10233087/sentiment-analysis-using-r

https://sites.google.com/site/miningtwitter/questions/sentiment

http://trestletechnology.net/2011/11/sermon-sentiment-analysis/

You can look at the above given references for the practical implementation of Sentiment Analysis. Some of them may be outdated, in the next post we will do a practical step by step implementation of Sentiment Analysis with Twitter data using R.

Getting started with TwitteR Package

The intention of this blog post is to give you start on using the TwitteR Package of R. Using this package you can do lot of analysis on social media “Twitter”. I have written an post on analyzing a Cricketer’s Century Tweets and also the need for analyzing tweets already in my blog.

Pre-Requisite tools & Environment:

We are going to explore this completely with Windows 7 and R.

Steps to follow:

Step 1:We need to use the TwitteR package and ROAuth package for accessing the tweets. As per the recommendation from Twitter its always safe to access the tweets via SSL. First we will see the code for the same.

#install the basic packages

install.packages(“ROAuth”)

install.packages(“twitteR”)

#Initiate/Invoke the libraries

library(“ROAuth”)

library(“twitteR”)

#necessary step for Windows to handle the SSL Part

download.file(url=”http://curl.haxx.se/ca/cacert.pem”, destfile=”cacert.pem”)

Step 2: Use the OAuthFactory to setup the Credentials and start accessing data in the following way

cred <- OAuthFactory$new(consumerKey=’azbiz8LbVeA0lBUVh3c6lA‘,

consumerSecret=’Sq5kNMbdYoxNc616urV1Ayi0rKizwePRg2tDkIUEk‘,

requestURL=’https://api.twitter.com/oauth/request_token&#8217;,

accessURL=’http://api.twitter.com/oauth/access_token&#8217;,

authURL=’http://api.twitter.com/oauth/authorize&#8217;)


After this you can notice that “handshakeComplete” is FALSE. We need to complete the handshake to get access to the TwitterAPI and its data.

Step 3: Create a handshake with twitter, for which you will get a message like the following:

To enable the connection, please direct your web browser to:
http://api.twitter.com/oauth/authorize?oauth_token=ZS2khFL8LZmd4XZ92yeCjcchX08E80g3uzUucv6ds
When complete, record the PIN given to you and provide it here: install.packages("ROAuth")
Error: Unauthorized

Once you naviage to the URL you will get a PIN which you should type in the R Console. Now you can see that we have enterered the PIN from the browser after authorizing the Application.

You can also realize that now the “handshakeComplete” has become TRUE.

Step 4: Verify the status of OAuth authentication using the following command and it should return TRUE.

registerTwitterOAuth(cred)


Step 5: Now the next step is to start accessing the data using TwitterAPI. Let’s try to get started with accessing the User Information.

userInfo<-getUser(“seesiva”, cainfo=”cacert.pem”)

You need to make sure that you also pass the cainfo otherwise you will get an SSL Error.


Hope now we understand the steps required for accessing the Twitter data using the TwitteR Package. In this example we had shown the various attributes of the User Object retrieval. In the next post we will try to analyze some data.

Similar posts for your reference:

http://yourwhatyourepeatedlydo.blogspot.in/2013/04/downloading-twitter-data-using-r.html

http://davetang.org/muse/2013/04/06/using-the-r_twitter-package/

Twitter Analytics & Business impact (The 140 Character thing)

Hopefully everyone who reads this blog would have a twitter account. Not a necessary evil, huh?

Have you ever wondered whats going on about you, your business, the technology you’re in the twitter world. Welcome to Twitter Analytics.  Though twitter API has given as much of exposure to understand these woes better.

Why should I worry about twitter when I have plenty of things to worry about my business:

a. You can find your customers talking about you bad and good which would like to know for dynamically align your business strategy

b. You can acquired customers, you will come to know about customer problems

c. You can predict the online presence and your brand value with the help of your followers and their growth

d. Its real-time and can be viral

e. Above all its gets you closer to the customer

In the following posts we will see more how we can do analytics on the tweets.

Analysis of Cricketer “Dhoni’s 200” tweets on Twitter using R

What an innings of 200 on Day 3 in Chennai yesterday. I loved it. Just thought of exploring what people think on twitter about his 200 is what triggered me to write this blog but unfortunately it required lot of learning on using Twitter with R which I have summed it below. Irrespective of the intent behind analyzing Dhoni’s 200 data it also makes lot of business sense to analyze on trends in social media. In a bid to understand how the social media is dealing with your brand or products it’s important to analyze the data available in twitter. I’m trying to use R for fundamental analysis of tweets based on the TwitteR package available with R.

  1. If you have not installed the twitteR package you need to use the command install.packages(“twitter”)
  2. It will also install the necessary dependencies of that package(RCurl, bitops,rJson).
  3. Load the twitter package using library(twitter)

  4. In the above R console statements I tried to get the maximum tweets upto 1000, but I managed to get only up to 377 tweets. That’s the reason you are seeing n=377, otherwise it returned me error “Error: Malformed response from server, was not JSON”
  5. If you don’t mention value of n , by default it will return 25 records which you can determine using length(dhoni200_tweets)
  6. Next we need to analyze the tweets, so installing the Textmining package “tm”

  7. Next step is to give the tweets which we have collected to the text mining but for doing so we need to convert the tweets into data frame use the following commands to do so:

    > dhoni200_df=do.call(“rbind”,lapply(dhoni200_tweets,as.data.frame))

    > dim(dhoni200_df)

    [1] 377 10

    > dhoni200_df

  8. Next we need move the textdata as vectorSource to Corpus. Using the command > dhoni200.corpus=Corpus(VectorSource(dhoni200_df$text))
  9. When we issue the command > dhoni200.corpus you will get the result “A corpus with 377 text documents”
  10. Next refine the content by converting to lowercase, removing punctuation and unwanted words and convert to a term document matrix:

    > dhoni200.corpus=tm_map(dhoni200.corpus,tolower)

    > dhoni200.corpus=tm_map(dhoni200.corpus,removePunctuation)

    > mystopwords=c(stopwords(‘english’),’profile’,’prochoice’)

    > dhoni200.corpus=tm_map(dhoni200.corpus,removeWords,mystopwords)

    > dhoni200.dtm=TermDocumentMatrix(dhoni200.corpus)

    > dhoni200.dtm

    A term-document matrix (783 terms, 377 documents)

    Non-/sparse entries: 3930/291261

    Sparsity : 99%

    Maximal term length: 23

    Weighting : term frequency (tf)

  11. Analysis: When we try to analyze the words which has occurred 30 and 50 times respectively these were the results:

  12. Analysis: I tried to analyze further the association words when we use the word “century”. The following were the results:

    The term firstever seems to be of the highest with 0.61. In this command findAssocs the number 0.20 is the correlation factor.

  13. The command names(dhoni200_df) will list you the various columns which are coming out as tweets when converted to a data frame.

    [1] “text” “favorited” “replyToSN” “created” “truncated”

    [6] “replyToSID” “id” “replyToUID” “statusSource” “screenName”

  14. Analysis: Most number of tweets

    > counts=table(dhoni200_df$screenName)

    > barplot(counts)