BUS5CA Assignment 1: Social Media Analysis Using SAS Text Miner

This assignment, completed for the BUS5CA Customer Analytics and Social Media course, utilizes SAS Text Miner to analyze social media data and understand customer preferences. The task involves extracting keywords from article titles across different data channels and identifying the top 10 most used topics in each category. The student employs the SAS Result window to explain the findings. Furthermore, the assignment investigates the relationship between topics and the number of shares, identifying common topics associated with both high and low share counts. The analysis uses the entire dataset, segmented into high and low share groups based on the top and bottom 10% of the dataset. The 'Title' column is used as the 'Text' role for topic modeling, and the results are supported by explanations. The solution includes code to process and analyze the data, including descriptive statistics, word count analysis, and the creation of word clouds and visualizations using Python libraries such as pandas, nltk, and sklearn. The analysis involves identifying common and uncommon words, text preprocessing techniques such as removing punctuation, converting text to lowercase, removing tags, stemming, and lemmatization. The code generates word clouds and analyzes the frequency of words, bi-grams, and tri-grams, providing insights into the most prominent themes within the dataset. The assignment aims to uncover the impacts of online advertising and communication with customers, educating marketing teams on maximizing customer involvement.

Use the SAS Text Miner to extract the keywords from the title in each data channel.
What are the highly used (top 10) topics in each category? Use the SAS Result window
to explain your answers.
(Hint: ‘Topic’ column will need to be set as the only ‘Text’ role.)
Are there common topics which span across data channels and relate to a high
number of shares and a low number of shares? Use the whole dataset in the SAS Text
Miner to identify the relationship. You should provide the explanation to support
your
argument.
(Hint: Use the whole dataset to identify the articles with the high number of shares
and the low number of shares – by using appropriate thresholds with the top 10%
and the bottom 10% in the dataset. Separate the dataset using Excel based on this
before the analysis and use these two datasets to analyse the common topics in each
of them. In this question, please use ‘Title’ column as the only ‘Text’ role for topic
modelling.)
CODE:
import pandas
# load the dataset
dataset = pandas.read_excel (r'online_popularity_data.xlsx')
dataset.head()

##Descriptive statistics of word counts
dataset.word_count.describe()
#Fetch wordcount for each Title
dataset['word_count'] = dataset['Title'].apply(lambda x: len(str(x).split(" ")))
dataset[['Title','word_count']].head()
#Identify common words
freq = pandas.Series(' '.join(dataset['Title']).split()).value_counts()[:20]
freq
#Identify uncommon words
freq1 = pandas.Series(' '.join(dataset
['Title']).split()).value_counts()[-20:]
freq1
# Libraries for text preprocessing
import re
import nltk
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
from nltk.tokenize import RegexpTokenizer
from nltk.stem.wordnet import WordNetLemmatizer

##Creating a list of stop words and adding custom stopwords
stop_words = set(stopwords.words("english"))
##Creating a list of custom stopwords
new_words = ["using", "show", "result", "large", "also", "iv", "one", "two", "new", "previously",
"shown"]
stop_words = stop_words.union(new_words)
corpus = []
for i in range(0, 3847):
#Remove punctuations
text = re.sub('[^a-zA-Z]', ' ', dataset['Title'][i])
#Convert to lowercase
text = text.lower()
#remove tags
text=re.sub("</?.*?>"," <> ",text)
# remove special characters and digits
text=re.sub("(\\d|\\W)+"," ",text)
##Convert to list from string
text = text.split()
##Stemming
ps=PorterStemmer()
#Lemmatisation
lem = WordNetLemmatizer()
text = [lem.lemmatize(word) for word in text if not word in

stop_words]
text = " ".join(text)
corpus.append(text)
#View corpus item
corpus[222]
#Word cloud
from os import path
from PIL import Image
from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator
import matplotlib.pyplot as plt
%matplotlib inline
wordcloud = WordCloud( background_color='white',
stopwords=stop_words,
max_words=100,
max_font_size=50,
random_state=42
).generate(str(corpus))
print(wordcloud)
fig = plt.figure(1)
plt.imshow(wordcloud)
plt.axis('off')
plt.show()
fig.savefig("word1.png", dpi=900)
#Creating a vector of word counts
from sklearn.feature_extraction.text import CountVectorizer
import re

cv=CountVectorizer(max_df=0.8,stop_words=stop_words, max_features=10000, ngram_range=(1,3))
X=cv.fit_transform(corpus)
list(cv.vocabulary_.keys())[:10]
#Most frequently occuring words
def get_top_n_words(corpus, n=None):
vec = CountVectorizer().fit(corpus)
bag_of_words = vec.transform(corpus)
sum_words = bag_of_words.sum(axis=0)
words_freq = [(word, sum_words[0, idx]) for word, idx in
vec.vocabulary_.items()]
words_freq =sorted(words_freq, key = lambda x: x[1],
reverse=True)
return words_freq[:n]
#Convert most freq words to dataframe for plotting bar plot
top_words = get_top_n_words(corpus, n=20)
top_df = pandas.DataFrame(top_words)
top_df.columns=["Word", "Freq"]
#Barplot of most freq words
import seaborn as sns
sns.set(rc={'figure.figsize':(13,8)})
g = sns.barplot(x="Word", y="Freq", data=top_df)
g.set_xticklabels(g.get_xticklabels(), rotation=30)
#Most frequently occuring Bi-grams
def get_top_n2_words(corpus, n=None):

vec1 = CountVectorizer(ngram_range=(2,2),
max_features=2000).fit(corpus)
bag_of_words = vec1.transform(corpus)
sum_words = bag_of_words.sum(axis=0)
words_freq = [(word, sum_words[0, idx]) for word, idx in
vec1.vocabulary_.items()]
words_freq =sorted(words_freq, key = lambda x: x[1],
reverse=True)
return words_freq[:n]
top2_words = get_top_n2_words(corpus, n=20)
top2_df = pandas.DataFrame(top2_words)
top2_df.columns=["Bi-gram", "Freq"]
print(top2_df)
#Barplot of most freq Bi-grams
import seaborn as sns
sns.set(rc={'figure.figsize':(13,8)})
h=sns.barplot(x="Bi-gram", y="Freq", data=top2_df)
h.set_xticklabels(h.get_xticklabels(), rotation=45)
#Most frequently occuring Tri-grams
def get_top_n3_words(corpus, n=None):
vec1 = CountVectorizer(ngram_range=(3,3),
max_features=2000).fit(corpus)
bag_of_words = vec1.transform(corpus)
sum_words = bag_of_words.sum(axis=0)
words_freq = [(word, sum_words[0, idx]) for word, idx in
vec1.vocabulary_.items()]
words_freq =sorted(words_freq, key = lambda x: x[1],

reverse=True)
return words_freq[:n]
top3_words = get_top_n3_words(corpus, n=20)
top3_df = pandas.DataFrame(top3_words)
top3_df.columns=["Tri-gram", "Freq"]
print(top3_df)
#Barplot of most freq Tri-grams
import seaborn as sns
sns.set(rc={'figure.figsize':(13,8)})
j=sns.barplot(x="Tri-gram", y="Freq", data=top3_df)
j.set_xticklabels(j.get_xticklabels(), rotation=45)
#Function for sorting tf_idf in descending order
from scipy.sparse import coo_matrix
def sort_coo(coo_matrix):
tuples = zip(coo_matrix.col, coo_matrix.data)
return sorted(tuples, key=lambda x: (x[1], x[0]), reverse=True)
def extract_topn_from_vector(feature_names, sorted_items, topn=10):
"""get the feature names and tf-idf score of top n items"""
#use only topn items from vector
sorted_items = sorted_items[:topn]
score_vals = []
feature_vals = []
# word index and corresponding tf-idf score

for idx, score in sorted_items:
#keep track of feature name and its corresponding score
score_vals.append(round(score, 3))
feature_vals.append(feature_names[idx])
#create a tuples of feature,score
#results = zip(feature_vals,score_vals)
results= {}
for idx in range(len(feature_vals)):
results[feature_vals[idx]]=score_vals[idx]
return results
#sort the tf-idf vectors by descending order of scores
sorted_items=sort_coo(tf_idf_vector.tocoo())
#extract only the top n; n here is 10
keywords=extract_topn_from_vector(feature_names,sorted_items,5)
# now print the results
print("\nTitle:")
print(doc)
print("\nKeywords:")
for k in keywords:
print(k,keywords[k])