Opinion mining on newspaper headlines using SVM and NLP

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 9, No. 3, June 2019, pp. 2152∼2163
ISSN: 2088-8708, DOI: 10.11591/ijece.v9i3.pp2152-2163 Ì 2152
Opinion mining on newspaper headlines using SVM and
NLP
Chaudhary Jashubhai Rameshbhai, Joy Paulose
Dept. of Computer Science, Christ University, India
Article Info
Article history:
Received Jan 4, 2018
Revised Jul 23, 2018
Accepted Dec 15, 2018
Keywords:
Newspaper
Sentiment analysis
Opinion mining
NLTK
Stanford coreNLPm
SVM
SGDClassiﬁer
Tf-idf
CountVectorizer
ABSTRACT
Opinion Mining also known as Sentiment Analysis, is a technique or procedure which
uses Natural Language processing (NLP) to classify the outcome from text. There are
various NLP tools available which are used for processing text data. Multiple research
have been done in opinion mining for online blogs, Twitter, Facebook etc. This pa-
per proposes a new opinion mining technique using Support Vector Machine (SVM)
and NLP tools on newspaper headlines. Relative words are generated using Stan-
ford CoreNLP, which is passed to SVM using count vectorizer. On comparing three
models using confusion matrix, results indicate that Tf-idf and Linear SVM provides
better accuracy for smaller dataset. While for larger dataset, SGD and linear SVM
model outperform other models.
Copyright © 2019 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Chaudhary Jashubhai Rameshbhai,
Department of Computer Science,
Christ University Hosur Road, Bangalore, Karnataka, India 560029.
Phone: +91 7405497405
Email: chaudhary.rameshbhai@cs.christuniversity.in
1. INTRODUCTION
Opinion Mining or Sentiment Analysis is a task to analyze opinions or sentiments from textual data.
It is useful in analyzing NLP applications. With the development of applications like network public opinion
analysis, the demand on sentiment analysis and opinion mining is growing. In today's world, utmost people are
using internet and social media platforms, to share their views. These analysis are available in various forms
on the internet, like reviews about product, Facebook post as feedback, Twitter feeds, blogs etc. At present,
news play a dynamic role in developing a person's visions and opinions related to any product, political party
or company. News article published in the newspaper or shared on the web can sometimes create negative or
positive impacts on the society on large scale. As per Dor (1), most of the people judge the news contents
directly by scanning only the news headlines relatively than going through the complete story. Hence, minor
headlines can also impact on large scale. In this paper, Opinion mining is performed based on just the headlines
without going through whole articles. The proposed method begins with data collection and preprocessing.
Data are collected from different news source using Python newspaper package. Review for news headlines
are assigned either +1 or -1 manually based on sentiments. In order to perform SVM to build a classiﬁcation
model, news headline data are fetched and processed in CoreNLP (2). CoreNLP returns set of relative words
which are imported into count vectorizer (3) to generate matrix.
This paper is organized as follows: Section 2 provides overview of related works on opinion mining.
Section 3 contains elaborate explanation of the proposed method. Section 4 discusses the experimental results
of three models. Section 5 concludes and provides future scope of this proposed method.
Journal homepage: http://iaescore.com/journals/index.php/IJECE

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2. RELATED WORK
Agarwal et al. (4) proposed a method containing two algorithms. First algorithm is used for data
pre-processing while other to detect polarity value of a word. Natural Language Processing Tool (NLTK) and
SentiWordNet are employed to build the proposed method. NLTK is a Python library for word tokenization ,
POS (Part of Speech) - Tagging , Lemmatization and Stemming. SentiWordNet (5) lexicon, a method extended
from WordNet is specifically designed for Sentiment Analysis. Each word is assigned with positive or negative
numerical scores. Negative words are denoted with negative score, while positive words are assigned with
positive score. Output of NLTK is fed to SentiWordNet in order to assign numerical score for each word and
compute the final sentiment score, which is sum of all the numerical scores. If the final value is greater or equal
to 0, the headline is classified as positive or negative headline.
Yang et al. (6) proposed a hybrid model for analyzing sentiments of textual data for a single domain.
It is implemented domain wise due to increase in complexity upon segregation. Single classification model is
used to segregate the responses as positive, negative and neutral. The model is a combination of multiple single
classification method which provides more efficient classification.
Rana and Singh (7) compared two machine learning algorithms Linear SVM and Naive Bayes for
Sentiment Analysis. Review on movies are used as dataset containing 1000 samples. Proter Stemmer is em-
ployed to preprocess dataset. While Rapid Miner tool is used to generate model, Linear SVM and Naive Bayes
are used as classifier. Precision, recall and accuracy of both the models are calculated. From the result, it is
observed that the Linear SVM gives better result than Naive Bayes.
Bakshi et al. (8) proposed an approach to classify positive, negative and neutral tweets of Twitter. It
is focused on a single company Samsung Electronics Ltd. Data is fed and processed to clean the data. The
algorithm is applied to analyze the sentiment of tweets and segregate into different categories.
Aroju et al. (9) proposed a method to perform Opinion Mining on three different newspapers related
to similar news. SVM and Naive Bayes are used for opinion mining. Around 105 news headlines are collected
from three different sources (35 headlines from The Hindu, The Times of India and Deccan Chronicle News-
paper). Data is processed using POS - tagger and stemming. Weka tool is used to implement the method. For
experimental results, F-score, Precision and Recall are calculated. From the result it is evident that The Hindu
newspaper contains more positive news than The Times of India and Deccan Chronicle.
Hasan et al. (10) proposed an algorithm which uses Naive Bayes to perform opinion mining. Data
are reviewed in English from e-commerce website Amazon. The reviews are also translated to Bangla using
Google Translator. Opinion mining is calculated for reviews in both Bangla and English. The Bangla dataset
translated from English contains noise. The reviews in Bangla as training data are fed into Naive Bayes to build
classifier excluding noisy words.
Akkineni et al. (11) proposed a method of classifying opinion, opinions are classified based on subject
of opinions and objective of having such opinion, this method helps to classify whether sentence is a fact or
opinion. Approach adopted for classifying in this paper range are heuristic approach where results within a
realistic time-frame. They are likely to produce the results themselves but are mostly used with the optimized
algorithms, Discourse Structure which focuses on the given text that just communicates a message, and linking
it to how that message constructs a social reality or view of the world,key word analysis which classifies text
by affect categories based on the presence of unambiguous affect words such as happy, sad, afraid, and bored,
Concept analysis which concentrates on semantic analysis of text through the use of web ontologies or semantic
networks.The conceptual and affective information associated with natural language opinions are aggregated.
Arora et al. (12) proposed Cross BOMEST, a cross domain sentimental classification. Existing
method BOMEST, it retrieves +ve words from a content, followed by determination of +ve word with as-
sistance of Ms Word Introp. In order to escalate the polarity it replaces all it’s synonym. Moreover, it helps in
blending two different domains and detect self-sufficient words. The proposed method is test and implemented
on Amazon dataset. Total of 1500 product reviews are randomly selected for both +ve and -ve polarity. Out of
which 1000 are used for training and remaining to test the classification model. As a result, when applying on
cross domain precision, accuracy of 92% is achieved. For single domain, precision and recall of BOMEST is
improved by 16% and 7%. Thus, Cross BOMEST improves the precision and accuracy by 5% when compared
to other existing techniques.
Susanti et al. (13) employs Multinomial Na¨ıve Bayes Tree which is combination of Multinomial Na¨ıve
Bayes and Decision Tree. The technique is used in data mining for classification of raw data. Multinomial
Na¨ıve Bayes method is used specifically to address frequency calculation in the text of the sentence or docu-
Opinion mining on newspaper headlines using... (Chaudhary Jashubhai Rameshbhai)

2154 Ì ISSN: 2088-8708
ment. Documents used in this study are comments of Twitter users on the GSM telecommunications provider
in Indonesia.[] This paper used the method to categorize customers sentiment opinion towards telecommunica-
tion providers in Indonesia. Sentiment analysis only consists of positive, negative and neutral class. Decision
Tree is generated with this method and roots in the feature ”aktif”, where probability of feature ”aktif” belongs
in positive class. In result and analysis, it is indicated that the highest accuracy of classification using Multino-
mial Na¨ıve Bayes Tree (MNBTree) method is 16.26% when using 145 features. Furthermore, the Multinomial
Na¨ıve Bayes (MNB) yields the highest accuracy of 73,15% by using all dataset of 1665 features.
In this type of research selection of appropriate feature is one of the challenges. Many researchers
are using decision tree and n-gram approach for feature selection and Supervised Machine learning technique
for model building. The tedious job in this type of research is data preprocessing. Most of the researchers are
using NLP tools for data preprocessing (14), (15), (16), (17), (18). In this paper, n-gram and coreNLP is used
for feature selection and Linear SVM are used for model building.
3. PROPOSED WORK
Many algorithms are available for finding sentiment from text, but they are performed on large text
dataset like movie reviews, product reviews etc. Finding opinions from news headlines is also possible but the
accuracy of existing algorithm is not satisfactory. This paper tries to improve the accuracy of existing algorithm
(4) using different approach. The proposed method is distributed into three processes.
As shown in Figure 1, Process I is regarding data collection and pre-processing. Process II is the
core fragment to build classifier and Process III tests the classification model on test data. These processes are
discussed in detail further.
Data Pre-
processing
Model Building
Model
Evaluating
Figure 1. Process diagram
3.1. Data Pre-processing And Model Building
In this process, data pre-processing and model building are implemented. Figure 2 depicts the basic
flowchart of the data preprocessing and model building. Stop words are removed from news headlines followed
by converting uppercase texts to lowercase. The semi-processed headlines are fed to coreNLP. The output of
coreNLP with sentiment scores are set as input for process II. The input is received from process I and converted
into unigram and bi-gram representation. Model A is generated from the unigram and bi-gram representation.
Model A employs Linear SVM. Data representation is further converted into Tf-idf resulting in Model B and
C. Model B and C employ Linear SVM. However, unlike model B, model C uses SGD classifer to train the
data.
News Headlines
Dataset with
Sentiment Score
Remove All
Stop Words
from Headlines
Convert All Head-
lines to Lowercase
Provide Lowercase
Headlines as
Input to coreNLP
Output : Pre-
processed News
Headlines With
Sentiment Score
Unigram and
Bi-gram Repre-
sentation of Data
Use Tf-idf for
Term Frequency
SVM + SGD SVM
C
B A
Figure 2. Flow diagram of data pre-processing and model building
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Data are collected from the website http://www.indianexpress.com during August 2017. Figure 3
depicts sample unprocessed data. 1472 news headlines are collected and manually classiﬁed as either +1 or
-1. For positive news, sentiment score is +1, while for negative news is -1. Subsequently, after allocating the
sentiment score, all the stop words from headlines such as: is, the, are etc are eliminated. NLTK STOP WORDS
is used to perform the task of removing stop words. The algorithm is case sensitive for which all the headlines
are converted to lowercase. The data is fed to Stanford CoreNLP to generate the dependency parser. Standford
CoreNLP dependency parser (19) checks the grammatical construction of a sentence, which establishes relation
between ”ROOT” word and altering words. To understand how coreNLP works consider news headlines: ”Two
killed in car bomb in Iraq Kirkuk”. Figure 4 depicts parsing of sample data using dependency parser. ”ROOT”
word of the headline is returned and relation between each word of the headline. From the sample data, killed
is returned as ”ROOT” word and relation between all the words. Figure 5 shows parsing of sample headline
using dependency parser and converting the output in the form of string array. String array consists of ”ROOT”
word and relative words. The process is applied on all the data to generate array of strings with ”ROOT” word
and relative words. Figure 6 is statistical representation of frequency of words in data. Figure 7 depicts sample
data with sentiment score. The processed data are input for process II.
Figure 3. Dataset screenshot
Figure 4. Dependency parser working diagram
Figure 5. Generating pandas dataframe using coreNLP

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Figure 6. Most frequently occurred words in dataset
Figure 7. Sample dataset after applying coreNLP
3.2. Unigram and Bi-gram Representation of Data
Before building the model, the raw data is converted from string to numerical values. In machine
learning, Text Analysis is a key application area. Most of the algorithms accept numerical data with ﬁxed
size rather than text data of varying size. A collective approach uses a document-term vector where individual
document is encrypted as a discrete vector that sums occurrences for each word in the vocabulary it contains
(3). For example, consider two one-sentence documents:
D1: ”I like Google Machine Learning course”
D2: ”Machine Learning is awesome”
The vocabulary V = { I, like, Google, Machine, Learning, course, is, awesome } and two documents
can be encoded as v1 and v2. Figure 8 and 9 show the representation of given sentences in unigram and bi-gram
model. The bi-gram model reﬁnes data representation where occurrences are determined by a sequence of two
words rather than individually.
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Figure 8. Unigram representation of given vocabulary
Figure 9. Bi-gram representation of given vocabulary
Figure 10 shows snipped code to generate unigram and bi-gram representation of given data. Data is
an array of 4 news headlines. Here, CountV ectorizer() is used to convert string data into numeric values.
To generate unigram model, pass argument ngram range = (1, 1) and ngram range = (1, 2) for bi-gram.
In ﬁgure, there are two matrices generated using pandas library. In matrix, columns represents unique words
(31 for unigram and 61 for bi-gram) and rows represents 4 news headlines. If the word exists in particular
news headlines the value for that particular feature will be 1 and if the word exists twice then the value for that
feature will be 2. Value depends on frequancy of word in headline. This method is used when a model is built
in Process II.
data = [
’ Coal Burying Goa : What the t o x i c t r a i n l e a v e s in i t s wake ’ ,
’ Coal Burying Goa : Lives touched by coal ’ ,
’ Coal burying Goa : Danger ahead , new coal c o r r i d o r i s coming up ’ ,
’Goa mining : Supreme Court i s s u e s n o t i c e s to Centre , s t a t e government ’
]
c l f = CountVectorizer ( ngram range = ( 1 , 1 ) )
df= c l f . f i t t r a n s f o r m ( data ) . t o a r r a y ( )
pd . DataFrame ( df )
0 1 2 3 . . .
0 0 1 0 0 . . .
1 0 1 1 0 . . .
2 1 1 0 0 . . .
3 0 0 0 1 . . .
4 rows * 31 columns
c l f = CountVectorizer ( ngram range = ( 1 , 2 ) )
df= c l f . f i t t r a n s f o r m ( data ) . t o a r r a y ( )
pd . DataFrame ( df )
0 1 2 3 . . .
0 0 1 0 0 . . .
1 0 1 1 0 . . .
2 1 1 0 0 . . .
3 0 0 0 1 . . .
4 rows * 61 columns
Figure 10. Snipped python code to generate Unigram and Bi-gram from given string data.

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3.3. Model A. Linear SVM
In this process, Linear SVM is used to build the model and the data that has been used to build the
model is Numeric. The Description for total dataset is shown in Table 1. The Matrices in Figure 11 and 12
have been generated by using unigram and bi-gram, column shows the number of words in headlines and row
represents number of headlines. When the value in Matrix is 0, it means that particular word does not exist in
the headlines. In unigram, number of feature depends on total unique words in dataset.
Table 1. Dataset Description
Total Sample Total Feature
Unigram 1472 4497
Bi-gram 1472 13832
Figure 11. Dataset representation in Unigram model.
Figure 12. Dataset representation in Bi-gram model.
Table 1 shows total number of features and samples in unigram and bi-gram. Total sample size is
total number of news headlines and total feature size is number of unique words in dataset. Here, total sample
dataset size is same for both models. But in unigram model total number of feature is 4497 and 13832 for
bi-gram model. For building this model, 80% data are considered for training set and 20% are considered for
evaluating the model. Here, kernel is linear because we have two class labels, so SVM generates linear hyper
plane which will separate words. It separates all negative news headline words and positive news headline
words.
3.4. Model B. Tf-idf and Linear SVM
Linear SVM is used in this model building and the dataset is converted into document frequency using
Tf-idf. Tf is Term-frequency while Tf-idf (3) is Term-frequency time’s inverse document-frequency. It is used
to classify the documents. The main aim of Tf-idf is to calculate the importance of a word in any given headline
with respect to overall occurrence of that word in the dataset. The importance of a word is high if it is frequent
in the headline, but less frequent in overall headline. Tf-idf can calculated as follows (3):
tfidf(t, d) = tf(t, d) ∗ idf(t) (1)
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Where tf(t,d) is term frequency in particular headline, the term occurred number of times in particular headline
and is multiplied with idf(t).
idf(t) = 1 + log
1 + nd
1 + df(d, t)
(2)
Where nd is the total number of headlines, and df(d,t) is the number of headlines that contain term t. The
resulting Tf-idf vectors are then normalized by the Euclidean norm:
vnorm =
v
||v||2
=
v
v2
1 + v2
2 + .. + v2
n
(3)
d a t a c o u n t s = [ [ 3 , 0 , 1] ,
[2 , 0 , 0] ,
[3 , 0 , 0] ,
[4 , 0 , 0] ,
[3 , 2 , 0] ,
[3 , 0 , 2 ] ]
For example, Tf-idf is computed for the first term in the first document in the data counts array as follows:
To calculate Tf-idf of first term in document:
Total No. of Documents : nd,term1 = 6
Total No of Documents which contain this term 1 :
df(d, t)term1 = 6
idf is for term 1 :
idf(d, t)term1 = 1 + log nd
df(d,t) = log 6
6 + 1 = 1
tfidfterm1 = tfterm1 ∗ idfterm1 = 3 ∗ 1 = 3
Similarly for other two terms:
tfidfterm2 = 0 ∗ (6
1 + 1) = 0
tfidfterm3 = 0 ∗ (6
1 + 1) = 2.0986
Represent Tf-idf in vector:
tfidfraw = [3, 0, 2.0986]
After applying Euclidean norm:
[3,0,2.0986]
√
(32+02+2.09862)
= [0.819, 0, 0.573]

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In idf(t), to avoid zero divisions ”smooth idf=True” adds ”1” to the numerator and denominator.After
modification in equation the first two term value will be same but in term3 value changes to 1.8473:
tfidfterm3 = 1 ∗ log 7
3 + 1 = 1.8473
[3,0,1.8473]
√
(32+02+1.84732)
= [0.8515, 0, 0.5243]
S i m i l a r l y by c a l c u l a t i n g every value in d a t a c o u n t s a r r a y the f i n a l
output w i l l be :
T f i d f = TfidfTransformer ( )
X= T f i d f . f i t t r a n s f o r m ( d a t a c o u n t s )
X. t o a r r a y ( )
output of i s :
a r r a y ( [
[ 0.8515 ,0.0000 ,0.5243] ,
[ 1.0000 ,0.0000 ,0.0000] ,
[ 1.0000 ,0.0000 ,0.0000] ,
[ 1.0000 ,0.0000 ,0.0000] ,
[ 0.5542 ,0.8323 ,0.0000] ,
[ 0.6303 ,0.0000 ,0.7763]
] )
The total number of sample data size and feature data size will remain same for building this model.
Here, the frequency of each word is changed according to Tf-idf. Unlike the previous model words with less
frequency in headlines will have lower values. It means words with lesser frequency will have lesser impact on
model. For training, this model is using 80% data and for testing it is using 20% data.
3.5. Model C. Stochastic Gradient Descent (SGD) Classifier
The SGD is used to train the data for Linear SVM. SGD (3) is a discriminative learning of linear
classifiers like SVM and Logistic Regression which is simple and very efficient. SGD has been effectively
implemented in numeric data machine learning problems majorly involved in text categorization and NLP.
Data provided is in sparse, the classifiers in SGD efficiently scales to the problem with more than 105
training
samples and with more than 105
attributes. The major advantage of SGD is that it can handle large dataset.
Here, in this research problem small size of dataset has been used but this approach can be extended this
research for up to 105
features.
4. MODEL EVALUATING
Result of three different models are compared. The confusion matrix has been used as a performance
metric. Table 2 describes the structure of confusion matrix. The formula for accuracy of model is shown in eq.
4. Table 3, 4 and 5 are confusion matrices of Model A, B and C and Table 6 shows the accuracy score of three
models. This table implies that the bi-gram will give more accurate result than unigram. However, in unigram
model, number of feature is less than bi-gram model, due to which time in building the model in unigram is
less than bi-gram. Here, the accuracy of Model B is higher than Model A because it is trained with Tf-idf.
With the increase in feature size (>20000), Model A and B will not provide feasible solutions. To overcome
such issues Model C is introduced in this paper and it is trained using SGD, it supports up to 105
features (3)
for building a model. Thus Model C can be used when the feature size is high, otherwise Model B works well
when the feature size is less.
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News Headlines Dataset without Sentiment Score
BA C
Generate Con-
fusion Matrix
Generate Con-
fusion Matrix
Generate Con-
fusion Matrix
Result of
Model A
Result of
Model B
Result of
Model C
Figure 13. Models evaluation
Table 2. Confusion Matrix
PREDICTED
TRUE FALSE
ACTUAL TRUE TP FN
FALSE FP TN
Accuracy Score =
TP + TN
TP + TN + FP + FN
∗ 100 % (4)
Table 3. Model A (Linear SVM) Confusion Matrix
Unigram Model
PREDICTED
TRUE FALSE
ACTUAL TRUE 223 29
FALSE 9 34
Bi-gram Model
TRUE FALSE
ACTUAL TRUE 228 27
FALSE 4 36
Table 4. Model B (Linear SVM + Tf-idf) Confusion Matrix
Unigram Model
PREDICTED
TRUE FALSE
ACTUAL TRUE 227 22
FALSE 5 41
Bi-gram Model
TRUE FALSE
ACTUAL TRUE 228 21
FALSE 4 42

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Table 5. Model C (SGD) Confusion Matrix
Unigram Model
PREDICTED
TRUE FALSE
ACTUAL TRUE 218 34
FALSE 14 29
Bi-gram Model
TRUE FALSE
ACTUAL TRUE 226 29
FALSE 6 34
Table 6. Accuracy Comparison between Different Models:
Model A (Linear SVM) Model B (Tf-idf + Linear SVM) Model C (SGD)
Unigram 87.11 % 90.84 % 83.72 %
Bi-gram 89.49 % 91.52 % 88.13 %
5. CONCLUSION AND FUTURE SCOPE
Opinion mining is a vast research domain which can be implemented using Neural Networks, NLP etc.
The proposed method is implemented and tested on National/International news headlines of many different
regions. The proposed method outperforms existing model. However, the proposed method can not handle
sarcasm.
As a future work, based on data accessibility, the proposed method can be implemented on social me-
dia data like Twitter, Facebook etc. Also in public sector, the proposed method can be used for the government
to analyze impact of a policy in particular region. The proposed method can be altered to handle sarcasm in
process I and II in order to increase the accuracy of the model.
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