Exploring Sentiments of Voters through Social Media Content: A

Case Study of 2017 Assembly Elections of Three States in India

Aman Agarwal and Veena Bansal

Indian Institute of Technology Kanpur, India

Keywords: Sentiment Analysis, Unstructured Data, Campaign Management.

Abstract: Purpose: Winning an election requires more than a good and appealing manifesto. The purpose of this paper

is to establish that content from the social media provides useful insights and can be used to manage an

election campaign provided the right content is properly analyzed. Information such as frequency of mentions,

sentiments of the mentions and demography is obtained analysis. This information provides insights into the

demography of supporters, topics that are most talked about revealing their importance to the voters,

sentiments of voters.

Design/Methodology/Approach: We analyzed 25000 documents from twitter, forums, reviews, Facebook

pages, blogs etc. over a period of 12 months in three states of India using Watson Analytics for Social Media

(WASM) of IBM. We used ETL (extract, transform and load) utility of WASM to fetch the documents for

our chosen themes, topics, dates and sources. WASM deploys deep learning to perform sentiment analysis.

Findings: We found that social media content analysis provides useful insight that goes beyond general

perception and can be used for managing a campaign.

Originality/Value: There have been many efforts where researchers are trying to predict election results

based on social media analysis. However, these efforts have been criticized as predicting election results is a

very complex problem. We, in this work, have shown that social media content can definitely help in gaining

a clear understanding of the sentiments of voters.

1 INTRODUCTION

Sentiment suggests a settled opinion that reflects

one’s feelings. If people express their true opinions

and they are available for analysis, one can analyze

them to gain insight. Sentiment analysis has been

used for classification as well as prediction.

Sentiment analysis requires opinions (data) and

natural language processing techniques. Advent of

big data and deep learning has made sentiment

analysis possible. Sentiment analysis of twitter has

been used to predict election results and number of

seats after the elections (Burnap et al, 2016,

Makazhanov, 2014, Srivastava, 2014). However,

these predictions have been critically evaluated as

predicting election results is an extremely difficult

problem that cannot be solved by analyzing tweets

(Gayo-Avello, 2012). Nevertheless, political parties

are known to use social media to their advantage

during campaign (Ceron et al, 2014, Kapoor &

Dwivedi, 2015, Singh et al, 2017). One can analyze

reviews and classify them as negative, positive and

possibly neutral. In this paper, we analyze web

content including Facebook, twitter, news etc. to gain

insight into the sentiment of voters. These insights

can be used to manage elections campaign (Ahmed et

al, 2016). We review some the relevant work in next

section, research methodology and data analysis are

presented in sections 3 and 4. We conclude our paper

with discussion in section 5.

2 LITERATURE REVIEW

Sentiment analysis is a complicated task. To begin

with, we need to specify the documents that will be

analyzed, their location and the time frame. One

popular source of text/documents has been twitter.

Tweets have been analyzed for finding out the mood

and emotions of public (Bollen et al; 2011). Tweets

over a time period have been extensively analyzed for

predicting election results (O’Connor at el, 2010;

Tumasjan et al, 2010). One can also find out if a party

is being abused on social media. Some of these

596

Agarwal, A. and Bansal, V.

Exploring Sentiments of Voters through Social Media Content: A Case Study of 2017 Assembly Elections of Three States in India.

DOI: 10.5220/0009517105960602

In Proceedings of the 22nd International Conference on Enterprise Information Systems (ICEIS 2020) - Volume 1, pages 596-602

ISBN: 978-989-758-423-7

attempts have been critically evaluated and the

conclusion seems to be that election is a complex

phenomenon and limited social media cannot predict

the results (Gayo-Avello, 2012; Skoric et al, 2012).

For one, not everyone uses social media and not

everything that is said on social media is trustworthy

(Castillo, 2011; Eni and Panagiotis, 2010). It involves

classifying sentiments into positive, negative, neutral

or tension, depression, anger, vigor, fatigue,

confusion or a user defined category. Analyst can

provide a labeled list of words for each sentiment or

can augment the default list of the words if one exists

(Ratkiewicz, 2011). There are many such lists in

existence in public domain that come without any

guarantee of being correct and one easily find errors

in the correspondence of sentiment and the word list

(Goncalves et at, 2013). One may use sentiment

analysis to discover the sentiments of the voters if not

to predict the outcome of the elections. Such an

analysis can be done using a supervised machine

learning technique provided labeled data is available.

Machine-learning techniques complimented with real

time ETL capability (Extract, Transform, Load) can

adapt its classification model according to new data

and based on its performance (Pang and Lee, 2008).

It has been shown that combining methods gives

better performance than a single method. Watson

from IBM provides a sentiment analysis application

using its cloud-based platform including ETL

capability. Watson has deep natural language

processing capability. For example, “I am not only

happy, I am also also thrilled” will be categorized as

a positive sentiment in spite the presence of “not”

because of its deep understanding of natural

language.

With Watson (Pang and Lee, 2008), one doesn’t

have to spend time on programming or downloading

the relevant content. Instead, the focus is on defining

the problem statement, providing the required input,

analyzing results and then repeat all three steps if

required. It is a challenge to provide the required

input as knowing the relevant websites, tweet tags are

not simple. The results may also take some

exploration before one is able to make sense and

comprehend them (Pang and Lee, 2008). Sentiment

analysis falls in general category of big data analytics

as it deals with unstructured data from diverse sources

referred to as variety of the data (one of three Vs:

Volume, Velocity and Variety). Watson provides a

dash board for visualization of data in multiple ways.

In the next section, we discuss our research work in

detail.

3 RESEARCH METHODOLOGY

We had assembly elections in India in five states in

February 2017. We decided to work with three states

namely Uttar Pradesh (UP), Uttarakhand (UK) and

Goa. In India, state elections have been cast based

instead of issue based, especially in UP which is the

largest of three states. However, this time, it was not

clear if cast based campaign will work. Indian

economy has been growing at more than 6% for past

10 years now and literacy rate is over 74%. It is

possible that voters are looking beyond their casts and

leaders of their casts. There was a general perception

about the issues that were considered important and

demography of voters in support of each political

party. We had two objectives:

1. Identify if issues are important and what

issues for each state

2. Identify demography of voters

This information can be used to manage election

campaign. As mentioned in the last section, we have

used IBM Watson Analytics for Social Media

(WASM) available on cloud called Bluemix. We

created an account to get access to WASM which is

an integrated environment for extraction,

transformation, loading, analyzing and visualizing

web and social media content. WASM lets you create

various visualizations for insightful discoveries.

WASM environment requires no coding at all which

is a big-time saver. We also don’t need powerful

machines as WASM is on the cloud.

We created three projects under Social Media

Analytics tab, one for each state. The input that

WASM requires from us consists of topics, themes,

dates, languages and sources. Topics and themes

together form the criteria for selecting a document

from the specified sources if it is in the selected

language and within selected dates. In this study, the

topics are the major political parties in each state. For

example, BJP, Samajwadi Party, INC and BSP are the

topics that we specified for UP which are the names

of major political parties in UP. Likewise, we

specified major political parties for Goa and UK.

Right after specifying the topics, WASM starts

participating by making suggestions as it has huge

knowledge base and deep learning capability. WASM

suggests words related to topics including hashtags,

various other similar words for extracting data from

the Internet.

We have the option of specifying two more fields

namely Context and Exclude keywords which will

enhance extraction of relevant data and reduce

Exploring Sentiments of Voters through Social Media Content: A Case Study of 2017 Assembly Elections of Three States in India

597

extraction of irrelevant data from the specified

Internet sources. The keywords we provide for

Context and Exclude keywords are used to pick

content from the Internet. Therefore, it is important to

pick the right keywords. Natural languages,

especially English, has many polysemous words;

context and exclude keywords help in picking the

intended meaning of word. Themes are the attributes

on which Topics are classified and analyzed. We

selected themes based on our own perception. There

are certain global themes that apply to elections of all

states and there are local themes that apply only to

particular states. Some of the common themes across

all three states are: demonetization, development, beef

and casteism. Local themes for UP were electricity,

minority appeasement, Ram mandir and corruption.

We need to adjust the date to match the time frame of

the event. In this study, we have looked at sentiments

of last one year from the date of first day of election,

from 1-01-2016 to 1-02-2017 as the elections were

held in Feb 2017 to March 2017. Currently, Watson

Analytics allows extracting data in seven languages

namely, Arabic, English, French, German, Italian,

Portuguese, Spanish. There is no Indian languages as

of now.

So, we had to choose only English as the preferred

language for data extraction, which means that we can

only analyze data written in English and we will miss

data written in regional languages. Finally, we

specified the following platforms for extracting the

data: Twitter, Forums, Reviews, Facebook, Videos,

and Blogs. In the next section, we present our results

and learnings.

4 RESULTS AND DISCUSSION

Millions of data/documents were extracted,

transformed and loaded by WASM. We used free

edition of WASM that allowed only the latest 25000

documents to be analyzed in one go. WASM creates

multiple relevant visualization of the results that can

be easily analyzed to gain insights. We can find out

how many times each topic has been mentioned in the

analyzed documents. A snapshot of the mentions per

topic has been shown in Figure 1. It shows that total

16,021 documents were extracted which mention our

topics 23,683 times. Out of those, BJP (topic 1) leads

the Share of Voice (mention per topic) by a good

margin closely followed SP-Congress (topic 2).

Whereas BSP (topic 3) got the least Share of Voice.

Hence, our first finding is as follows.

Finding 1: Campaign managers of BJP can be sure

that their party is being talked about whereas BSP

needs to do something to get attention of voters

A snapshot of share of voice of each theme by each

topic is shown in Figure 2. The major themes that we

looked at for UP elections are beef, Ram mandir,

development, demonetization, electricity, caste,

corruption, law and order and minority appeasement.

Interestingly very few talked about Ram mandir and

beef issue which have been hot topic for UP in the

past. Development is the most talked about theme.

Figure 2 reveals that BJP is associated with

demonetization, development, corruption and caste.

Just to give a little background, BJP has not been

able to win elections in UP ever. This time also, no

one

was expecting BJP to win elections but they got

clear

majority.

When

started

this

work,

also

Figure 1: Share of voice for each topic.

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had the same feelings but the analysis results changed

our perception. From this visualization, political

parties can assess what kind of association they have

with various issues in the eyes of the public. Hence

the following finding.

Finding 2. A political party may find out important

themes and association it has with various themes in

the eyes of the voters.

A natural question at this stage is: were the

mentions positive or negative? Sentiment analysis

answers this question in sufficient detail. Figure 3

shows share of positive, negative, ambivalent and

neutral sentiments for each topic. The results in

tabular form give absolute numbers (refer to Table 1).

Hence the following finding.

Finding 3. A political party may find out the nature

of sentiments associated with its mention.

One can also find out the media where topics are

mentioned, demography of the authors and the

location of these mentions as shown in Figures 4, 5

and 6.

If we look at the Twitter data in the sources tab,

we will find that it is dominated by BJP. This presents

an excellent opportunity for BJP to direct its election

campaign efforts towards Twitter and other similar

social media sites. Demography details like gender,

marital and parental status gives useful insights to the

political parties about the section of society which

supports them and make their campaigning strategy

accordingly. Hence the following findings

Finding 4. One may find out what platform is being

used by voters to express their views.

Finding 5. One may find the demography of voters

expressing their views.

Figure 2: Mentions per theme and per topic.

Figure 3: Sentiments for each topic for various themes.

Exploring Sentiments of Voters through Social Media Content: A Case Study of 2017 Assembly Elections of Three States in India

599

Table 1: Results of sentiment analysis for each topic.

Topic #Number of

Mentions

Positive

Sentiments

(in %)

Ambivalent

Sentiments

(in %)

Neutral

Sentiment

(in %)

Negative

Sentiment

(in %)

BJP 15323 42 9 27 22

SP-Congress 6248 42 10 30 18

BSP 2112 33 8 40 19

Figure 4: Mention of each topic by geography.

Figure 5: Sources of mention.

Findings. One may find out the location of the voters

who are vocal.

Similar analysis for Goa revealed that INC leads

the share of voice by huge margin and the sentiment

by far is positive. The local themes for GOA are Goan

identity and culture and environment. In UK, BJP

leads the share of voice and sentiment is by far

positive. These results are summarized in Table 2.

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Table 2: Results of sentiment analysis for each topic across three states.

State: UP

Winning Party: BJP

State: UK

Winning Party: BJP

State: Goa

Winning Party: INC

Party (Major) % mention % positive %mention % positive %mention % positive

BJP 64 42 99 44 20 41

SP-INC alliance 26 42 - - - -

INC 1 34 69 44

5 CONCLUSION

We have been able to gain useful insight in spite of

the lack of support for popular Indian languages.

Incidentally, BJP got a clear majority in UP and UK

and INC got little less than clear majority in Goa.

Without WASM, this work would not have been

possible. It took us many trials before we got the right

set of topics, themes, context keywords and exclude

keywords. Domain knowledge plays an important

role. With Internet penetration increasing

exponentially in India, more and more people are

getting associated with social media and taking part

in online discussion. The outcomes are going to

improve further in future and various agencies might

integrate this methodology with poll survey to make

predictions. It is no surprise that campaigns are now

managed by using data and data analytics.

ACKNOWLEDGMENTS

We gratefully acknowledge contribution of IBM and

its employees who helped us in getting access to

WASM and provided initial training.

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