1

A comparison of methods of understanding customer thinking arising from social media marketing

Abstract

This paper explores two distinct tools for the evaluation of consumer responses to social

media marketing, the commonly used sentiment analysis and a procedure from the field of

Linguistics known as ‘Metaphor Identification Procedure.’ The research is able to give some

examples of how the two procedures produce different results for the same corpus but notes

that neither method is the ‘perfect’ tool for exploring consumer responses but both can be

used to gain a semblance of understanding of the thinking of consumers. Ultimately, the

paper concludes that there is still much work to be done in the quest for a tool to better

understand consumer’s thinking.

Keywords: Metaphor Identification Procedure, Social Media Marketing, Sentiment Analysis

1. Introduction

1.1 Research premise

With the growth of social media marketing from ‘buzzword’ to strategic tool for marketing

communications1 academics have developed a number of methods to interpret the reactions

of consumers to communications from businesses. The most notable and often used of

these methods is sentiment analysis which was proposed by Pang and Lee.2 Sentiment

analysis can be used to produce quantitative data which interprets whether consumers have

a positive or negative sentiment towards a product or brand as seen in the aforementioned

Pang and Lee, but also other papers such as Agarwal et al3. Cambria et al.4 note that

sentiment analysis can be ineffective when the words a consumer uses can have ‘hidden’

meaning. One of the ways in which language used can have a hidden meaning is when they

contain conceptual metaphors. Conceptual metaphor is defined by Kovecses 5 as ‘consisting

of two conceptual domains, in which one domain is understood in terms of another. A

conceptual domain is any coherent organization of experience.’ Common examples of

conceptual metaphors include the idea that LOVE IS A JOURNEY (as expressed in the

sentence ‘I don’t think our relationship is going anywhere’) and AN ARGUMENT IS WAR (He

shot down my argument) 6

Coulter and Zaltman 7 state understanding the metaphors used by consumers will help to

better explain their thinking and behaviour towards a product or brand. Sentiment analysis is

conducted in a way which merely offers an analysis of the words on a page, it would be

unable to account for the usage of metaphor which Coulter and Zaltman see as being so

2

important. This suggests that there is a need to look at different methods of analysing the

responses of consumers to social media marketing to better understand what consumers

feel about a brand or product.

In looking for methods to interpret metaphor usage a procedure from the field of Linguistics

known as the Metaphor Identification Procedure (designed by the Pragglejaz Group in 2007 8) which can be used for taking naturally occurring examples of discourse (defined by Crystal 9 as when linguistic structures (such as language) combine to produce dialogue in a real

situation (in this case customer comments on social media) and extrapolating and classifying

any metaphors present within it was discovered. A search for papers that used MIP in social

media monitoring was conducted, but none were found. An example of MIP used in the

broader context of the interpretation of advertising can be found in a study by Bullo 10 where

metaphors used by consumers when talking about adverts were analysed in order to explore

what it demonstrated about the customer’s thoughts about a brand or product. This leads in

to the main argument of this research.

1.2 Argument

This research puts forward the argument that by using MIP in the analysis of consumer

responses to social media marketing that the findings arising from this will give greater

insight into how consumers perceive a brand or product when compared to sentiment

analysis.

To that end, this research will be designed to observe the differences in results from the use

of sentiment analysis and MIP. This will be done by using both approaches on an identical

corpus to highlight the differences. This comparison between two separate approaches into

is a unique aspect to this paper, with most contemporary papers focusing on just the use of

sentiment analysis. 11 12 13

With the argument and an initial methodology presented the aims and objectives of the study

can now be discussed.

1.3 Aim

The aim of this research is to demonstrate how when analysing consumer responses to

social media marketing MIP will produce results which will give a greater understanding of a

consumer’s thinking and behaviour towards a brand than sentiment analysis.

In order to meet the aim of this research there are a number of objectives which must be

met. These are:

3

Through a literature review demonstrate current knowledge on the importance of

understanding consumer responses and current sentiment in academia towards

sentiment analysis and MIP. This will highlight gaps which the research will address

and which can also be used to inform the rest of the research.

Contrast the differences in results between carrying out sentiment analysis and MIP

on consumer discourse to demonstrate how the two techniques vary and produce

different ways of understanding the consumers thinking.

To make an overall judgement on the use of MIP in consumer responses to social

media marketing and to make recommendations on the practicality of its usage.

1.4 Outline

This report is structured as follows: the literature reviews draws on understanding the

importance of consumer responses to social media marketing, but also understanding the

views of academia on the methods being used in this research. The methodology will

discuss corpus selection and how the results will be presented. The results of using the two

methods will be presented and the findings will be contrasted. Finally, an overall judgement

on the usefulness of MIP will be made.

With the premise of the paper outlined and the initial argument detailed the first objective of

the research, the conducting of a literature review can begin.

2.Literature Review

This section will focus on reviewing existing literature based around the following three

areas:

The importance of understanding consumer responses to social media marketing

Sentiment analysis

Metaphor Identification Procedure

These will be addressed separately in sections.

2.1 The importance of understanding consumer responses to social media marketing

Whilst the ability for companies to monitor customer feedback has long been heralded as a

key feature of the use of social media marketing 14 15 it has been reported that some

companies often overlook this key feature. Michaelidou et al. 16 found that only 46% of

SME’s in the UK saw this as a reason to use social media, furthermore the report found that

these companies were often looking at the volume of comments made, rather than the

4

content of what consumers were saying. This could potentially lead to problems which other

academics have elaborated on.

One problem which Henning-Thurau et al. 17 point out is that social media has empowered

customers in that they are no longer just recipients of marketing communications (as they

were in traditional mass media campaigns) but are able to influence how marketing

communications are disseminated if they choose to engage with them (through sharing,

liking and commenting etc.) Christodoulides 18 had suggested much similar in an earlier

paper, suggesting that in social media marketing the consumers are in control of the ‘stories’

that are told by brands. These two examples echo the point that Grunig 19 made about social

media being an example of two way symmetrical communication and means that consumers

are very much a part of the communications mix. It can therefore be argued that if

consumers are able to influence communications that it is important to understand what they

are thinking about the product, or their influence may have unexpected consequences.

One of these consequences is that it may be possible for consumers to ‘hijack’ marketing

communications as noted by Edelman and Salsberg. 20 The pair define this as being ‘A

companies campaign is taken hostage by those who oppose it.’ Those who oppose a

campaign can range from dissatisfied consumers to activists. These ‘hijacked’ campaigns

can have serious implications for companies financially as there can be huge costs involved

in mitigating negativity and existing or potential consumers may be put off purchasing if the

hijacking is severe enough.

Henning-Thurau et al. 21 state that due to this possibility of communication ‘hijacking’ there is

a need to ‘moderate’ what responses consumers are giving in order to make sure that a

brand image is preserved as to how a company wishes it to be. Again effective moderation

requires understanding of what a consumer is truly saying and as will be discussed in the

review of sentiment analysis this moderation process can be hampered by either negative

responses not being removed or positive responses being removed if they are

miscategorised by sentiment analysis. Kaplan & Haenlein 22 argue that this moderation

process can be detrimental to social media marketing as it damages a company’s ability to

engage in active and open communications with consumers. Kaplan re-iterates the

importance of listening and understanding to consumers via social media in order to tailor

the communications towards what consumers like.

The consequences of failing to understand what a consumer is trying to communicate are

addressed in Kaplan & Haenlein 23 which uses the case study of Sony attempting to pass off

a corporate blog maintained by an advertising agency as something produced by two fans of

the brand. This lead to consumers responding in a negative manner, but instead of giving

5

consumers the apology they wanted Sony continued to deny the blog was ‘corporate.’

Eventually Sony were forced to acknowledge the blog was a corporate creation and the

brand’s reputation was damaged. Kaplan & Haenlein suggest that if Sony had listened to

what consumers had communicated earlier in the process then (and been honest about the

blogs intentions) Sony may have been able to avoid damage to the brand and pleased

consumers instead of disappointing them.

Overall it can be seen that the importance of listening to consumers is vital when conducting

a social media campaign and that failure to listen and engage with consumers can lead to

hijacking and other ways in which a brand can be damage. This therefore suggests tools and

methods which maximise the understanding of consumers is vital, thus putting more impetus

behind this research.

2.2 Theory on Sentiment analysis

Sentiment analysis is a method by which to review consumer responses on social media

through observation and interpretation of what the consumer is saying. The method was

introduced to monitoring social media by Pang and Lee 24.This initial academic usage

involved using consumer output from Twitter and using software to analyse the language

and any emoticons used to explore how consumers reacted to a film. There are a large

volume of academic papers which have used sentiment analysis in explorations of consumer

responses in social media 25 26 27 28

Sentiment analysis attempts to sort corpus into three distinct categories – positive, neutral

and negative sentiments 29 A feature of social media which academics 30 believe can help to

train sentiment analysis software are emoticons (e.g :D for happy) stating their assumption

that ‘an emoticon within a message represents an emotion for the whole message and all

the words of the message are related to this emotion.’ This logic has been used by other

researchers 31 32 33 34 35 and has led to an improvement in training sentiment analysis software

and has boosted accuracy rates, One study 36 for example saw a near 10% in accuracy

when involving emoticons. There is an argument however that this reliance on emoticons

can be misleading, for example where sarcasm is used which may result in something being

reported as positive when it is in fact negative.37

Pang and Lee 38 state that sentiment analysis can be automated, meaning that it is far less

labour intensive than other forms of analysis. This is seen as an advantage as it gives

marketers feedback on the sentiment of consumers far quicker than previously possible.

There are examples of researchers using semi-automated methods of sentiment analysis 39

where software is instructed by a human operator on how certain words and word patterns

6

should be interpreted. Whilst this is acknowledged as being more labour intensive than

completely automated sentiment analysis it is suggested that it is far more accurate. Pang

and Lee suggest that as machine-learning improves that sentiment analysis will improve and

will become even more important to marketers as time goes on.

However, as Cambria et al. 40 point out the use of sentiment analysis can sometimes

produce results which do not reflect reality. An example ‘small’ may be a good indicator in

certain industries (such as a small wait for a taxi as opposed to a ‘small’ portion in a

restaurant.) This is supported by Abdul-Mageed et al 41 who suggest that oversimplification

of language during this training process can cause these kinds of results. Low reliability has

been a cause of concern for some time with regards to sentiment analysis. Peng and Lee 42

put the accuracy of early sentiment analysis at around 60%, often finding that the nuances of

language caused problems for automated sentiment analysis. Other studies have reported a

higher degree of accuracy; one study reported accuracy at around 68% after engaging in

training of the software. 43

With an understanding of the workings and some of the limitations of sentiment analysis

identified the paper can now move on to look at MIP.

2.3 Theory on Metaphor Identification Procedure

Metaphor identification procedure is defined by Steen et al 44 as being ‘an

explicit, systematic, and reliable tool for finding linguistic expressions that may be related to

metaphor in conceptual structure.’ This procedure (on a very basic level) allows for the

review of corpus by scholars who manually go through text and establish what is meant by

the use of each word and if it is used out of context then it is marked as a metaphorical unit.

Critics of MIP such as Crisp 45 suggest that in MIP ‘claiming that a lexical unit is used

metaphorically means no more and no less than that there is a linguistic basis… What

actually goes on in readers’ minds is another matter.’ What Crisp is alluding to here is that

MIP is not a tool to understand why a metaphor is used, but merely to highlight it. However,

the Pragglejaz Group 46 had perhaps foreseen this criticism in the design of the procedure

noting that it had the ability to be used for researchers in a number of different of disciplines

which have an interest in the use of metaphor.

Metaphor identification procedure has been used in other studies, such as Shutova and

Teufel 47 who used the foundations of MIP but modified the procedure slightly to take into

account the context in the way a metaphor was used. This validates the point made by Crisp

7

that MIP by itself cannot help to explore context, but shows that through adaption of the

initial procedure that it can be used in a cross disciplinary manner.

A problem with the Metaphor identification procedure was identified by one of its principle

authors 48 in that MIP cannot distinguish between direct and indirect metaphor usage. Steen

defines direct metaphors as ‘language is used directly to set up a conceptual domain which

briefly functions as a local topic in its own right. This alien topic is then exploited to carry out

a metaphorical comparison with the more encompassing topic.’ He gives an example of a

sentence of system developers being seen as doctors. (An indirect metaphor is where a

metaphor hints at what is being compared but doesn’t say it directly.) Steen suggests that

this can create problems as direct metaphors are used to change perspectives and tend to

be a more deliberate means of communication, whereas indirect metaphors tend to be used

non-deliberately by speakers. This means that the vast majority of metaphors used tend to

be indirect as Steen found in a review of English and Dutch corpuses. It could be argued that

in this research this inability to distinguish between metaphor types may be an issue as the

metaphors used by a consumer may not accurately portray their opinions about a brand or

product.

Overall, it can be seen that MIP is a highly adaptable procedure which can be used to

understand the use of metaphor. As Steen states however MIP is not without its flaws and

must be used with care.

With the literature review completed there are a number of key questions which have arisen

which can be used to guide the research in terms of areas to look at. This will be addressed

at the start of the next chapter, which will outline the methodology of the research.

3 Methodology

As discussed in the introduction to this paper the research will carry out a review of

consumer responses on social media to a company’s marketing communications looking to

understand a ‘hidden’ feature of language in metaphors. This review will be carried out using

two separate methods, the first being a sentiment analysis and then an adaption of the

metaphor identification procedure. The decisions that will be made (such as corpus

selection, adaptions to MIP and how the results of the reviews will be reported) will be

discussed in this chapter.

8

The chapter will begin by addressing the research questions that have arisen after the

literature review in the previous section.

3.1 Research questions arising from Literature Review

The following questions could potentially be asked as part of this study and have been

identified as part of the literature review:

Has sentiment analysis software become more effective in assessing consumer

responses on social media since Peng and Lee initially used it and is it now

comparable in accuracy to manual methods such as MIP?

How can the metaphor identification procedure be adapted (as done by Shutova and

Teufel) to provide a better understanding of consumer responses to social media

marketing?

Is there evidence of indirect metaphors being used more frequently by consumers as

Steen suggests? Can it be argued that these do not really show what a consumer is

thinking, going against the suggestions of Coulter and Zaltman.

With these questions laid out the research can be designed in a way in which these

questions can be answered and the overall aim of the study can be met.

3.2 Research Design

Whilst the two research tools that will be used in this research have been decided upon

since its inception there are still a number of points of the design of the research which

require thought and clarification. The design point which needs to be addressed first in this

research is where the corpus which will be analysed will come from.

When looking at social media marketing in a study it is important to select one specific

platform to investigate as each platform behaves in slightly different ways. This is especially

important in looking at how consumers are communicating as different platforms will force

consumers to communicate in different ways (for example Twitter’s 140 character limit will

force consumers to be more concise and use more emoticons.) The platform that was

decided upon for use in this research was Twitter. This is because several other studies

have used Twitter 49 50 51 thus making comparisons with other studies much easier. It may

also assist in that people may be more likely to use a metaphor as they have a limited

amount of characters and therefore may use metaphor to describe their feelings towards a

product or brand in a more concise manner.

9

The next issue which must be decided upon is where the corpus which will be analysed will

be found. It was decided to use response to one of the most retweeted company

communications from 2014. This tweet was from Burger King announcing the re-launch of

their chicken fries. The tweet can be seen below:

INSERT IMAGE HERE

Figure One: Company communication

Initially there were 153 responses from consumers to this tweet. For the purposes of this

research responses which were made in languages other than English were omitted.

Furthermore tweets which just contained a single emoticon (e.g. :O or :D) were excluded as

it was felt that these results would not give any real insight into consumer thinking. With

these responses excluded there will be 117 responses which will be analysed as part of this

research. The next part of this methodology will explain how the two tools will be utilised in

the analysis of the responses and will first discuss sentiment analysis.

3.3 Sentiment Analysis

The first part of the research that will take place is the sentiment analysis. A commercially

available sentiment analysis service (Textalytics) will be used to assess the sentiment in

each of the tweets which were made in response to the Burger King tweet. This software

was used as it gives an indication of what words it considers negative, which may be useful

to understand why the software has interpreted tweets has having a certain sentiment.

In order to make sure the software correctly interprets the words that consumers use the

responses will be checked for non-standard forms and replaced with the standard form of

the word (e.g. YEESSSS! will be replaced with YES). This replacement of non-standard

forms was previously done by Thelwall et al 52 with the justification that the sentiment of the

tweet is important, not the way a word is spelled.

No training of the software will be made. The results from the sentiment analysis will then

show the percentages of each sentiment the software has found (e.g. 50% Positive, 30%

Neutral 20% Negative.) After conducting the sentiment analysis the next step will be to carry

out the metaphor identification procedure.

3.4 Metaphor identification procedure

The next step of the research will entail the use of the metaphor identification procedure in

order to determine any metaphorical basis behind the words that consumers have chosen to

10

use in their responses. The Pragglejaz Group 53 proscribed the following method in carrying

out MIP:

1. Read the entire text–discourse to establish a general understanding of themeaning.2. Determine the lexical units in the text–discourse3. (a) For each lexical unit in the text, establish its meaning in context, that is,how it applies to an entity, relation, or attribute in the situation evokedby the text (contextual meaning). Take into account what comes beforeand after the lexical unit.(b) For each lexical unit, determine if it has a more basic contemporarymeaning in other contexts than the one in the given context. For ourpurposes, basic meanings tend to be—More concrete [what they evoke is easier to imagine, see, hear, feel,smell, and taste];—Related to bodily action;—More precise (as opposed to vague);—Historically older;Basic meanings are not necessarily the most frequent meanings of thelexical unit.(c) If the lexical unit has a more basic current–contemporary meaning inother contexts than the given context, decide whether the contextualmeaning contrasts with the basic meaning but can be understood incomparison with it.4. If yes, mark the lexical unit as metaphorical.

This research will make an amendment to the method laid out. This will consist of an

addition to step one in that the sentiment of the consumers tweets will be assessed manually

so that the validity of the sentiment analysis can be analysed.

After the validity of the sentiment analysis has been assessed the next step of the MIP will

entail careful analysis of each word used by the consumer. This encompasses steps 3 & 4.

The dictionary used for establishing the meaning of words will be the Oxford English

Dictionary. The decision to use this dictionary was due to its reputation as a reflection of the

present state of English language.

In terms of the report of results, the initial sentiment analysis findings will be presented.

These will then be followed by a section on the perceived accuracy of the sentiment analysis

after the first section of the MIP; this will then be followed by a discussion of the metaphors

discovered.

With the methodology clearly presented the findings of the research can now be presented.

11

4 Findings

This section will focus on the findings of the research following the methods which were

proscribed in the previous section. The section will begin with the discussion of results

from carrying out sentiment analysis.

4.1 Results from Sentiment Analysis

The first step of the research was to carry out the sentiment analysis of the tweets made

by consumers. The tweets were input individually into the sentiment analysis software

and the sentiment was recorded. It was found that of the 117 responses that 22.22% of

responses were negative, 43.58% of responses were neutral and 34.18% of responses

were positive. From these results it can be suggested that reaction of consumers to re-

introduction of Chicken Fries by Burger King was fairly neutral. The next step would be to

manually mark the sentiment of each tweet to judge the accuracy of the sentiment

analysis in a modification of the MIP method.

The manual analysis found that negative responses accounted for just 7.69% of the total

corpus (including a number that had been misidentified as neutral responses by the

software) neutral responses accounted for 5.12% and positive responses accounted 87.19%

of the responses made. This demonstrates that by performing MIP there is already a greater

understanding of how consumers are responding to tweets, as performing just sentiment

analysis would have given the impression of general indifference on the whole, yet through

MIP an overwhelmingly positive response can be observed.

The accuracy for the sentiment analysis was worked out at 42.01% far lower than the

accuracy reported by Peng & Lee 54 and Kouloumpis et al. 55 although it can be argued that

this demonstrates the need for training of sentiment analysis software to truly understand a

corpus. Through information supplied through the sentiment analysis software and the use of

MIP a number of reasons why the accuracy of the software was so low can be explained.

In this study it was found sentiment analysis software misinterpreted what was written by

consumers involved the use of obscene language, which often resulted the sentiment

analysis categorizing positive responses as negative. This can be seen in responses such

12

as ‘FUCK YES’ which is a clear positive response but is registered as negative. This is not a

consistent phenomenon however; ‘NO FUCKING WAY MY LIFE IS COMPLETE!!’ was

registered (correctly) as a positive response, suggesting that the sentiment analysis software

is somewhat unpredictable. A further example of unpredictable behaviour from the sentiment

analysis software is the use of the word ‘chicken’ in responses which sees responses such

as ‘CHICKEN FRIES ARE BACK’ registered as negative as the software uses the informal

definition (meaning cowardly) rather than the noun relating to the animal. Furthermore

examples where people talking about ‘crying’ because of the news is categorised as

negative, despite the fact that consumers are implying they are overcome with happiness.

These examples correlate with the findings of Abdul-Mageed et al 56 that over-simplification

of language can lead to sentiment analysis being ineffective.

The findings of this part of the research suggest that sentiment analysis is not really an

effective tool in understanding what consumers are communicating in their responses to

social media marketing. As shown in this example Burger King may have conducted

sentiment analysis and come to the conclusion that they needed to put more resources into

promoting chicken fries to make consumers view the product in a more positive light, but as

shown this was not the case. This suggests that sentiment analysis is not an effective way of

understanding consumer responses. With the findings from the sentiment analysis discussed

the paper can now move on to present the test of the findings from using the MIP.

4.2. Results from Metaphor Identification Procedure

The second step of the research involves the use of the metaphor identification procedure to

further explore the responses given by consumers to the Burger King communication. As

already demonstrated the metaphor identification can be used to establish the context and

tone of consumer responses in a far more accurate (yet time consuming) manner when

compared to sentiment analysis. However the purpose of using MIP in the research to

establish what can be learned from consumer’s responses through its usage and to do this

the metaphors that were found must be examined.

Of the 117 responses 18.80% were found to contain metaphor usage within them and are

shown below.

Responses Metaphor Identified

IM ALIVE (ALIVE) - living is happinesslet's bring back the old chicken tender recipe

next plEAsE (back) time as space THEY'RE BACK! :D (back) time as space

I'm actually really excited that Chicken Fries are (back)time as space

13

back at @BurgerKing! They better be the same or better.

They back? (back)time as spaceCHICKEN FRIES ARE BACK (back)time as space

OH MY GOD MY CHILDHOOD IS BACK (back)time as spaceYES THEY ARE BACK YES :D (back)time as space

they bringing back childhood memories, next thing you know McDonald's bringing back fajitas!

:D (back)time as spacejust had them bomb :D (bomb) bomb is big - big is good

YES YES YES YES MY CHILDHOOD IS HAPPY (childhood) Childhood is Alter Ego<3 you know my heart (heart) heart is a container of emotions

IM IN HEAVEN :D (Heaven) HAPPINESS IS HEAVENHYPE (Hype) Hype is excitement

I'm too hyped right now... (Hype) Hype is excitementTHESE ARE MY LIFE YES (LIFE) - Happiness is livingLife has meaning again (life) life is a story

hahaha everyone has told me about this ;) my love for them is real (love) love is desire

OMG you’re the real MVP (MVP) BURGER KING IS A STANDOUT ATHLETEIm stoked to be honest as fuck right now (stoked)EXCITEMENT IS HEAT

This somewhat low number of metaphors can be explained by the large amount of one word

responses (such as ‘YES!’) that were generated in response to the tweet. This suggests that

MIP may be unsuitable for the social media arena as nothing can be gleaned from these

types of responses that automated sentiment analysis would not be able to tell us (that this

is a positive response.) When looking at the metaphors that were identified by using the

process it was discovered that the majority of metaphors used by consumers in their

responses were indirect metaphors (confirming work by Steen 57 suggesting that indirect

metaphors are more prevalent in naturally occurring corpuses.) This can be explained by

suggesting that in responses to a company, customers are not really trying to influence the

company, so are less likely to use a direct metaphor.

In analysing the metaphors used by consumers in this corpus there are further examples

which correlate with Steen’s findings, namely that metaphors are used often used non-

deliberately. This is shown in examples such as ‘CHICKEN FRIES ARE BACK’ and ‘YES

THEY ARE BACK YES :D’ where the use of ‘back’ alludes to the metaphor TIME IS

SPACE. Whilst without directly talking to the consumers who wrote these tweets we are

unable ascertain if these metaphors were meant deliberately it can be hypothesised that

they were not as they are not related to either the product or the company themselves. It

could be argued that these indirect metaphors help to explore how consumers feel about a

14

product. An example of this can be found in one response to the stimulus tweet ‘Im stoked to

be honest as fuck right now’ in this response ‘stoked’ is used metaphorically and expresses

the thought that EXCITEMENT IS HEAT and helps to convey the excitement that the

consumer has for the product. Furthermore, there was an example of a direct metaphor

being used ‘OMG you’re the real MVP’ maps the idea of a standout athlete onto Burger King.

This metaphor shows that in that consumers mind that Burger King stand out over other

companies.

Overall the use of the metaphor identification procedure in this instance yielded little more

insight than a cursory glance at the responses would have garnered. When the amount of

work that is required to conduct the MIP is considered it can be suggested that in social

media it is a heavily labour intensive task which may not yield any insights which would be

usable in a company’s effort to market its products. This suggests that Coulter and Zaltman’s

suggestion 58 that understanding metaphor is crucial to understanding a consumer’s view

about a product or brand is incorrect as metaphors do not always produce usable data.

5 Conclusion

This paper put forward the argument that the use of the Metaphor Identification Procedure

would produce results which would give a far greater insight into consumer’s thinking and

behaviour towards a brand when compared to sentiment analysis. Arguably, this has not

been the case.

Whilst this research has shown that whilst the Metaphor Identification Procedure (with slight

modifications) is an excellent tool for uncovering the sentiment present in the tweets made

by consumer’s and is helpful in exploring unexpected results reported by sentiment analysis

it has also shown that the procedure is unsuited to the social media environment. This is

because consumers tend to use short messages on social media in response to

communications from companies as demonstrated in the responses collected. This means

that consumers are less likely to use metaphors and as demonstrated in this paper, when

they do these metaphors tend to be indirect and not reveal any real beliefs that the

consumer has toward a brand or product.

Implications for Business

The judgement of this paper is that metaphor identification procedure would not be a tool

which could be recommended to monitor social media traffic for a company. Furthermore the

paper has shown that sentiment analysis is a similarly unreliable tool for monitoring social

15

media and that further work is needed to find a tool which can adequately explore

consumer’s sentiments on this channel.

Limitations

There are a number of limitations of this study which must be taken into consideration when

judging the relevance of this paper. These include:

The Metaphor Identification Procedure calls for more than one author to carry out the

procedure in order to make sure that metaphors are correctly identified and not just

based on the interpretation of a sole author. In this paper a sole author carried out

the MIP, meaning that it was not verified and thus is highly interpretative.

Corpus size – This paper carried out tests of MIP and sentiment analysis on just one

set of tweets related to a single tweet sent by a business. Whilst many papers on

sentiment analysis use a similar corpus size it could be argued that it is unfair to

judge MIP on such a small sample.

Limited amount of other work – There have been few studies which look at the use of

MIP to understand the attitudes of consumers. This makes comparisons between

studies near impossible and means that it is very difficult to triangulate claims made

in the paper.

16

References

1. Eyrich N, Padman M, Sweetser K. 2008. PR practitioners’ use of social media tools and communication technology. Public Relations Review 2008; 34 (2008) 412–414

2. Pang B, Lee L. Opinion mining and sentiment analysis. Foundations and trends in information retrieval, 2008; 2(1-2), 1-135.

3. Agarwal A, Xie B, Vovsha I, Rambow O, Passonneau R., Sentiment analysis of twitter data. In Association for Computational Linguistics. Proceedings of the Workshop on Languages in Social Media 2011 30-38

4. Cambria E, Rajagopal D, Olsher D, Das, D. Big social data analysis. In: Akerkar R (ed) Big Data Computing. London: Chapman and Hall; 2013. 401-414.

5. Kovecses Z. Metaphor : a practical introduction. Oxford: Oxford University Press 2010

6. Lakoff G, Johnson M. Metaphors We Live By. Chicago: University of Chicago Press. 1980

7. Coulter R, Zaltman G. Using the Zaltman Metaphor Elicitation Technique to Understand Brand Images. Allen C, John, R. (eds) NA - Advances in Consumer Research Provo, UT : Association for Consumer Research; 501-507 1994

8. Group P. MIP: A method for identifying metaphorically used words in discourse. Metaphor and symbol, 2007 22(1), 1-39.

9. Crystal D. David Crystal’s Introduction to Language. London:Routledge 201110. Bullo S. Evaluation in Advertising Reception: A Socio-Cognitive and Linguistic

Perspective. London: Palgrave MacMillian 2014.11. Rosenthal S, Nakov P, Ritter, A, Stoyanov, V. 2014. Semeval-2014 task 9: Sentiment

analysis in twitter. In: Moro, A. Proc. SemEval. Prague12. Agarwal A, Xie B, Vovsha I, Rambow O, Passonneau R., ref 3.13. Kouloumpis E, Wilson, T, Moore, J. Twitter sentiment analysis: The good the bad

and the omg!. ICWSM, 11, 538-541. 201114. Kaplan, A. Haenlein, M.. ‘Users of the world, unite! The challenges and opportunities

of Social Media’ Business Horizons 2010 5 (3) pp.59-6815. Eyrich N, Padman M, Sweetser K. ref 1. above16. Michaelidou N, Siamagka N, Christodoulides G. Usage, barriers and measurements

of social media marketing: An exploratory investigation of small and medium B2B brands. Industrial Marketing Management 2011 40 1153-1159

17. Hennig-Thurau T, Hofacker C, Bloching B. Marketing the Pinball Way: Understanding How Social Media Change the Generation of Value for Consumers and Companies, Journal of Interactive Marketing, 2013 27(4) 237-241

18. Christodoulides, G. Branding in the post-internet era, Marketing Theory, 2009 9 (1) 141-144.

19. Grunig J. Paradigms of global public relations in an age of digitalisation. Prism 2009 6(2)

20. Edelman D. Salsberg B. Beyond Paid Media: Marketing’s new Vocabulary. http://www.mckinsey.com/insights/marketing_sales/beyond_paid_media_marketings_new_vocabulary (Accessed 8th August 2014)

21. Hennig-Thurau T, Hofacker C, Bloching B ref 17 above22. Kaplan A, Haenlein M. Users of the world, unite! The challenges and opportunities of

Social Media. Business Horizons 2010 53, 59—6823. Kaplan A, Haenlein M ibid24. Pang B, Lee L. ref 2. Above25. Liu B. Sentiment analysis and opinion mining. Synthesis Lectures on Human

Language Technologies, 2012 5(1), 1-167.26. Rosenthal S, Nakov P, Ritter, A, Stoyanov, V. ref 11. above27. Agarwal A, Xie B, Vovsha I, Rambow O, Passonneau R ref 3. Above28. Kouloumpis E, Wilson, T, Moore, J. ref 13. Above29. Pang B, Lee L. ref 2. Above

17

30. Pak A, Paroubek A. 2010. Twitter as a Corpus for Sentiment Analysis and Opinion Mining." LREC 2010.

31. Liu, B ref 25. Above32. Rosenthal S, Nakov P, Ritter, A, Stoyanov, V. ref 11. above33. Pang B, Lee L. ref 2. Above34. Agarwal A, Xie B, Vovsha I, Rambow O, Passonneau R ref 3. Above35. Kouloumpis E, Wilson, T, Moore, J. ref 13. Above36. Kouloumpis E, Wilson, T, Moore, J. ref 13. Above37. Rosenthal S, Nakov P, Ritter, A, Stoyanov, V. ref 11. Above38. Pang B, Lee L. ref 2. Above39. Thelwall M., Buckley K. Paltoglou, G. Sentiment strength detection for the social

web. Journal of the American Society for Information Science and Technology, . 2012 63(1), 163-173

40. Cambria E, Rajagopal D, Olsher D, Das, D. ref 4. Above41. Abdul-Mageed M, Kübler S, Diab.M. Samar: A system for subjectivity and sentiment

analysis of arabic social media. Computer Speech & Language. 2014 28 (1) 20-3742. Pang B, Lee L. ref 2. Above43. Kouloumpis E, Wilson, T, Moore, J. ref 13. Above44. Steen G., Aletta G. Dorst, J. Berenike H. Kaal A, Krennmayr T. Metaphor in

usage. Cognitive Linguistics. 2010 21(4): 765-79645. Crisp, P. Between Extended Metaphor and Allegory. Language and Literature. 2008

17 (4) p291-30846. Group P. ref 8 above47. Shutova E, Teufel, S. Metaphor Corpus Annotated for Source-Target Domain

Mappings. In LREC 2010.48. Steen, G.J. When is metaphor deliberate? In N.-L. Johannesson, C. Alm-Arvius, &

D.C. Minugh (Eds.), Selected papers from the Stockholm 2008 Metaphor Festival (Stockholm Studies in English), (pp. 43-63). Stockholm: University of Stockholm.

49. Pang B, Lee L. ref 2. Above

50. Kouloumpis E, Wilson, T, Moore, J. ref 13. Above51. Agarwal A, Xie B, Vovsha I, Rambow O, Passonneau R ref 3. Above52. Thelwall M., Buckley K. Paltoglou, G. ref 39. Above53. Group P. ref 8 above54. Pang B, Lee L. ref 2. Above55. Kouloumpis E, Wilson, T, Moore, J. ref 13. Above56. Abdul-Mageed M, Kübler S, Diab.M ref 41 above57. Steen, G.J. ref 48.above58. Coulter R, Zaltman G. ref 7. Above