HumanDigital Sentiment Indication vs Insight

Research Paper First Published: November 2011

Sentiment -‐

Indication vs. Insight

In cooperation with Liam O’Neill, Master’s Graduate at London School of Economics and Political Science

Research Paper – Sentiment: Indication vs. Insight

Human Digital © 2011 | 2

Executive Summary 3

Introduction 5

Why does online sentiment analysis matter? 5

Reliance on Basic Metrics 5

Problems with social media monitoring tools 6

Aims of this paper 7

Methodology 7

The Subject – BlackBerry PlayBook 7

Quantitative Content Analysis 7

Procedure 8

Coding variables – Tonal Sentiment 8

Coding variables – Topics 8

Coding variables – Behavioural Sentiment 9

Findings and Analysis 11

What ‘Positive, Negative, Neutral’ and Topic analysis models tells us 11

Insight through deep sentiment analysis: Adding Value 11

Behavioural Sentiment Variables 14

1. Experience: Direct Experience vs. Indirect Experience 14

Analysis 15

2. Industry Context: Stand Alone vs. Comparative 17

Analysis 18

3. Reasoning: Rational vs. Emotional 19

Analysis 20

4. Composure: Passive vs. Aggressive 21

Analysis 22

Conclusion 23

Bibliography 25



Executive Summary

This paper applies social science methodology to brand sentiment analysis in order

to discover whether additional value can be added to the industry standard metrics

of ‘Positive, Negative, or Neutral’ in analysing sentiment towards brands. The aim of

this paper is to deliver deeper insight from a dataset of online brand sentiment, as

opposed to the indicative offerings of basic ‘Positive, Negative, or Neutral’ metrics.

Using Quantitative Content Analysis and an inductive analysis framework, new

variables were used to analyse consumer behaviour and the drivers of brand

sentiment expressed in a digital environment. The BlackBerry PlayBook tablet was

used as the subject of the study due to its position as one of several emerging

products in the tablet market. This status meant that an appropriate volume of

online commentary that could be collected for analysis.

The basic, top-‐level metrics of ‘Positive, Negative, or Neutral’ were: 48% Positive,

38% Negative, and 14% Neutral. This output is typical of analysis from social media

research agencies and tools. While the metric provides an indication in regards to a

‘temperature check’ on what the audience is saying online, its strategic value is

limited and it provides little direction on behaviour. It is an indication rather than an

insight.

This paper demonstrates how valuable insight can be derived through applying a

social science methodology to a dataset of user-‐generated comments relating to

sentiment.

Implementing the analysis developed for this research, this method allows for:

• Identifying certain drivers of sentiment. In this case, negative sentiment was

based on preconceived views of the product rather than from direct, hands on

experience with the product. In contrast, Positive sentiment towards the

product was more likely to be informed by Direct Experience with the product

through purchase or a hands-‐on trial.



• Insight into the spaces where there are disingenuous and un-‐informed

conversations. For instance 35% of all negative comments in the landscape are

attributed to ‘Non-‐Specific’ comments. Furthermore, 4 out of every 5 of these

are based on no experience with the product.

• Insight into the spaces where rational and informed conversations are taking

place. For instance, although only 12% of the Share of Voice is driven by the

‘User Experience’ of the product, 2 in every 3 comments about it is positive, and

9 out of 10 are based on Direct Experience of the product.

• Insight into to what the people are talking about, how they are expressing

sentiment, and how sentiment differs within topic groups. When compared

side-‐by-‐side, the passionate commentary shows that the audience is

predominantly negative for all topics except UX, where it is overwhelmingly

positive.

Given the mass participation across the social web we believe that if the correct

methodology is applied via a blend of Qualitative and Quantitative metrics,

interested parties are now able to understand target audiences better than ever

before. The above findings are some examples of that insight.



Introduction

Why does online sentiment analysis matter?

An important part of market research analysis and strategy has always been to

discover what a target audience is thinking. Sentiment analysis involves

“computational treatment of opinion, sentiment, and subjectivity in text,” (Pang &

Lee, 2008), and has seen a recent surge in interest due to increased access to

consumer opinion since the advent and rise of social media. As Zabin & Jefferies

point out: “consumers have at their disposal a soapbox of unprecedented reach and

power by which to share their brand experiences and opinions, positive or negative,

regarding any product or service,” (2008). Naturally, companies are seeking to

harness these user-‐generated opinions as they can have an influence on shaping the

opinions of other consumers, and “ultimately their brand loyalties, their purchase

decisions, and their own brand advocacy,” (Ibid).

Much analysis has been applied to the range of opinions expressed through web-‐

based user-‐generated content in attempts to qualitatively and quantitatively assess

audience thoughts and behaviours. However, the capacity to analyse online

sentiment towards brand has in many cases failed to reach its potential, due to the

use of basic metrics and an over-‐reliance on automated data collection tools with

limited capacity for insight and inconsistent outputs.

A research paper from Human Digital in July 2010 found that, when tested side-‐by-‐

side, automated social media monitoring tools offer vastly different results for

volumes of data, platform categorization, and crucially, brand sentiment analysis.

This highlights the difficulty of accurately engaging in sentiment with an algorithm.

Reliance on Basic Metrics

Understanding of social media behaviour and how to interpret the volumes of data

through meaningful metrics is an underdeveloped industry (Schweidel, et al, 2001).

As a result of this, many leading firms “rely on simplified measures such as the total



volume of posted comments or the average sentiment expressed across all posted

comments,” (ibid, p.2).

A common output of sentiment is the percentage attribution of the labels ‘Positive,

Negative, or Neutral’ to a dataset. Dillon, et al (2001) found that these measures of

‘brand ratings’ are problematic because they do not consider that brand sentiment

can be informed by different factors. For instance: “Brand-‐specific associations refer

to features, attributes, or benefits that consumers link to a brand and that

differentiate it from the competition,” while “general brand impressions refer to

general impressions about the brand that are based on a more holistic view of the

brand,” (p.1). Simply testing for ‘Positive, Negative, or Neutral’ sentiment does not

account for any drivers of sentiment, nor does it consider any context surrounding

and influencing sentiment. It therefore offers only a shallow insight into the actual

state of brand sentiment.

Problems with social media monitoring tools

There has been a rise in search-‐based services that monitor online conversations and

automatically analyse online sentiment via keywords (Pang & Lee, 2008). However,

automated tools fail to deliver on their promise to filter and analyse social media

data (Human Digital, 2010). When tested side-‐by-‐side, these automated services

offer vastly differing results for brand sentiment, further highlighting the difficulty of

accurately engaging in sentiment with an algorithm.

The process of automating brand sentiment with search algorithms has several

limitations (Pang & Lee, 2008). These include the fact that sarcasm and irony are

very difficult to detect, difficulties in correctly identifying negative sentiment due to

the complexities of negation terms in sentence structure (see Pang & Lee, 2008, p.36

for a detailed analysis of this fault), and accurately identifying the relationship

between topic and sentiment (ibid). Also of note is that if two different online

reviewers have the same sentiment, they may have different reasons for doing so (as

also identified by Dillon, 2001).



Taking into account these limitations of automated services, there remains a need to

collect data in a more robust fashion and improve on the metrics and analysis

methods that currently exist in the industry.

Aims of this paper

This paper will apply a social science methodology to brand sentiment in an attempt

to offer valuable insight into online brand sentiment. By offering insight over

indication, this paper seeks to provide the kind of results that can potentially impact

the top-‐level strategy of an organisation.

The theoretical research question that this paper seeks to address is ‘Given the rise

of online participation, what can the application of a social science methodology to

the issue of sentiment provide?’.

This study seeks to address this question by applying a Quantitative Content Analysis

methodology to a dataset of User Generated comments on a particular product (the

BlackBerry PlayBook). The sub-‐questions that this research asks are: ‘What are the

factors that influence brand sentiment?’ and ‘How can new variables and analysis

add value to sentiment analysis in a way that can inform communications strategy?’

Methodology

The Subject – BlackBerry PlayBook

The Research-‐In-‐Motion (RIM) Blackberry PlayBook is a 7” tablet launched in June

2011. It is the first attempt of RIM to break into the growing tablet market that is

dominated by Apple’s iPad. This product was deemed appropriate for this study as it

is a Fast Moving Consumer Good that was likely to generate substantial online

discussion from which a dataset could be drawn from.

Quantitative Content Analysis

A Quantitative Content Analysis (QCA) methodology was adopted for this research.

QCA enables research on a large sample of texts, making these texts speak as a

group to answer research questions. It allows for “the objective, systematic, and



quantitative description of the manifest content of communication” (Berelson

1952:18). This makes it an appropriate method for researching online brand and

product sentiment because it can provide a snapshot of opinion within a body of

online textual discussions. It allows researchers to make inferences from texts by

“objectively and systematically identifying specified characteristics of messages.

(Holsti 1969:14).

Procedure

QCA provides “replicable and valid inferences by systematically identifying specified

characteristics of the message’ (Krippendorf 1980:21). It requires the collection of a

sample of texts, which can then be coded against characteristics, and then

quantitatively analysed. To that end, a sample of 500 user conversations on the

BlackBerry PlayBook were drawn from the following online channels: Twitter,

Engadget, BDG, The Guardian online, The Telegraph online, ZDNet, PC Mag, PC Pro,

PC World, and Gizmodo. A sample was drawn from various platforms due to

Schweidel et al’s (2011) observation that different opinions exist across different

platforms.

Coding variables – Tonal Sentiment

The industry-‐standard Positive, Neutral and Negative sentiments were coded to

provide an introductory insight into audience perceptions.

Coding variables – Topics

In order to gain insight into the contexts that surround sentiment, the data was

coded by Topic: the aspect of the product that the sentiment referring to. Topic

groups were inductively derived through an initial read through of the dataset

before any coding was undertaken:

• Non-‐Specific Commentary: These are general opinions that give no reference

to any particular aspect or feature of the product.



• Characteristics: What the product offers (or does not offer) in terms of

features and functionality of the product: applications, email, internet

tethering, calendars, etc.

• Hardware: The physical infrastructure: the screen size and resolution, build

materials, button placement, etc.

• User Experience: Comments relating to the product’s utility, ease of use,

speed in use, or efficiency, etc.

• Price: Retail price of the product.

The reason for selecting an inductive method was so that the dataset could ‘speak

for itself’. Understanding that each brand is different, the idea was to create

variables that were specific to this case, rather than to be locked in to existing or

standard metrics.

Coding variables – Behavioural Sentiment

New coding variables were inductively derived from initial analysis of the dataset in a

similar fashion to the early stages of thematic analysis methods (as outlined in

Bryman, 2001). The dataset was analysed with particular emphasis on patterns,

repetitions and groupings within the texts that could lead to the emergences of

theories surrounding the behaviours of the audience and sentiment. This process

identifies trends within the data, which can then be analysed quantitatively. The aim

was to identify characteristics of comments that help to tell us more about the

attitude and behaviour of the person making the comment. In this way, it was hoped

that findings would offer insight into what is informing, influencing and driving

sentiment.

This process resulted in the following additional coding variables that attempt to

analyse what drives sentiment:

1. Experience (Direct Experience vs. Indirect Experience): A hypothesis was drawn

that those with ‘direct experience’ (having either physically trialled or purchased the

product) would have different opinions than those who had a ‘indirect experience’ –

such as having seen an advertisement, read a review, or seen a video review. While



these variables were derived inductively from the data, this approach is also similar

to Schweidel et al’s (2011) variables of ‘customer’ and ‘non-‐customer’, but this study

also includes those who have trialled the product but not yet purchased it.

2. Industry Context (Stand Alone vs. Comparative): Another insight from the

inductive exercise was the inclusion of the product’s competitors. Some were

discussing the PlayBook only, giving opinion on the product as a stand-‐alone subject.

Other commenters were comparing the PlayBook to its competitors (such as the

Apple iPad or Motorola Xoom). The comments were coded as either ‘Stand Alone’ or

‘Comparative’ in order to test what these characteristics can show about sentiment.

This also aligns with the observations of Dillon et al (2001) who says that brand

sentiment can be influenced by comparisons of products and attributes with

competitors.

3. Reasoning (Rational vs. Emotional): When inductively surveying the dataset, it

emerged that some sentiment had an element of rational depth behind it, such as

weighing up the pros and cons of the product or considering how the product would

fit in with the user’s lifestyle. Other comments were more emotional, knee-‐jerk

reactions that offered opinion with little or no reasoning or rationality.

4. Composure (Passive vs. Aggressive): It should be of little surprise that Internet

discussions get somewhat heated, and this was no different for the sample in this

case. If was felt that the tone of the comments had potential to add value to the

industry standard method, and therefore the sample was coded by these variables in

ordered to give insight into the areas of discussion that generate the most

‘passionate’ sentiment.



Findings and Analysis

What ‘Positive, Negative, Neutral’ and Topic analysis models tells us

The system of coding sentiment as ‘Positive, Negative or Neutral’ provides the

headline figures as shown below:

This offers a general snapshot of the online sentiment towards the BlackBerry

PlayBook – there is a lot of ‘opinionated’ discussion, with most of the audience

having decided whether they are a fan of the product or not -‐ but provides little

insight into behaviour, and therefore has little value in terms of strategy formation.

Insight through deep sentiment analysis: Adding Value

Topic breakdown: Share of Voice

0% 20% 40% 60% 80% 100%

Sentiment Positive - 48%

Neutral - 14%

Negative - 38%



This output indicates that the dominant topic within conversations is ‘Non-‐Specific

Commentary’ – e.g. the audience expressing unfocused sentiment such as ‘I love

this!’ or ‘this looks rubbish’. Therefore, it can be noted that 43% of the conversations

about the Blackberry PlayBook have little value in terms of insight. ‘Characteristics’ is

the next leading topic (27%) suggesting that when people do talk about the product

in details, it is the email, WiFi, apps, etc, that is provoking the interest. The user

experience (UX) of the product commands just 12% of the conversation, which

initially suggests that it is not the most important topic to be communicating. This,

however, is misleading and misses a significant opportunity, which will be explored

later in this paper.

Insights, strategic recommendations and direction are still difficult to extract from

this (admittedly granular) selection of metrics. All we have determined is that most

people commenting are either positive or negative, and when they are not creating

noise (Non-‐Specific Commentary) they are talking about a range of characteristics.

One of the three empirical research questions this paper asks is, ‘how can new

variables and analysis add value to sentiment analysis and inform communications

strategy?’ In other words, ‘by adding additional behaviour metrics to the analysis of

topic and sentiment, can we produce data to better inform strategy?’ The following

sections explore this in further detail.

Positive, Negative, Neutral sentiment against topics



An improvement on the previous metrics, the data here indicates that brand

sentiment performs slightly better within some areas of conversation, but with no

real indication as to why this is the case. While these differences in sentiment and

brand health within topics can be observed, this output still has limited capacity to

inform strategy as it presents only a basic temperature gauge of online sentiment

within these topic groups.

As the above figure shows, viewing the above three topic areas independently of

their Share of Voice shows how sentiment differs depending on what aspect of the

product is being discussed.

From here, the new behavioural sentiment variables can be analysed against specific

segments of the data that combine topic and sentiment. To demonstrate this, the

behavioural sentiment variables will be applied to three segments of data: Non

Specific/Negative, User Experience/Positive, and Characteristics/Negative.



Behavioural Sentiment Variables

1. Experience: Direct Experience vs. Indirect Experience

The data shows that there are large differences between the underlying behaviours

that drive Positive and Negative sentiment within different topics of discussion. The

Negative sentiment associated with Non Specific commentary predominantly comes

from people who have never used the product. In contrast, the vast majority of



those commenting on the User Experience of the PlayBook are influenced by having

trialled or purchased the product. This new behavioural sentiment variable shows

that there are vastly different conditions within positive and negative sentiment.

This clearly offers more insight into sentiment than the standard metrics of “Positive,

Negative, Neutral”.

Examples:

“It's a shame this device is so obviously weak.”

“Another FAIL. Much like all of the other Blackberry junk.”

Non Specific, Negative, Indirect Experience

“The gesture is the best thing about the playbook. Very easy to use.”

“I have one. It's GREAT ! Love to swipe. Hate pushing buttons.”

User Experience, Positive, Direct Experience.

Analysis

This value-‐added methodology allows for analysis of where the fickle and

disingenuous conversations surrounding a product or brand are taking place. This

could be within positive or negative sentiment, within topics, or within channels.

Viewing topic analysis in a silo would point to ‘Non-‐Specific Commentary’ as a key

topic area (due to it’s dominance in Share of Voice), but with further insight provided

by new variables, this topic group is shown to house knee-‐jerk, emotional reactions

to the product based on attitudes towards the brand.

In addition, this methodology allows for identification of the rational and informed

conversations that are influenced by Direct Experience with the product. Topic and

keyword analysis done by automated services and based solely on ‘Positive,

Negative, Neutral’ can be misleading due to the lack of human analysis and insight.

Identifying rational commentators and conversation areas can help achieve a more



targeted strategy. In this case, the high level of Indirect Experience within the

Negative sentiment indicates a high level of disingenuous ‘noise’ that is clouding out

the rational and genuine discussion.

This improved insight can filter out the noise and inform who you should engage

with and why, thus influencing and aligning with the organisation’s communications

strategy. Furthermore when trended over time as an overlay to Schwiedel et al’s

study (2011), this methodology can begin to map whether certain types of

sentiment, driven by specific variables (for example direct or indirect experience

with a product) are typically expressed over certain channels. Imagine the marketing

department at RIM knowing that emotionally displayed negative sentiment driven by

a non direct brand experience was statistically more likely to occur on Twitter,

whereas, rational, informed negative sentiment driven by a direct experience with

the product was statistically more likely to occur within forums.

Understanding whether experience of the product is affecting what the audience is

creating and consuming can have substantial ramifications for brands that transcend

marcomms and start to impact on product release cycles and product development.

If a product is being released into the marketplace without an effective groundswell

of informed opinion, it is more likely that the audience are going to draw their own

conclusions of the product based on pre-‐existing prejudices. Having an appropriate

appreciation of the brand health of your own organisation and the competitors will

help to determine whether an audience, left to their own opinions, will be positive

or negative, which directly affects what the release strategy for the product.



2. Industry Context: Stand Alone vs. Comparative

Within these Sentiment/topic groups, we can see that there are differing results. The

Non Specific/Negative comments are more likely to contain a reference to a

competitor product, meaning that casual offhand commentary will compare the

Playbook to its competitors in a negative light. This is less so within both the User

Experience/Positive group and the Characteristics/Positive group.

Examples

“No thanks, I'll stick with my iPad.”

“This can't be for real. The Playbook is such a colossal fail. There are

other tablets besides the Playbook and the iPad 2.”

Non Specific, Negative, Comparative

“Amazing device, in fact I am using it right now to comment :) RIM

has definitely outdone themselves with this tablet. Everything on it is

easy and fun to use.”

“Think you’re good at multi-‐tasking? Wait till you experience it on the

BlackBerry Playbook!”

User Experience, Positive, Stand Alone



Interestingly, when topic groups are taken out of consideration, there is actually

little variation in sentiment:

Overall sentiment when discussing PlayBook on its own:

Overall sentiment when discussing PlayBook in context of competitors:

This shows the value of analysing the behavioural sentiment variables within the

context of the different topic groups. In many cases, the data becomes more useful

and insightful when cross-‐referenced across several variables.

Analysis

Traditionally, measurement of market position online has been comparative – share

of voice against competitors, sentiment towards competitor brands – without an

appropriate understanding of whether this is actually harming your brand or not.

This aspect of the methodology allows the brand to see whether the presence of

competitors in the conversation is having a positive or negative effect on brand

health. This reveals more ‘hidden’ data within the landscape, and allows the

0% 20% 40% 60% 80% 100%

Positive - 49%

Neutral - 15%

Negative - 36%

0% 20% 40% 60% 80% 100%

Positive - 47%

Neutral - 11%

Negative - 42%



marcomms professionals a new lens on competitive advantage – for instance, if

positive sentiment increases despite the presence of a higher profile competitor, it

would be recommended that marcomms introduce competitive comparison into

activity to increase brand health perception.

3. Reasoning: Rational vs. Emotional

Again, the above graph shows that there are significantly different conditions within

various sentiment groups. The Negative commentary that is Non Specific in nature is

also emotionally expressed and influenced, rather than rational, informed or

considered. This negativity was often influenced by preconceived attitudes towards

the brand or product, which were expressed in absence of consideration of any

potential redeeming factors that this product can offer. In contrast, we can observe

that the positive sentiment within both User Experience and Characteristic topic

groups is expressed rationally. Here, the commentators’ expressions were more

based on how the product could positively enhance their experiences rather than

how the product made them feel.



Examples

“Guy next to me on subway has a PlayBook. Considered tossing him

some spare change so he could buy a real tablet, warm meal.”

Non Specific, Negative, Emotional.

“Quality screen without backlight bleed, decent stereo speakers,

decent amount of RAM, USB and HDMI as well as an OS that that is

more than an app drawer with with basic task switching which is

primarily designed to be a storefront.”

Features, Positive, Rational.

“Wow, I have to say it but the playbook handles orientation changes

on responsive sites better than the iPad. No jerkiness at all.”

User Experience, Positive, Rational.

Analysis

As mentioned earlier, the Non Specific topic group has the highest Share of Voice

within the dataset. The findings within this topic group demonstrate that the

commentators who are producing what might be seen as ‘irrelevant’, ‘throw-‐away’

criticisms of Blackberry based on a negative bias towards the brand are having a

disproportionate effect on the brand health metrics. This is an example of the kind of

insight that this methodology can deliver, as opposed to the metrics of “Positive,

Negative, Neutral”.

People commenting negatively are likely to express opinions based on a

preconceived admiration, aversion or expectation towards the product. In other

words, people are reacting to the product on the basis that the product is made by



Blackberry, rather than reacting upon a consideration of the attributes of the

product itself, particularly so for Negative comments.

Brands such as Apple and Nike have found success in encouraging an emotional

connection to the brand. However, by contrast, companies such as Dell (at the

height of the ‘Dell Hell’ crisis that engulfed the company in 2005 and affected share

price) and Motrin (who were on the receiving end of a consumer backlash to a

advertising campaign in 2007) have been adversely affected by an emotional

response to their brands.

Understanding whether emotion or reason is driving brand health positively can

have a substantial impact on marcomms activities – this is best achieved through

human analysis of content for the same reasons as sentiment analysis detailed on

page three: the unreliability of keyword identification in determining human

behaviour.

4. Composure: Passive vs. Aggressive

While none of the above categories are predominantly aggressive, we can see that

there are differing levels of aggression within the topic groups. The Non

Specific/Negative comments were around five times more likely to be aggressive

than Positive comments on the products Characteristics. On a whole, negative



commentary was twice as likely to be aggressive than positive commentary,

regardless of the topic.

Examples

“You honestly think this is better than the Xoom? I doubt it. The Xoom

is wonderful. RIM is shit.”

“I just can't believe RIM went into the tablet market with this

lackluster tablet!”

Non-‐Specific, Negative, Aggressive

“I have had my Playbook for over three weeks now and already I do

not want to live without it.”

User Experience, Positive, Passive

Analysis

It is important to understand which areas of the business and product are seen as

passive and passionate, to ensure that the tone of marcomms activity is speaking to

the audience needs and interests, and that the brand are not seen as out of touch.

Isolating passionate discussions shows differing results within topic groups, and

analysis of these results emphasises the significance of User Experience within the

data. When compared side-‐by-‐side, the passionate commentary shows that the

audience is predominantly negative for all topics except UX, where it is

overwhelmingly positive.

This is going to be different for every client, depending on competitive landscape,

product release cycle, expectations, reputation, etc. It is not as simple as saying that

passion breeds consumer loyalty, because passion can equally be negative as



positive, and if the audience chooses to target this in the wrong way, it can have a

negative effect on the brand health.

In this case, it is important to counter the audience passion towards the product – all

the data suggests that calm, rational conversation about the products strengths are

being drowned out under the weight of the irrational, passionate sentiment about

perceived weaknesses. However, it is important to promote passionate sentiment

towards the UX as this has a positive effect on brand health.

Conclusion

User Generated sentiment around a brand or product is increasingly becoming a

metric organisations are keen to track and understand. Negative and positive

sentiment is a key brand health indicator, so being able to track trends in this area is

an objective of many organisations.

This new methodology of measuring online sentiment offers insight by identifying,

isolating and analysing the behaviour behind sentiment expression. This results in a

wider and more detailed insight into sentiment. This deep insight can therefore act

as a guide and indicator as to how organisations might wish to address certain issues

across this spectrum. This value-‐added system of sentiment analysis can provide an

insight into strategy formation, whereas automated measures of sentiment acts only

as a temperature check, and does not have the sufficient depth required to inform

tactics or strategy.

The ability to understand whether the audience are creating or consuming content

that is promoted by an informed response to the product is important to note,

especially if it is having a positive or negative effect on brand health. By analysing

Direct Experience and Indirect experience, this method allows for the identification

of where the informed conversations are taking place, and how the brand/product

sentiment is performing in these areas.



Some conversations surrounding the product compare it to its competitors, and

some do not. Identifying and isolating these conversations can offer insight as to

how this relates to sentiment. Strategically, this information can offer insight as to

how marcomms campaigns will consider competitor products.

Reasoning and composure variables can offer insight as to the range of sentiment

tone, and how this differs among conversation topics. This information can offer

valuable strategic insight in terms of responding to, or fitting in with, these tones.

In summary, this paper has added strategic value to the limited, yet often employed,

brand sentiment metrics of ‘Positive, Negative, or Neutral’. By coding data in a

Quantitative Content Analysis methodology and using human analytical methods,

datasets pertaining to products and brands can provide valuable insight into online

brand health and inform communications strategy.



Bibliography

Bryman, A (2001), Social Research Methods

Dillon, William R., Thomas J. Madden, Amna Kirmani and Soument Mukherjee

(2001), “Understanding What’s in a Brand Rating: A Model for Assessing Brand and

Attribute Effects,” Journal of Marketing Research, 38 (4), 415-‐429

Pang & Lee (2008) Opinion Mining and Sentiment Analysis, Foundations and Trends

in Information Retrieval archive Volume 2 Issue 1-‐2, January 2008

Schweidel, David A., Moe, Wendy W. and Boudreaux, Chris, Listening in on Online

Conversations: Measuring Brand Sentiment with Social Media (September 2011).

Available at SSRN: http://ssrn.com/abstract=1874892

J. Zabin and A. Jefferies, “Social media monitoring and analysis: Generating

consumer insights from online conversation,” Aberdeen Group Benchmark Report,

January 2008.

HumanDigital Sentiment Indication vs Insight

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