Analyzing users’ sentiment towards popularconsumer industries and brands on Twitter

Guoning Hu, Preeti Bhargava, Saul Fuhrmann, Sarah Ellinger and Nemanja SpasojevicLithium Technologies | Klout, San Francisco, CA

Email: {guoning.hu, preeti.bhargava, saul.fuhrmann, sarah.ellinger, nemanja.spasojevic}@lithium.com

Abstract—Social media serves as a unified platformfor users to express their thoughts on subjects rangingfrom their daily lives to their opinion on consumerbrands and products. These users wield an enormousinfluence in shaping the opinions of other consumersand influence brand perception, brand loyalty andbrand advocacy. In this paper, we analyze the opinionof 19M Twitter users towards 62 popular industries, en-compassing 12,898 enterprise and consumer brands, aswell as associated subject matter topics, via sentimentanalysis of 330M tweets over a period spanning a month.We find that users tend to be most positive towardsmanufacturing and most negative towards service in-dustries. In addition, they tend to be more positive ornegative when interacting with brands than generallyon Twitter. We also find that sentiment towards brandswithin an industry varies greatly and we demonstratethis using two industries as use cases. In addition, wediscover that there is no strong correlation betweentopic sentiments of different industries, demonstratingthat topic sentiments are highly dependent on thecontext of the industry that they are mentioned in.We demonstrate the value of such an analysis in orderto assess the impact of brands on social media. Wehope that this initial study will prove valuable for bothresearchers and companies in understanding users’ per-ception of industries, brands and associated topics andencourage more research in this field.

I. IntroductionSocial media has become one of the major means of

communication and content production. It serves as aunified platform for users to express their thoughts onsubjects ranging from their daily lives to their opinionon companies and products. This, in turn, has made it avaluable resource for mining user opinion for tasks rangingfrom predicting the performance of movies to results ofstock markets and elections.

Although most people are hesitant to answer surveysabout products or services, they express their thoughtsfreely on social media and wield an enormous influence inshaping the opinions of other consumers. These consumervoices can influence brand perception, brand loyalty andbrand advocacy. As a result, it is imperative that largeenterprises pay more attention to mining user opinionrelated to their brands and products from social media.With social media monitoring, they will be able to tapinto consumer insights to improve their product qual-ity, provide better service, drive sales and even identifynew business opportunities. In addition, they can reducecustomer support costs by responding to their customersthrough these social media channels, as 50% of users prefer

reaching service providers on social media rather than acall center [1].

Sentiment analysis has become synonymous with opin-ion mining and involves the computational study of text inorder to identify sentiment polarity (positive, negative orneutral), intensity and subjectivity [2]. It is an excellenttool for enterprises to analyze users’ expressed opinionson social media without explicitly asking any questions asthis approach often reflects their true opinions. Thoughit has drawbacks regarding the population sampled (as itmay not represent the general public), it can be used toapproximate public opinion.

In this paper, we analyze the opinion of Twitter userstowards different industries, encompassing several globalconsumer brands, and associated subject matter topicsvia sentiment analysis of their tweets. Because twittermessages are short, it is quite challenging to identifythe target that an opinion is expressed towards. As apreliminary study, here we take a special approach basedon the observation that when a user interacts with anindustry or brand, such as a reply or a retweet, thesentiment in the related text likely shows this user’sopinion toward that industry or brand. Therefore, byaggregating such interactions from a large number of users,the sentiment distribution can reveal the general attitudeabout the industry or brand among twitter users.

Our dataset consists of 19M unique users, 62 popularindustries (such as Airlines, Automotive, Telecommunica-tions etc.), encompassing 12, 898 enterprise and consumerbrands1 (such as United, Volkswagen etc.), 8030 subjectmatter topics and about 330M user-brand interactionsover a period spanning a month. Specifically, we try toanswer the following questions:

• What is the opinion of users towards different in-dustries? What are the industries that are viewednegatively or positively?

• Within industries, what is the users’ sentiment to-wards different brands?

• What are the frequent topics that users mention whenthey interact with brands over social media? What istheir sentiment towards those topics?

• How does users’ general behavior on social mediadiffer from their behavior when they interact withbrands?

Our main findings are:

1Brand is here defined as an independently recognizableproduct line or business unit.

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User brand interaction SentimentThings I have learned today: we own way too many dishes, Negative@comcast is literally the WORST, and moving is the main cause of drinking problems

Beautiful morning flying out of the Bay with @southwestair. Positive#baytola #sfbay #family #fun #flying https://t.co/bnogVW2X8H@united A measure of the harm inflicted by disrupting people’s travel plans: Negativeno one here felt $800 came close to making them whole.

SUPER excited and grateful to be at the #smashbox event Positive@menloparkmall with @sephora! Thanks for ... https://t.co/CMexZF3pLr

TABLE I: Examples of users’ interaction with a brand (highlighted) and the interaction’s sentiment

• There is a positive correlation between users’ generalsentiment and their sentiments toward brands. Userswho generally express one sentiment class (positive,negative or neutral) on social media tend to expressthat same sentiment toward brands.

• Among industries, the most negative industries tendto be those providing services (such as Airlines, Mailand Shipping, and Telecommunications) whereas themost positive industries tend to be those manufactur-ing and selling consumer goods such as Household ap-pliances. We also find that sentiment towards brandswithin an industry varies greatly and we demonstratethis using two industries as use cases.

• Finally, we find that there is no strong correlation be-tween topic sentiments of different industries, demon-strating that topic sentiments are highly dependenton the context of the industry that the topics arementioned in.

To the best of our knowledge, no other work has at-tempted to analyze sentiment of users towards industries,consumer brands and topics at such a large scale.

II. Related WorkSeveral recent papers have focused on analyzing social

media sentiment for a variety of purposes such as publicopinion mining, stock market prediction and prediction ofelection results. In this paper, we do not intend to cover allwork on sentiment analysis of social media exhaustively.Instead, we focus on discussing a few relevant works.

Balasubramanyan et al. [3] found high correlation be-tween public opinion (in terms of consumer confidence andpolitical opinion), measured from polls, with sentimentmeasured from tweets. Hu et al. [4] analyzed public eventsby automatically characterizing segments and event topicsin terms of the aggregate sentiments they elicited onTwitter. Bollen et al. [5] analyzed tweets to find correlationbetween overall public mood and social, economic andother major events. Si et al. [6] used the co-occurrencesof stock tickers in tweets to propagate sentiment of stocksin order to predict stock values. Mittal and Goel [7] minedpublic mood from Twitter and used it along with previousdays’ stock market values to predict future stock marketmovements. Ceron et al. [8] used sentiment analysis ontweets to understand the policy preferences of Italian andFrench citizens. Tumasjan et al. [9] analyzed the role ofpolitical sentiment and mentions of political parties inTweets as indicators of election results.

In contrast to this rich landscape, very few studieshave focused on investigating users’ sentiment towards

major worldwide brands. Adeborna and Siau [10] proposea sentiment topic recognition model based on correlatedtopics models with variational expectation-maximizationalgorithm using airline data from Twitter. They validatetheir approach on a dataset of 1046 tweets concerningthree major airlines by computing their airline qualityrating. Mostafa [11] used a random sample of 3516 tweetsto evaluate consumers’ sentiment towards 16 global well-known brands.

Our work differs in the scale of the Twitter datasetsize as well as the number of industries and brands weanalyze. In addition, we look at users’ sentiment towardstopics. Finally, we derive some meaningful insights fromthis analysis and demonstrate the value of such an analysisin order to assess the impact of brands on social media.

III. Data SetA. Industries and brands dataset

For our purposes, a brand is defined as an independentlyrecognizable product line or business unit, often with itsown logo, trademark, slogan, etc. Examples of brands inour dataset include Cadbury, AAA, and 23andme. A brandmay be synonymous with a given company or business,but we make no attempt to represent company ownershipor hierarchies (e.g. Fanta is considered a separate brandfrom Coca-Cola, although both brands are owned by thecorporate entity The Coca-Cola Company). Brands in ourdata set can contain one or more Twitter profiles andbelong to one or more industries.

An industry is defined as a high-level category of brandsthat perform similar functions or provide similar products.In this analysis, we use a subset of 62 industries drawnfrom LinkedIn Industry Codes2, the Fortune 5003, theForbes Global 20004, and Hoovers5. See Figure 2 for alist of included industries.

The brand dataset was initially bootstrapped by collect-ing companies’ names and industries from the LinkedInprofiles of Klout6 users. We then attempted to match thecompany names to Twitter handles via the Bing SearchAPI. However, this approach had some limitations:

• LinkedIn included many companies that had shutdown, changed names, etc.

2http://developer.linkedin.com/docs/reference/industry-codes

3http://fortune.com/fortune500/list/4http://www.forbes.com/global2000/5http://www.hoovers.com/industry-analysis/industry-

directory.html6https://klout.com

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Fig. 1: Heatmap of users’ brand and general sentiments

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Cross Industry Content Sentiment (Deep Learning Based)

PositiveNeutral

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Fig. 2: Sentiment distribution by industry, ordered by negative sentiment (best viewed in color)

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Cross Industry Content Sentiment (Deep Learning Based)

PositiveNeutral

Negative

Fig. 3: Sentiment distribution by industry, ordered by positive sentiment (best viewed in color)

• Even with the industry name appended, the BingSearch API often returned non-brand Twitter ac-counts with similar names to the target brands.

• Bing Search API provided only one Twitter handleper brand, whereas many brands have several spe-cialized handles (for customer service vs. corporatecommunications, in different languages, etc.).

Thus, in order to guarantee the quality of the data, wechose to manually curate the brand dataset. Candidatehandles were collected both from the initial bootstrap dataand by mining the Twitter Firehose stream7, looking atsignals such as reply volume and average time to reply(which can indicate a handle staffed by professional socialmedia agents), follower and mention volume, and the use ofenterprise-level publishing and reply tools. Each candidatehandle was reviewed to determine if it belonged to a brand.If yes, it was assigned a new or existing brand node and

7http://support.gnip.com/apis/firehose/overview.html

given one or more industry labels. At this time, the branddata set contains 12,898 brands and 16,137 handles.

B. Twitter datasetFor each brand, we collected interactions (including

replies to tweets, mentions, retweets, and cc) of Twitterusers with the brand for the month of April 2017, viathe Twitter Firehose stream. Each tweet was processedthrough the Lithium NLP pipeline [12] to extract infor-mation such as the tweet language, named entities etc.For each Twitter user in our dataset, we collected all theirEnglish tweets for April 2017.

The complete dataset contains more than 19M uniqueusers that interacted with the 12, 898 brands. Theseusers generated more than 330M user-brand interactions.Among these interactions, there are more than 56Munique tweets in English. Overall, these users postedabout 282M unique tweets in English. Table I shows someexamples of user brand interactions in our dataset alongwith their sentiment.

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Cross Airline Industry Content Sentiment (Deep Learning Based)

PositiveNeutral

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Fig. 4: Sentiment distribution for Airlines, ordered by negative sentiment (best viewed in color)

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Cross Automotive Industry Content Sentiment (Deep Learning Based)

PositiveNeutral

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Fig. 5: Sentiment distribution for Automotive, ordered by positive sentiment (best viewed in color)

C. Topics datasetEach tweet in our dataset is associated with subject

matter topics that are most relevant to the tweet text. Forinstance, the tweet “Anti-net neutrality bots are swarmingthe FCC‘s comments http://engt.co/2q4Kd7l” gets asso-ciated with topics such as Net Neutrality, Federal Commu-nications Commission and Telecommunications etc. Thesetopics belong to the Klout Topic Ontology8 (KTO) [13]- a manually curated ontology built to capture socialmedia users’ interests and expertise scores, in differenttopics, across multiple social networks. As of April 2017, itconsists of roughly 8,030 topic nodes and 13,441 edges en-coding hierarchical relationships among them. On Klout,we use these topics to model users’ interests [14] andexpertise [15] by building their topical profiles. These usertopics are also available via the GNIP PowerTrack API9.The topics are inferred by mapping the recognized

entities in a tweet to KTO topics via the Lithium NLPpipeline [12] which uses a weighted ensemble of severalsemi-supervised learning models that employ entity co-

8http://bit.ly/2fcg3wi9http://support.gnip.com/enrichments/klout.html

occurrences, word embeddings, Freebase hierarchical re-lationships and Wikipedia.

IV. Method

To analyze the sentiment of a tweet and label it asone of the 3 sentiment classes (‘Positive’, ‘Negative’ or‘Neutral’), we used a Deep Learning based approach asthis field has recently enabled strong advances in NLP [16].In particular, we use Recurrent Neural Networks (RNNs)which are a class of neural networks that are able to modelrelationships in time. Unlike traditional neural networks,RNNs use units with internal states that can persist infor-mation about previous events. This makes them suitablefor problems that require sequential information, suchas text processing tasks. We also experimented with alexicon based approach but found deep learning to be moreaccurate given empirical evidence ([17], [18]).

In our implementation, we use a conventional LongShort Term Memory (LSTM) network [17]. We first pre-process the text by tokenizing and normalizing it. We thenfeed this text to the LSTM, and use the labels to performsupervised learning on the messages. We use a vocabularyof 40k words, 128 embeddings units, 32 units, and a

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Fig. 6: Average User-Brand Interactions in Each Industry

Fig. 7: Word cloud of topic sentiments across all industrieswith color representing the sentiment (red is negative,green is positive) and the shade representing its intensity

dropout of 0.5. Our training dataset consists of about 14Mmessage sentiment labels that come from a brand agentmanual assignment dataset [18]. The trained model out-puts a probability distribution over the 3 sentiment classesfor a test sample. To compute a final sentiment score, weuse a simple approximation of taking the difference of thepositive and negative sentiment probabilities.

V. ResultsWe categorized the results obtained from this analysis

into three main categories:

A. Brand sentiment vs general sentimentWe first examined whether users showed different sen-

timents when interacting with brands and otherwise onTwitter. In particular, we calculated two sentiment scores(on a scale of [-1,+1]) for each user in our dataset:

• Brand sentiment: This is the average sentiment ofunique tweets posted by this user during his/her

interactions with brands. Here, we calculated thesentiment score for all brands, as well as scores forindividual industries. Average sentiment is computedby taking the average of the difference of positive andnegative sentiment.

• General sentiment: Average sentiment of all uniquetweets posted by this user (including interactions witha brand).

Figure 1A shows the two-dimensional heatmap of thesetwo scores for all the 19M users of our dataset in the62 industries, where the brighter color indicates higherdensity of users. We also break down this heatmap fordifferent industries. Figures 1B, 1C, 1D, and 1E show thehistograms for Automotive, Airlines, Fashion and Financeindustries respectively. Some important observations illus-trated in these figures are:

• Both the brand and general sentiment scores arecentered around 0.2.

• General sentiment scores are more closely centeredaround 0.2, whereas brand sentiment scores spreadsmore towards 1 (positive) and -1 (negative). Thisshows that users tend to be more positive or negativethan usual when interacting with brands.

• There is positive correlation (as computed by Pear-son’s correlation coefficient), 0.7, between these twoscores for all brands. This shows that users who gen-erally express one sentiment class (positive, negativeor neutral) in their tweets tend to express that samesentiment toward brands.

• The diagonal in each figure corresponds to userswhose brand sentiment score and general sentimentscore are almost the same. This represents a signifi-cant proportion of all users (about 3.5%) who do notpost much other than when interacting with brands.

B. Industry and brand sentimentWe then examined sentiment distributions across dif-

ferent industries and brands. To do so, we first classifiedeach tweet as positive, negative, or neutral based on thehighest sentiment probability computed by our LSTMmodel. We then calculated the percentage of positive,negative, and neutral tweets among all unique tweets foreach industry and brand. Figure 2 shows the percentsentiment distribution of individual industries, sorted by

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Fig. 8: Topic Sentiment across different industries (best viewed in color)

negative sentiment. Figure 3 shows the same data exceptthat industries are sorted by positive sentiment.

Clearly, sentiment distributions vary significantly acrossdifferent industries. Interestingly, the most negative indus-tries tend to be those providing services to customers, suchas Airlines, Mail and Shipping, and Telecommunications,whereas the most positive industries tend to be those man-ufacturing and selling consumer goods, such as Householdappliances. Additionally, the most polarized industries aresurprisingly not Politics and Sports (as one might expect).We also analyzed the sentiment within specific indus-

tries10, taking Airlines (which has the highest negativesentiment) and Automotive (which has a high positivesentiment) as use cases. Figure 4 shows the sentimentdistribution of individual brands in the airline industry,sorted by negative sentiment. Clearly, sentiment distribu-tion varies significantly across different brands. As shown,users are quite negative towards Spirit, Delta, and United,while quite positive towards Air-Mauritius, Sun-Country,and Thomas Cook.

Figure 5 show the sentiment distribution of individualbrands in the Automotive industry. Again, we observesignificant variation across different brands. The mostpositively viewed brands include luxury brands such asAston Martin, Lamborghini and BMW, while the most neg-atively viewed brands include rental car companies (suchas Enterprise and National), Hyundai and Volkswagen.

In addition, we noticed that brands with a higher vol-ume of interactions tend to have more neutral sentiment.As an illustration, Figure 6 shows the average user-brandinteractions across industries. Among the top 10 industrieswith the most interactions, 4 of them are among the top 10industries with the most neutral sentiment, and 8 of themare among either the bottom 10 industries with the mostpositive or negative sentiment. We suspect this is because

10Due to lack of space, it is not possible to show sentimenttowards brands in all the 62 industries

these industries get a lot of interactions and coverage frommedia, whose messages generally tend to be neutral.

C. Topic sentimentWe also analyzed the sentiment towards topics associ-

ated with users’ brand interaction tweets. Here we assumethat when users interact with brands belonging to anindustry, they will mention certain topics in the tweet.By assigning the tweet’s sentiment score to these topics,we can roughly estimate the average sentiment of thosetopic within those industries. Figure 7 shows the wordcloud representing the average sentiment of the 100 mostfrequent topics in users’ interactions across all industries.As shown, highly negative topics include Fox News whilehighly positive topics include Video Games and Music.Figure 8 shows the average sentiment of the 20 most

frequent topics (in users’ interactions across all industries)within each industry, sorted by the average sentiment.It depicts a weak correlation between industry and topicsentiment. But there are many exceptions to this, such astopic CNN in industry Heavy Equipment and topic Russiain industry Entertainment. Moreover, average sentimentstowards these topics vary significantly across differentindustries and are highly dependent on the context of theindustry that the topics are mentioned in. For instance,from Figure 7 we can infer that Fox News is a highlynegative topic and we might expect it to be negative acrossall industries. However, we can see in Figure 8 that thistopic actually has positive sentiment in industries such asEvents, Music and Sports.Figure 9 shows the correlation between industries rep-

resented by their topic sentiment vectors. Each industryis represented as a 100 dimensional vector of the mostfrequent topics (those shown in Figure 7) and the value ofeach dimension represents the average sentiment towardsthat topic within that industry. We centered these sen-timent values around zero in order to mitigate any biasin the data. We then computed cosine similarity between

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Fig. 9: Relationship between Industries represented by topic sentiment vectors (best viewed in color)

the vectors. Overall, there is no strong correlation betweentopic sentiments of different industries, demonstratingthat topic sentiments are highly industry-dependent.

VI. LimitationsWhile we have attempted to cover as much ground as

possible with respect to industries, brands and topics aswell as users’ interactions and sentiment towards them, wenote that this study has some limitations:

• We limit our dataset to Twitter alone since it is oneof the most widely used platforms for self expression.

• Our work mainly focuses on the most popular indus-try and brands.

• Due to resource constraints, we analyzed Twitter datafor only a month.

• The popular topics within industries are often basedon current context and can change with time.

• This study analyzed sentiment only at the messagelevel. Accurate sentiment analysis at the aspect levelwill lead to more insightful results.

VII. ApplicationsAs mentioned earlier, these insights about users’ senti-

ment towards consumer brands can help brands provide

better online customer service, assess the impact of socialmedia marketing campaigns or public relations incidents,identify perceived flaws in their products, and even iden-tify new sales or business opportunities. They also helpbrands gauge the consumers’ perception of them.

Another direct application of analyzing consumers’ sen-timent is to rank a brand relative to its competitors,allowing that brand to contextualize its performance onsocial media. This is easily demonstrated by the SocialImpact Ranking tool11 that Lithium has developed andwhich employs the sentiment of users’ tweets, in additionto several other metrics, to assess the impact of differentcompeting brands in various industries on social media. Asan illustration, Figures 10 and 11 show screenshots of theimpact ranking of airlines on different dates separated by3 weeks. Figure 10 coincides with an unfortunate incidentinvolving a passenger on United Airlines12As evident,United has the highest mentions as well as strong negativesentiment towards it on Twitter. However, the number ofmentions have decreased 3 weeks later (Figure 11).

11https://klout.com/impact-ranking/12http://bit.ly/2wDUR6u

Fig. 10: Airlines ranking retrieved on April 24, 2017 Fig. 11: Airlines ranking retrieved on May 15, 2017

Brands are very interested in understanding customers’attitudes towards them. There have been many demandson quantitative measures of overall attitude of users to-wards a brand as well as their underlying business com-ponents, such as product, website, support and customerservice. Sentiment analysis of brands, topics and theirrelationship plays a key role in building such measures.

VIII. Conclusion and Future WorkIn this paper, we analyzed the opinion of 19M Twitter

users towards 62 popular industries, encompassing 12,898enterprise and consumer brands, as well as associatedsubject matter topics via sentiment analysis of 330Mtweets over a period spanning a month. To the best ofour knowledge, no other work has attempted to analyzesentiment of users towards industries, consumer brandsand topics at such a large scale. We found that userstend to be most positive towards manufacturing and mostnegative towards service industries. In addition, they tendto be more positive or negative when interacting withbrands than generally on Twitter. We also found that sen-timent towards brands within an industry varies greatly.In addition, we discovered that there is no strong cor-relation between topic sentiments of different industries,demonstrating that topic sentiments are highly dependenton the context of the industry that they are mentionedin. We also demonstrated the value of such an analysis inorder to assess the impact of brands on social media usingseveral metrics.

This preliminary study acts as a first step towardsunderstanding the users’ perception of industries, brandsand associated topics. We hope that our analysis andthe insights that we derive from it will prove valuablefor both researchers and companies in understanding userbehavior and encourage more research in this field. Infuture, we will work on employing these datasets resultsfor sentiment forecasting. We also plan to conduct surveysamong Twitter users in order to validate the insights wehave derived from our analysis.

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