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Analysing Social Media Marketing on Twitter Using Sentiment Analysis EXAMENSARBETE INOM TEKNIK, GRUNDNIVÅ, 15 HP STOCKHOLM, SVERIGE 2018 Analysing Social Media Marketing on Twitter using Sentiment Analysis MAX MATTILA HASSAN SALMAN KTH SKOLAN FÖR ELEKTROTEKNIK OCH DATAVETENSKAP Analysing Social Media Marketing on Twitter using Sentiment Analysis MAX MATTILA HASSAN SALMAN Degree Programme in Computer Science and Engineering Date: June 6, 2018 Supervisor: Richard Glassey Examiner: Örjan Ekeberg Swedish title: Attitydanalys av marknadsföring på Twitter School of Electrical Engineering and Computer Science iii Abstract Social media is an increasingly important marketing platform in to- day’s society, and many businesses use them in one way or another in their advertising. This report aimed to determine the effect of dif- ferent factors on the sentiment in the response to a tweet posted on Twitter for advertising purposes by companies in the fast food sector in North America. The factors considered were the time of posting, the length and the sentiment of a tweet, along with the presence of media other than text in the tweet. Sentiment was extracted from samples of the response to the advertising tweets collected daily between the 27th of March and the 28th of April and plotted against the factors men- tioned. The results indicate that the sentiment of the advertising tweet along with the time of posting had the biggest impact on the response, though no definitive conclusions on their effects could be drawn. iv Sammanfattning Sociala medier är en allt viktigare marknadsföringsplattform i dagens samhälle, och många företag använder dem på ett eller annat sätt i sin marknadsföring. Syftet med denna studie är att genom attitydanalys undersöka hur ett antal faktorer inom marknadsföring på det sociala mediet Twitter påverkar responsen till den. Dessa faktorer var följan- de: inläggets tid, längd och attityd, samt förekomst av media i inlägget. Inläggen samlades från Twitter mellan 28. mars och 28. april och atti- tyden i dem mättes genom attitydanalys, varpå attityden i svaren till reklaminläggen jämfördes baserat på de ovannämnda faktorerna. Re- sultaten visar på att attityden i reklaminläggen och tiden då de läggs upp har störst påverkan på hur svaren ser ut, men inga säkra slutsatser har kunnat dras. Contents 1 Introduction 1 1.1 Research Question . .2 1.2 Hypothesis . .2 1.3 Motivation . .3 1.4 Limitations . .3 2 Background 4 2.1 Social Media Marketing . .4 2.2 Natural Language Processing . .5 2.3 Sentiment Analysis . .5 2.3.1 Methods . .5 2.4 Tools . .6 2.4.1 Twitter Developer API . .6 2.4.2 Vader Sentiment . .6 2.4.3 Linear Regression . .7 2.5 Related Work . .8 3 Methods 9 3.1 Data Gathering . .9 3.2 Sentiment Analysis . 10 3.2.1 Lexicon-based . 10 3.3 Data Analysis . 11 4 Results 12 5 Discussion 17 5.1 Analysis of Results . 17 5.2 Considerations . 18 5.3 Future Work . 19 v vi CONTENTS 6 Conclusions 20 Bibliography 21 Chapter 1 Introduction In recent years the role of social media has expanded far beyond just dealing with our social lives. Social media platforms, such as Face- book and Twitter, now play an integral part in how we interact with politics and the world. Social media also play an important economic role, with many businesses using social media as integral parts of their marketing strategies, taking advantage of the direct interaction with consumers that social media allow. A report compiled by the Content Marketing Institute in North America[8] stated that 96% of business- to-consumer content marketers use social media for marketing pur- poses and some companies, such as Apple, even use social media as a part of their customer support. The success of marketing campaigns is of great importance to the companies launching them. Social media management services, such as Sprinklr and Sprout, have already emerged with the rise of social media’s role in marketing, facilitating the planning and analysis of social media marketing campaigns. Additionally, many social media platforms provide their own research and consultation on marketing strategies for their platforms. The effectiveness of ad campaigns is of- ten measured by brand and campaign awareness by looking at metrics such as increase in followers and mentions following the campaign, view rate and view time, as well as brand sentiment, meaning the gen- eral perception of the brand on social media, not to mention the actual sales figures of the companies in question. Sentiment analysis and natural language processing are two addi- tional tools that can be used in analyzing the response to and success of advertisements. They are both fields of computer science aimed 1 2 CHAPTER 1. INTRODUCTION to make computers able to parse the sentiment or meaning behind language used by humans. Previous research in sentiment analysis has managed to find correlations between basketball players’ perfor- mances in games and the sentiment of the players posts on social me- dia[17] and managed to predict stock market movements by analyzing the sentiment in social media[2]. It is therefore possible that perform- ing sentiment analysis on the large amount of data generated daily by both consumers and businesses on social media, would grant impor- tant insight into the consumer market and the reception of marketing campaigns and the businesses behind them. 1.1 Research Question The question this report aims to answer is in what way the following factors affect the success of a tweet posted on Twitter by a company in the fast food sector: • Time of posting • Length of tweet • Sentiment of tweet • Presence of other media (such as images or videos) The success of a post is defined to be the sentiment of the response following the advertising tweet. 1.2 Hypothesis The hypothesis is that the time of posting of tweets has an impact on the responses they garner. For example, a tweet posted early in the morning is expected to perform worse than a tweet posted later in the day, and a tweet posted during the weekend, when people are likely to be free from work and possibly in a better mood as a result of that, is expected to perform better than a tweet posted during the week. The presence of media is expected to have a positive impact on the success of a tweet, since fast food imagery tends to have a positive effect on our appetite. CHAPTER 1. INTRODUCTION 3 1.3 Motivation The results of this report could potentially grant further insight into social media marketing and the makings of a successful Twitter mar- keting campaign. This could impact how businesses model their ad- vertising strategies and manage their social media, in turn having eco- nomic consequences. It is also possible that this report would grant better understanding of the relationship and interaction between busi- nesses and consumers on social media. 1.4 Limitations This report will only be looking at the posts of a select few fast food companies on Twitter shared between the 27th of March and the 28th of April. These companies are McDonald’s, Burger King, Subway, Pizza Hut, Papa Johns, Wendy’s, Taco Bell, KFC, Chick-fil-A and Domino’s Pizza. The tweets will be collected exclusively from the North Ameri- can branches of the companies. Chapter 2 Background 2.1 Social Media Marketing Social media marketing is the use of social media to promote services and products. Big brands today use social media to a large extent in order to promote their services and products. Facebook, Twitter and Youtube are the most popular social media and are widely used by large companies for marketing purposes [15]. Social media constitute a very cheap and cost-effective marketing platform, since the usage of social media is free in most cases. The main cost for businesses is the time employees spend on planning and executing the social media marketing, as opposed to traditional me- dia such as TV and news media, where advertising slots can be pricy. Additionally, using social media for marketing purposes means that any advertising is made available to the consumers immediately and grants the business the possibility of receiving instant feedback to the marketing efforts. [10] While using social media for marketing has been identified as be- ing crucial for the growth of a company, it does imply some risks for the companies and their public image. If the social media marketing is not carried out with a clearly defined plan that is in line with the existing business goals and is instead carried out in an arbitrary fash- ion, the potential risks are much harder to predict, and the company’s revenue can be negatively affected. [13]. So, while the cost of social media marketing is very low in compar- ison to the existing alternatives, its effectiveness and potential can be both an asset and a risk. It is therefore of great importance that social 4 CHAPTER 2. BACKGROUND 5 media campaigns are carefully planned and that its goals are clearly identified before it is realized, in order to minimise risks and optimize the reception of the campaign and the profits accordingly. 2.2 Natural Language Processing Natural Language Processing (NLP) is a research area that includes the development of computer programs and algorithms to analyse, under- stand or generate natural human language. It also includes modelling and simulation of human linguistic behaviour using computers [4]. NLP is considered as a field in Artificial Intelligence , along with com- puter and information science, linguistics, mathematics, electronic en- gineering and psychology [11]. The most commonly used methods for NLP today are based on machine learning (ML) [3]. 2.3 Sentiment Analysis Sentiment Analysis (SA) extracts the sentiment out of a text and then analyses it.
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