# Consumer Insight on Driverless Automobile Technology Adoption via Twitter Data: A Sentiment Analytic Approach

Michael Adu Kwarteng, Alex Ntsiful, Raphael Kwaku Botchway, Michal Pilik, Zuzana Komínková Oplatková

► To cite this version:

Michael Adu Kwarteng, Alex Ntsiful, Raphael Kwaku Botchway, Michal Pilik, Zuzana Komínková Oplatková. Consumer Insight on Driverless Automobile Technology Adoption via Twitter Data: A Sentiment Analytic Approach. International Working Conference on Transfer and Diffusion of IT (TDIT), Dec 2020, Tiruchirappalli, India. pp.463-473, 10.1007/978-3-030-64849-7_41. hal-03701820

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# Consumer Insight on driverless automobile technology adoption via twitter data:
A Sentiment Analytic approach

Michael Adu Kwarteng [0000-0002-6787-0401] Alex Ntsiful [0000-0002-8337-7596]

Raphael Kwaku Botchway [0000-0002-4726-2898], Michal Pilik [0000-0002-7032-3812]

Zuzana Komínková Oplatková [0000-0001-8050-162X].

¹ Faculty of Management and Economics, Tomas Bata University in Zlin,
Mostni 5139, 76001 Zlin, Czech Republic
{Kwarteng, Ntsiful, Pilik} @utb.cz

² Faculty of Applied Informatics, Tomas Bata University in Zlin,
Nad Stráněmi 4511, 760 05 Zlin, Czech Republic
{Botchway, Komínková} @utb.cz

## Abstract

Technology has sped up the innovation effort in the automobile industry. Further to this
automobile innovation such as intelligent climate control, adaptive cruise control, and
others, we find in today's vehicles, it has been predicted that by 2030, there will be
driverless vehicles, of which samples are already on the market. The news and the sights
of these so-called driverless vehicles have generated mixed reactions, and this moti-
vated our study. Hence the present study focuses on a dataset of tweets associated with
driverless vehicles downloaded using the Twitter API. Valence Aware Dictionary and
sentiment Reasoner (VADER), a lexicon and rule-based sentiment analysis tool were
used in extracting sentiments on the tweets to gauge public opinions about the ac-
ceptance and adoption of the driverless vehicles ahead of their launch. The VADER
sentiment analysis results, however, show that the general discussion on driverless ve-
hicles was positive. Besides, we generated a word cloud to visually analyze the terms
in the dataset to gain further insights and understand the messages conveyed by the
tweets in other to enhance the usage and adoption of driverless vehicles. This study will
enable self-driving vehicle technology service providers and autonomous vehicle man-
ufacturers to gain more insights on how to transform the transportation sector by in-
vesting in research and technology.

**Keywords:** *Driverless vehicle, Twitter, Sentiment analysis, autonomous, technology,*
*innovation*

## 1. Introduction

Technology has sped up the innovation effort in the automobile industry. A shred of evidence is that most of the latest vehicles we find in town are endowed with technological advancements such as automatic braking systems, crash sensors, cruise control, auto speed control, intelligent climate control, adaptive cruise control, advanced emergency
gency braking system, and many others. Further to these advancements, research indi-
cates that by 2030, the automobile industry could fully introduce unto the market, an-
other dynamic product-driverless vehicle (Panayiotopoulos & Dimitrakopoulos, 2018)
which could oust the driver from his/her seat literally. With its other variant names such
as an un-crewed car, unmanned vehicle, self-drive car, robot car, and the like, driverless
vehicle is a type of automobile designed and equipped with artificial intelligence (AI)
in such a way that it can self-sense its environment and navigate safely to its destination.
It is popularly known in the literature as autonomous vehicles (AVs) but for this paper
and many variant audiences in mind, we will use a driverless vehicle. It is interesting
to note that there are already some of these vehicles on the market, though most of them
are being piloted. Morgan (2015) cites Mercedes Benz F015, General Motors GM EN-
V, Google self-driving car, Servvan Robotic Vehicle, BMW E-patrol, BMW Honey
Comb, and the Zoox Level 4 Reversible, as examples of driverless vehicles available.
Morgan notes that some of these vehicles are so intelligent that they could sense the
driver's capabilities and decide to take over the driving when necessary.

However, it is important to note that driverless vehicles would not replace the tradi-
tional vehicles but would rather serve as an addition to the levels of vehicles we have
in the automobile industry. In line with this assumption, SAE international (2016) ex-
plains that there would be five levels of vehicles with level 0 being the fleet of vehicles
which is fully operated by humans and level 4 as those vehicles fully driven without
any human involvement. The levels of 1-3 vehicles are classified depending on the level
of their ‘autonomousity’ or the amount of human involvement in operating them. Extant
studies have argued that the adoption of driverless vehicles on our road comes with a
myriad of advantages (Piao et al., 2016; Sparrow& Howard, 2017; Meyer et al., 2017).
Examples of these benefits include a new line of business (Litman, 2018), reduction in
road accidents (KPMG International, 2019; Piao et al., 2016), and new transport ser-
vice, and new transport means for the aged and physically impaired (Sparrow & How-
ard, 2017; Meyer et al., 2017). For instance, Piao et al (2016) explain that driverless
vehicles could reduce road accidents by 90% because these accidents are caused by
human errors due to factors such as fatigue, alcohol, carelessness, and the influence of
drugs. Despite these merits, another stream of research has found that some members
of society think that accepting driverless vehicles in our fleet may be problematic. They
explain that the challenges, which come with the current level of automobile innova-
tions, mentioned earlier, have not been fully resolved (Panagiotopoulos & Dimi-
trakopoulos, 2018). For instance, studies such as (Milakis et al., 2017; Van Brummelen
et al.,2018) have found that drivers have trust issues with automated system vehicles
and also believe that the automated systems come with some workload and situation
awareness issues for the drivers. To this end, researchers have begun to engage the
public on their possible acceptance of the incoming technology and also the factors that
could influence their adoption (Payre et al., 2014; Xu et al., 2018). Thus, to add to this
body of initial and pre-driverless vehicle launching research is the main objective of
this study. However, we approach this research task with a different method, that is, the
sentiment analysis via twitter data. Specifically, the study seeks to gauge the public
opinions (positive and negative) about the acceptance and adoption of the driverless
vehicles ahead of their launch to offer insights to engineers, product designers, and
policymakers of automobile industries, especially those specializing in unmanned ve-
hicles, by using sentiment analysis via twitter.

We believe that both the generation Z and generation alpha, who would be ultimate
users of the driverless vehicles, mostly use social media to search for information on
the latest technology (Rathore et al, 2016), and that a medium such as the twitter could
give reach information needed for the study. We also contend that using a survey to ask
people ‘on the street’ about the driverless vehicle, they might have not seen a demo, a
method which the previous studies used (e.g. Daziano et al., 2017; Chowdhury &
Ceder, 2016) may not be realistic or the best. With the sentiment analysis, we can ex-
tract, from people who have seen a demo of this yet-to-be-launched innovation, rich
data of opinions, ideas, challenges, and other sentiments they may have with the tech-
nology. We also contend that the sentimental data is richer in that they are not responses
to a survey question where a respondent may give socially desirable answers but we are
extracting ‘post-natural responses to unasked questions. Moreover, this study’s popula-
tion is global, as we do not use twitter sentiments from one location but all users of
twitter. Our study is indeed significant for several reasons: First, we argue that, apart
from the unique methodological approach highlighted above, the findings of this study
will set the tone for further deductive empirical research. Second, the findings can also
serve as a guide to policymakers in developing automobiles, and road and traffic man-
agement policies. Finally, the results can also guide driverless vehicle engineers to in-
corporate consumer preferences and challenges into driverless vehicle engineering de-
signs.

The rest of the paper is organized as follows. Next to this introduction is the literature
review, followed by a snapshot of sentiment analytic approach. Following that is the
analysis of data and findings. We end the paper with a short discussion and conclusion.

## 2.0 Literature Review

### 2.1 Automobile technology adoption and consumer insights

Like with any other innovation, which comes to the market, customers may be skeptical
of the driverless vehicle and would begin to raise questions on several issues. For in-
stance, in Egbue and Longs (2012) study involving consumers' attitudes towards the
adoption of electric vehicles, it was found that cost and performance were rated higher
than environmental and sustainability benefits. Before this finding, Axsen et al. (2010)
have found that limitations with battery technology and the high cost of the battery were
key barriers to electric vehicle adoption. Similarly, the cost has been anticipated to be
a potential barrier to adoption when driverless vehicles become fully functional
(Fagnant & Kockelman, 2015). They explain that driverless vehicles require technolo-
gies like sensors, software for each automobile, and communication and guidance tech-
nology, which are expensive and build up to the total cost of the vehicle. In this regard,
Shchetko (2014) estimates that Light Detection and Ranging (LIDAR) systems, a com-
mon essential device for driverless vehicles, is priced between cost $30,000 to $85,000
and this cost excludes the cost of software, sensors, software, engineering, and extra power and computing requirements.

By giving a fair idea of how much driverless vehicles may cost, we refer to Dellen-back (2013) estimates of the cost of 2013 civilian and military driverless vehicles, which stood at $100,000 in 2013. Further, earlier studies have also found other adoption challenges for driverless vehicles including trust (Bansal et al., 2016; Kyriakidis et al., 2015). Although Paden et al., (2016) have highlighted high carbon emission, excessive traffic and accidents as the challenges we have with the conventional vehicles, problems which are not found with the driverless vehicle, Fagnant and Kockelman (2015)'s findings of security, trust, privacy, reliability, and liability with the autonomous vehicle cannot be discounted. By shedding more light on these concerns, Fagnant and Kockelman (2015) argue that there is the need to worry about electronic security as there are possibilities of computer hackers, terrorist organizations, and demotivated employees who could sabotage driverless vehicle and that could result in accidents and traffic on the roads. When the driverless vehicle becomes operational, data and information sharing become a common ritual and that is where privacy issues set in (Fagnant & Kockelman (2015). Despite these drawbacks, Kaur and Rampersad (2018) argue that there may be certain situations, which would make some consumers opt for driverless vehicles as compared to others, and that further research is needed to uncover those situations.

## 2.2 Overview of driverless automobile technology

The definition for driverless vehicle ranges from a vehicle that operates without human driver (Paden et al., 2016), to a vehicle whose critical control functions such as steering, braking, throttling is managed without the driver's support (NHTSA, 2013). It is envisaged that the introduction of the driverless vehicle can help reduce about 1.2 million road fatalities, which according to WHO (2015), occurs every year. Interest in this driverless automobile technology is said to have started as far back as 1939 during the World's Fair held in New York (Levy, 2016). However, LeValley (2013) reports that fully developed autonomy occurred in the early 21st Century. Levy explains that the developer of these autonomous technologies took inspiration from the Defense Advanced Research Projects Agency (DARPA), which developed driverless technology for the military. According to DARPA (2014), the military driverless vehicle was intended to reduce the number of soldiers who lose their lives on the war front. Gradually, DARPA continued to develop this concept until they had a breakthrough (called the DRAPA's first Urban Challenge) in 2007 when they have autonomous vehicles, which were capable of navigating through city-like terrain, obey traffic regulations, change and merging lanes while avoiding road obstructions.

Since that breakthrough, interest in autonomous vehicles has surged up. Extant studies have it that technology giants like Google, Tesla, and Uber, and well-known automobile firms like General Motors, Ford (all in the US) together with their European and Japan counterparts have made significant progress in this regard (Chehri & Mouftah, 2019). As the tech and automobile giants make progress, governments and legislative bodies begin to develop strategies, promulgate laws and regulation, and build infrastructure like smart cities, to support and in ahead of the launch of the driverless vehicles. Infrastructures are very essential to its success because, according to Chehri
and Mouftah (2019) autonomous vehicle uses a different range of technologies includ-
ing radar, cameras, radio antennas, and the support of artificial intelligence, 5G network
to safely navigate on roads. This suggests that for a country to adopt driverless vehicles
certain infrastructures must be in place as well as amendments to its laws, especially
those relating road and traffic issues.

To this end, Johnsen et al. (2017) indicate that German Bundestag has reviewed their Road Traffic Act given this new paradigm shift in the automobile industry. A section of the amended Road Traffic Act reads: ‘*The driver may turn his attention away from the traffic situation and vehicle operation if the car is in an automated or autonomous driving mode, but she/he must in principle remain vigilant so that he can immediately take control of the vehicle again, if necessary* (Johnsen et al., 2017, p. 49). Moreover, many other countries are preparing well for the task ahead in automobile technology and this has led to a periodic compilation of countries' readiness, known in literature now as Autonomous Vehicle Readiness Index (AVRI) by KPMG international since 2018. Thus, according to KPMG (2019)'s Autonomous Vehicle Readiness Index (AVRI), there are about 25 countries that are ready to embrace the driverless vehicles' agenda with Netherland leading the chart. The AVRI has four key measures and these include policy and legislation, technology and innovation, infrastructure, and consumer acceptance. Netherland leads the 2019 chart because it had 1st position in the infrastruc-tural ranking, 2nd on consumer acceptance, 5th on policy and legislation, and 10th in technology and innovation category, culminating to total points of 25.05. Four other countries following Netherland include Singapore (2nd, 24.32points), Norway (3rd, 23.75), United States (4th,22.58 points), and Sweden (5th, 22.48 points). For the com-prehensive list of the ranking, see the KPMG AVRI report 2019.

2.3 A snapshot of sentiment analytic approach via twitter

Sentiment analysis, popularly known as opinion mining, has been long studied in
both academia and the industry (Grover & Akar, 2017; Kar & Dwivedi, 2020 ). It
employs computational algorithms in the form of natural language processing bent on
identifying sentiment polarity, intensity, and topics, particularly where the so-called
sentiments apply (Liu, 2005; Chamlertwat et al, 2016). Sentiment analysis in practice
turns out to automate opinion discovery and classification systems that deal with a huge
amount of data by purposefully extracting and understanding complex human-gener-
ated content/judgment. (Lake, 2011)

In the last decade, the concept of sentiment analysis has become one of the most
topical and researched areas in machine learning (see, Agarwal, et al,2011; Whitelaw
et al, 2005). Technically, sentiment analysis applies to different levels of text granular-
ity (Agarwal, 2011). Thus, the scope of the sentiment analysis process involves docu-
ment-level classification task to a finer-grain level of a sentence and then to the phrase
level for execution. (Wilson, 2005). However, there are two main approaches used in
sentiment analysis, and these can be categorized under the machine learning approach
and Lexicon based approach. The machine learning approach works as a supervised
learning approach where the training process involves classifies input into output man-
ually. Once training data with sentiment values are captured by the process, the corre-
sponding domain data will generate results. On the other hand, the lexicon-based approach used in this study works as an unsupervised learning approach. It works based on the features fed by the encoded sentimental lexicon score to analyze the polarity whether it is positive or negative.

Over the years, sentiment analysis has been used extensively in several areas such as deducing opinions of customers' in the banking sector (Botchway et al, 2020), products review data analysis (Fang, & Zhang, 2015), gaining insights in telecommunication usage in Ghana (Nabareseh, et al, 2018)

## 2.4 Research Questions

Extant literature has shed more light on user-generated content on social media (Afful-Dadzie et al, 2016; Feldman, 2013; Aggarwal et al., 2011). This paper employed opinion mining/sentiment analysis to do a rigorous analysis using unstructured textual information on Twitter sites (data). The following research questions guided the study.

1. What are the characteristics of driverless vehicle adoption tweets? Are there any patterns of negativity or positivity associated with the adoption and usage of driverless cars on twitter sites?

2. What are the sentiments of driverless vehicles tweets? How do customers feel about the use of driverless cars?

# 4. Methodology

## 4.1 Data collection and pre-processing

Twitter has become the most preferred social media channel with 330 million monthly active users according to Statista (2019). The dataset consists of 11,000 tweets collected between May, 20, 2020, and June 29, 2020, using the Twitter streaming Application Programming Interface (API). With the help of the python library Tweepy (Feldman, 2013), we collected tweets that contained the keyword “self-driving cars” on Twitter. We then proceeded to clean the text by removing duplicate tweets, punctuations, stop

<figure><img src="image_2.png" /></figure>
<caption>**Figure 1:** Model framework of our Study</caption>

words, URLs, slangs, @ symbol used to mention usernames and converted all text to lowercase. Additionally, we omit terms with low frequency and filter out meaningless words. Consequently, 9590 documents (tweets) were stored as a corpus in a comma-separated value (CSV) format after the cleaning process.

## 4.2 Document Analysis

We used a lexicon-based sentiment analysis approach for our work (Grover et al, 2018). Although the lexicon approach is considered an unsupervised method, it is a major sentiment analysis technique that categorizes text documents into a set of predefined sentiment classes. The Valence Aware Sentiment Dictionary and Reasoner (VADER), a lexicon and rule-based tool specifically attune to sentiments expressed on social media (Hutto & Gilbert, 2014) were deployed from the python NLTK 3.4.1 toolkit. Given a document, VADER examines its lexical features to determine an initial sentiment score before applying five different rules based on grammatical conventions and syntax to amend that score. These rules handle capitalization and exclamation marks as sentiment amplifiers. Besides, they also handle negations and contrastive conjunctions well. VADER produces positive, negative, neutral, and compound scores for each tweet in the dataset. The positive, negative, and neutral scores are ratios for proportions of text that lie in these categories whereas the compound score sums up all the lexicon ratings which have been normalized between -1(most extreme negative) and +1(most extreme positive).

## 4.3 Findings

Our initial experiments reveal that VADER with a threshold value of 0.2 produces the best values of precision and recall, with an improvement in both classification directions (positive and negative) in terms of metrics. Hence documents (tweets) with VADER scores greater than 0.2 were classified as positive, documents with scores between 0 and 0.2 are classified and neutral with all other tweets with scores less than zero classified as negative. Out of 9590 documents (tweets), 4014, 1976, and 3600 were classified as positive, negative, and neural respectively. Figure 2 shows the proportion of sentiment classes. We generate a word cloud based on the frequencies of the words used in the dataset to determine the size of the words to gain further insights into the opinions expressed in the tweets.
<figure><img src="image_3.png" /><figcaption>Figure 2 Sentiment class graph</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>Figure 3 Top 10 hashtags from the dataset</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>Figure 4 Negative sentiments word cloud</figcaption></figure>

## 5. Short discussion and conclusion

In this study, we performed the sentiment analysis of 9590 tweets using the VADER lexicon. The tweets were classified into three classes namely: positive, negative, and neutral. VADER classified 4014 tweets as positive while 1611 and 3965 tweets were classified as negative and neutral respectively. The sentiment analysis results indicate that the overall discussion on self-driving cars in terms of usage and adoption involving stakeholders on Twitter was positive. This is quite encouraging judging from the increasing popularity self-driving vehicle technology is currently enjoying coupled with its huge potential to transform the transportation system (and by extension the economy and society). Analysing Figure 3 (Top 10 hashtags) and Figure 4 (word cloud of negative sentiments) provides further insights from different perspectives. Artificial intelligence (AI) tops the list in Figure 3 which shows the significant impact and pioneering role of AI in autonomous vehicle development (Maayan-Wainstein, 2020). An example is deep reinforcement learning (DRL), which combines strategies of deep learning and reinforcement learning to enhance the automation of training algorithms in applications used for lower-level vehicle automation. Since the bigger and bolder a word appears in
a word cloud depicts the importance and frequency with which the word is mentioned within a given context, prominent words like “car”, “people”, “crashes”, “ai”, “problem” found in the negative sentiment word cloud raises safety and technology issues that require attention. Investigating the tweets containing these terms to find out the actual message they carry will go a long way to enhance the usage and adoption of autonomous vehicles. In our view, the explanation provided above adequately answers research questions 1 and 2, which also collaborates with recent studies performed by researchers (Botchway et al., 2019; Afful-Dadzie et al., 2018; Ibrahim & Wang 2019). Ignoring non-English tweets was a major limitation to this study since their inclusion could potentially enrich the work by providing further insights that could be exploited. In the future, methods such as topic modeling and time-series amongst others could be applied to improve the analysis of tweets in the dataset.

## Acknowledgment

This work was supported by the research project NPU I no. MSMT-7778/2019 RVO - Digital Transformation and its Impact on Customer Behaviour and Business Processes in Traditional and Online markets and IGA/CebiaTech/2020/001. The work was further supported by the resources of A.I. Lab at the Faculty of Applied Informatics, Tomas Bata University in Zlin (ailab.fai.utb.cz).

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