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Tweets About Self-Driving Cars: Deep
Sentiment Analysis Using Long
Short-Term Memory Network (LSTM)
Anandi Dutta and Subasish Das
Abstract Due to the extensive growth of social media usage, sentiment analysis
using social media data such as Twitter is an important task. The current study
presents an empirical investigation of consumer sentiment toward self-driving cars
or autonomous vehicles (AVs) based on the acquired self-driving car-related tweets.
Information retrieval in social media is a complex task that requires technical insights.
We used a hierarchical attention-based long short-term memory network (LSTM), a
popular deep learning tool, to classify sentiment-specific document representations.
The findings show that favorable attitudes toward AVs are associated with technologi-
cal advantages and safety improvements, while more negative attitudes are associated
with self-driving car-related crashes, media coverage, and deployment uncertainty.
The results show that the estimated accuracy of LSTM is 85%. Our study indicates
the necessity of examining big social media data in understanding the perceptions of
end-users toward autonomous vehicles.
Keywords Autonomous vehicles ·Sentiment analysis ·Opinion mining ·Deep
learning
1 Introduction
Emerging technologies such as self-driving cars or automated vehicles (AVs) are sig-
nificantly transforming the transportation system. Companies like Tesla and Google
are competing to attract more people to purchase AVs. Vehicle autonomy is one of
the most controversial new technologies, and it is unfamiliar to many consumers. In
this context, the future market and strategic environment for deployment have had
A. Dutta
The University of Texas at San Antonio, San Antonio 78249, USA
e-mail: anandixd@gmail.com
S. Das (B
)
Texas A&M Transportation Institute, Bryan 77807, USA
e-mail: s-das@tti.tamu.edu
© Springer Nature Singapore Pte Ltd. 2021
D. Gupta et al. (eds.), International Conference on Innovative Computing
and Communications, Advances in Intelligent Systems and Computing 1165,
https://doi.org/10.1007/978-981- 15-5113- 0_40
515
516 A. Dutta and S. Das
a significant foundation laid by customer perception. As AVs become more preva-
lent, authorities, automakers, and companies must investigate their consequences and
impacts [1]. To improve consumer knowledge and to facilitate a better understanding
of the associated benefits of AVs, researchers conduct robust measurement with the
help of sentiment mining.
Social media is a major platform for users to express their opinions and concerns
regarding AV technologies and other related subjects. Analysis of these sentiments
can enable automakers to monitor their product life-cycle and obtain a data-driven
perspective on consumer attitudes and feedback. Many studies conducted analysis
on the public’s perception of AVs using different survey tools such as polls, and
telephone interviews [2,3]. Most of these studies have categorized different types of
users’ opinions based on age, gender, location, and other socio-demographic char-
acteristics. For example, some studies have concluded a totally different level of
acceptance in men than in women. Despite the large number of studies in this field,
there is limited number of studies on social media mining and self-driving cars.
There is a need for an in-depth study on people’s acceptance level and perceptions
regarding AVs.
In the present study, we collected approximately 38,000 unique tweets associated
with self-driving cars. We used a hierarchical attention-based long short-term mem-
ory network (LSTM) to predict customer sentiments from the text narratives of the
collected tweets. The intent of this paper is to address two research questions: (1)
RQ1: What is the general sentiment trend of the end-users toward self-driving cars?
(2) RQ2: Are deep learning tools adequate in classifying sentiments from self-driving
car-related tweets?
2 Related Work
In recent years, AV research has begun to focus more on the public perception of
AVs and the factors that can influence the likelihood of potential users adopting AVs.
In a recent study, Greig [4] conducted two surveys to determine public sentiment
toward the adoption of AVs. The study found that only one-fifth of the respondents
had positive feelings toward AVs, and a majority of them remained skeptical of AVs’
benefits. In another study, Zmud et al. [5] conducted an online survey in Austin,
Texas, to determine potential user intent toward AVs. The researchers classified
the respondents into four intent-to-use classifications. The ‘very unlikely’ category
contained 18% of respondents; 32% of respondents were in the ‘somewhat unlikely’
category; 36% were in the ‘somewhat likely’ category; 14% were in the ‘very likely’
category.
A recent study by Merat et al. [6] focused on the social-psychological factors that
can influence people’s acceptance of SAE Level 4 shared AVs (SAVs). The study
found that the most effective pathway to the widespread acceptance and adoption
of AVs is an incremental and iterative stage-by-stage process that provides users
with hands-on experience throughout every stage. In another study, Regan et al. [7]
Tweets About Self-Driving Cars: Deep Sentiment Analysis … 517
analyzed survey data from 5263 participants to measure public awareness of AVs
in Australia and determine people’s knowledge of AVs and the likelihood of them
adopting automated cars. In a similar Australian study, Greaves et al. [8] conducted
an online survey of 455 people to investigate consumer sentiment toward AVs. The
study found that young male respondents, less frequent drivers, and people open to
the idea of sharing their car were associated more highly with favorable attitudes
toward AVs. Negative attitudes were associated more with older female respondents,
those who drive more frequently, and those who are less open to sharing their car.
Liljamo et al. [9] examined the results from a large citizen survey of 2036 people
to determine the magnitude and type of concerns that people have in regard to the
adoption of AVs. The study found that the key perspectives that affect public approval
of AVs are traffic safety and ethical perspectives. Another study was conducted by
Nazari et al. [10]. This study jointly modeled public attention in both private AVs and
multiple shared autonomous vehicles (SAVs). In 2016, Sener et al. [11] conducted
a survey to examine the factors influencing people’s intent to use AVs in the future;
the survey included people in various Texas cities, including Dallas, Houston, and
Waco. The factors that had the most significant effect were attitudes toward self-
driving vehicles, execution expectancy, apparent protection, and community impacts.
Nordhoff et al. [12] conducted a survey with 10,000 participants to determine the
relationship between socio-demographic characteristics and people’s acceptance of
AVs. Asgari and Jin [13] employed a structural equations model with latent variables
to analyze consumers’ willingness to pay and adoption (WTPA) for different AV
categories. The model simultaneously regressed adoption and WTPA measures for
multiple variables, including demographic and socioeconomic factors, car ownership
and usage, and personal opinions or preferences.
3 Data Description
With approximately 500 million tweets per day, Twitter provides real-time textual
contents with a wide range of themes and topics. We used the open-source R soft-
ware package ‘twitteR’ [14] to collect relevant data. For tweet collection, users need
to collect data via Open Authorization (OAuth), an authentication mechanism that
allows Web tools to provide the user applicability to a Web service without allowing
an end user’s credentials to the client itself, was required for all Twitter-related data
collection.
To gather the relevant data, several keywords have been used during the
data collection process. The keywords include ‘driverlesscar,’ ‘driverlessvehicle,’
‘driverless,’ selfdrivingcar,’ ‘selfdrivingvehicle,’ ‘autonamtedcar,’ ‘automatedvehi-
cle,’ ‘autonomouscar,’ and ‘autonomousvehicle.’ The dataset was collected over a
span of four months in 2019 (March 12, 2019–July 16, 2019). A total of 79,214
unique tweets were collected. These tweets were retweeted 211,090 times and are
associated with approximately 37,000 Twitter handles.
518 A. Dutta and S. Das
4 Methodology
4.1 Long Short-Term Memory (LSTM) Networks
In 1977, Sepp Hochreiter and Jürgen Schmidhuber proposed long short-term mem-
ory (LSTM), which is a modified version of recurrent neural network (RNN) model.
Unlike conventional neural feed-forward networks, LSTM has response links that
provide a ‘general-purpose computer’ capable of calculating anything a Turing
machine can [15,16]. By presenting Constant Error Carousel (CEC) units, LSTM
manages the problems regarding vanishing and exploding gradient. In theory, the con-
ventional RNN models can handle the long-term dependencies in the input sequences;
however, the dilemma emerges when the model is in computational nature. While
preparing a typical RNN model applying the method of back-propagation, the gradi-
ents that are back-propagated tend to zero or infinity due to the calculations associated
with the method that require precise numbers. Therefore, the flow may be altered,
resulting in a different outcome than expected [17].
4.2 Our Approach
A natural language processing or NLP tool that concentrates on distinguishing nega-
tive and positive opinions, evaluations, and emotions expressed in text data is referred
to as sentiment analysis. The common approach of performing sentiment analysis is
to compare the presence of a word in a document and assignment sentiments based
on some established sentiment lexicons [18]. There exist several existing sentiment
lexicons with sentiment scores assigned to a group of words. For example, Affective
Norms for English Words (ANEW) was one of the most common sentiment lexicons.
‘AFINN,’ which was used in this study, performs better than ANEW [19].
The usual practice in machine learning and deep learning is to divide the dataset
into several groups: training, test, and validation datasets. It is important to note
that we developed the model based on the training data and the performance of
the model will be tested and validated by the other two datasets. The training for
model building, the validation data for out-of-sample error measurement and model
selection, and the test data are used for final model performance evaluation. We used
several deep learning R packages to perform the analysis [20,21]. Before running
the deep learning models, we performed word embeddings [22]:
•Word embeddings:Theword2vec is used to convert a sentence into its vector
representation. In the present approach, for a sentence S={w1,w
2,...,w
n},
where nrepresents the count of words in S. Here, each word, wi, is related with a
D-dimensional vector embedding, Xi∈Rd. The word vector representations are
concatenated in their individual order, X1:n=X1⊕X2⊕...⊕Xn, in which ⊕is the
Tweets About Self-Driving Cars: Deep Sentiment Analysis … 519
concatenation operator. Additionally, each sentence is padded with zero-vectors
to a fixed length.
For the deep sentiment analysis, we used the complete set of 37,999 unique tweets.
These tweets were divided into three datasets: training (60%), validation (20%),
and testing (20%). The sentiment scores are divided into three categories: positive,
negative and impartial. Some of the parameters of the model development are: (1)
Layer embedding: Output dimension =32, (2) Embedding layer dropout rate =0.5,
(3) Layer LSTM: units =256, dropout =0.2, recurrent dropout =0.2, (4) Layer
Output: units =3, activation =‘softmax.’
For each of the datasets, ratio of positive and negative sentiment is approximately
3:1. This finding answers RQ1 by indicating that positive sentiments are higher
toward AV-related tweets.
5 Results and Discussions
The loss and accuracy values for different epochs are shown in Fig. 1.Weusedseveral
performance measures to examine the model performances. True positive (TP) and
false positive (FP) represent measures of accurate and inaccurate classifications per
real classification category, respectively. True negative (TN) and false negative (FN)
are measures of accurate and inaccurate rejections per real classification category,
respectively. Some of the common measures are the following:
•Recall or sensitivity =TP
TP +FN
•Specificity =TN
TN +FP
Fig. 1 Performances of the model for training and validation data
520 A. Dutta and S. Das
•Prevalence =TP +FN
TP +TN +FP +FN
•Positive Predictive Value =TP
TP +FP
•Negative Predictive Value =TN
TN +FN
•Accuracy =TP +TN
TP +TN +FP +FN
•Balanced Accuracy =TP
TP +FN ×0.5+TN
TN +FP ×0.5
•Detection Rate =TP
TP +TN +FP +FN
•Detection Prevalence =TP +FP
TP +TN +FP +FN
Tables 1and 2list all performance measures calculated from the LSTM outputs.
The results show that the accuracy is 85.5% for train data (95% confidence range:
[85.1, 85.9%]), 82.6% for validation data (95% confidence range: [81.7, 83.4%]), and
82.8% for test data (95% confidence range: [81.9, 83.7%]). The findings answer RQ2
by providing evidence that deep learning tools are adequate in classifying sentiments
from self-driving car-related tweets.
6 Conclusions
In this paper, we presented an application of deep learning-based method for sen-
timent classification of self-driving car-related tweets. This study demonstrates the
capability of obtaining hidden trends about the consumer opinions and sentiments
from Twitter posts with high accuracy. This paper introduces the concept of applying
deep learning tools to classify AV-related sentiments and specifically outlines how
the findings benefit the decision-making process of the future marketplace of AVs.
Moreover, the paper reveals future customer needs in the context of AV deployment.
The current framework developed in this study contributes to the ongoing state-of-
the-art studies associated with application of sentiment analysis to understanding the
perception of people regarding AVs.
We applied deep learning tools to unearth the nature of public understanding
toward AV technology in the form of attitudes, sentiments, and opinions. The current
approach used in this study indicates that text narratives in self-driving car-related
tweets have significant key attribute measures that can be used in classifying different
types of sentiments. This study has some limitations. For example, all words are
Tweets About Self-Driving Cars: Deep Sentiment Analysis … 521
Tabl e 1 Performance measures of train, validation, and test data
Data Measures Negative Neutral Positive
Train Sen 0.8135 0.3552 0.9547
Spe 0.9393 0.99056 0.759
PPV 0.8451 0.80404 0.8622
NPV 0.9252 0.93374 0.9139
Pre 0.2894 0.09829 0.6123
DR 0.2354 0.03491 0.5845
DP 0.2786 0.04342 0.678
BA 0.8764 0.67288 0.8569
Validation Sen 0.7734 0.29918 0.9321
Spe 0.9173 0.98879 0.7253
PPV 0.7904 0.73986 0.845
NPV 0.9094 0.92976 0.8693
Pre 0.2874 0.09632 0.6163
DR 0.2222 0.02882 0.5745
DP 0.2812 0.03895 0.6799
BA 0.8453 0.64398 0.8287
Tes t Sen 0.7675 0.31773 0.9339
Spe 0.9244 0.98564 0.7264
PPV 0.8071 0.6935 0.8452
NPV 0.906 0.9339 0.873
Pre 0.292 0.09276 0.6153
DR 0.2241 0.02947 0.5746
DP 0.2776 0.0425 0.6799
BA 0.8459 0.65169 0.8302
Note:Sen sensitivity, Spe specificity, PPV positive predictive value, NPV negative predictive
value, Pre prevalence, DR detection rate, DP detection prevalence, BA balanced accuracy
weighted same for the sentiment scores. No specific weights were provided for high
influential and relevant words associated with the current format of datasets. The
same holds for negative words. Another potential improvement would be related to
the exposure of sentiment fluctuations over time. Currently, the study is limited to
four months in 2019, which is a short temporal window. Future research is needed
to improve the proposed approach and its effectiveness to future marketplace of AV
technologies.
522 A. Dutta and S. Das
Tabl e 2 Accuracies and
p-values by train, validation,
and test data
Data Measures Val u e s
Train Acc 0.8549
95CI (0.8503, 0.8595)
NIR 0.6123
p-value <2.2e−16
K0.7098
p-value (MT) <2.2e−16
Validation Acc 0.8255
95CI (0.8168, 0.834)
NIR 0.6163
p-value <2.2e−16
K0.6485
p-value (MT) <2.2e−16
Tes t Acc 0.8282
95CI (0.8195, 0.8366)
NIR 0.6153
p-value <2.2e−16
K0.654
p-value (MT) <2.2e−16
Note:Acc accuracy, 95CI 95% confidence interval, NIR no
information rate, p-value =p-value [Acc > NIR], Kkappa, p-value
(MT) mcnemar’s test p-value
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