View of Social Media-Based Sentiment Analysis: Electric Vehicle Usage in Indonesia

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Jln. Khatib Sulaiman Dalam, No. 1, Padang, Indonesia, Telp. (0751) 7056199, 7058325 Website: ijcs.stmikindonesia.ac.id | E-mail: [email protected]

Social Media-Based Sentiment Analysis: Electric Vehicle Usage in Indonesia Helmi Salsabila¹, Roni Habibi², Nisa Hanum Harani³

[email protected], [email protected], [email protected] Universitas Logistik dan Bisnis Internasional

Article Information Abstract Submitted : 23 Jun 2023

Reviewed : 27 Jun 2023 Accepted : 30 Jun 2023

This research analyzes the sentiment of using electric vehicles in Indonesia through social media using more than 10,000 Twitter data. The results indicate a variation of positive, negative, and neutral sentiments towards electric vehicles on social media. Female users play a significant role in expressing their views and actively participating in discussions related to electric vehicles. The locations with the highest user activity discussing electric vehicles are Indonesia, DKI Jakarta, Makassar, Tangerang, and Karawang. The peak activity was observed in September 2019, suggesting a significant interest in electric vehicles. The SVM algorithm achieved an accuracy of 88% for positive and neutral sentiments, but performed relatively lower for negative sentiments. This research lacks data on the gender and age of the respondents. Future studies should address these shortcomings to gain a deeper understanding of public perceptions regarding electric vehicles in Indonesia.

Keywords Sentiment, Electric vehicles, Social media, Indonesia

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A. Introduction

In recent years, the use of electric vehicles has become a global focus of attention as a more environmentally friendly alternative in the transportation sector[1]. In an effort to reduce emissions and enhance the sustainability of the transportation sector, the Indonesian government has implemented policies and incentives to encourage people to use electric vehicles. According to the Ministry of Industry (Kemenperin) as of September 2022, the number of electric vehicles (excluding hybrids or other types) in Indonesia has reached over 25,000 units.

Specifically, there are 21,668 electric motorcycles, 3,317 electric cars, 274 electric three-wheeled vehicles, 51 electric buses, and 6 electric goods vehicles. The total population of electric vehicles in the country has reached 25,316 units[2].

In the implementation of electric vehicles, there are various opinions existing within the society[3]. The opinions and sentiments regarding the use of electric vehicles are highly diverse[4]. Some strongly support and view electric vehicles as an effective solution to reduce pollution and dependence on fossil fuels. Supporters believe that electric vehicles can help reduce greenhouse gas emissions, improve air quality, and decrease dependence on petroleum[5].

However, there are also skeptical opinions regarding electric vehicles[6].

Some people doubt the availability of adequate battery charging infrastructure, limited range, and longer charging times compared to conventional fueling[6], [7].

Opinions and sentiments regarding the use of electric vehicles are often expressed through social media[8]. Social media users can directly share their views through posts, comments, or by using relevant hashtags[9]. Social media serves as an important channel for individuals to share their opinions, experiences, and sentiments related to various topics, including the use of electric vehicles[10].

Therefore, sentiment analysis based on social media can be a highly useful tool for understanding public views and attitudes towards electric vehicles in Indonesia.

Previous studies have been conducted in the context of sentiment analysis based on social media regarding the use of electric vehicles in other countries. For example, a study by [11] conducted sentiment analysis based on social media in China and found that the majority of sentiments related to electric vehicles were positive, citing reasons such as environmental cleanliness, energy efficiency, and advanced technology. On the other hand, a study by [12] a study conducted in the United States found that public sentiment regarding electric vehicles exhibited high variability, with some positive aspects (such as sustainability) and negative aspects (such as cost and availability of charging infrastructure).

However, in the context of Indonesia, research on sentiment analysis based on social media regarding the use of electric vehicles is still limited. Therefore, this study aims to contribute to the knowledge about public sentiment in Indonesia regarding electric vehicles and to understand the factors influencing such sentiment.

This study will integrate relevant supporting theories with the research problem. The theories that may be relevant in this context include the theory of innovation adoption, consumer behavior theory, human ecology theory, and social communication theory. Related studies will be used as theoretical foundations to support the understanding and explanation of the phenomenon of social media- based sentiment analysis in the use of electric vehicles in Indonesia.

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The aim of this research is to conduct a social media-based sentiment analysis focusing on the use of electric vehicles in Indonesia and to gather data from popular social media platforms in Indonesia, such as Twitter, to analyze public sentiment regarding electric vehicles. Additionally, this study aims to identify the factors influencing positive or negative sentiment related to the use of electric vehicles in Indonesia and to apply a model that utilizes machine learning techniques to classify sentiment related to electric vehicles into positive, negative, or neutral categories.

The findings of this research are expected to provide valuable insights and a deeper understanding of public sentiment regarding the use of electric vehicles in Indonesia.

B. Research Method

In this research, there are several stages of the process to be conducted. The first stage is data collection or "data crawling". Once the data is collected, the next stage is "data preprocessing". Next, the data will go through the "translated text"

stage for text translation. After that, the next stage is "polarity intensity measurement. Once the polarity measurement process is completed, the data will undergo the "labeling data" stage. The next stage is "modeling", where a model will be developed. After the model is established, the final stage is "model evaluation".

An overview of the research process stages can be seen in Figure 1. The stages begin from data collection to model evaluation, passing through preprocessing, translated text, polarity intensity, labeling data, and modeling stages. Detailed information can be found in Figure 1.

Figure 1. Diagram Design 1. Data Crawling

In this stage, data will be collected from popular social media platforms in Indonesia, particularly Twitter. Data collection will involve web scraping techniques using the "snscrape" library as a tool to retrieve data from Twitter[13].

The data to be collected will consist of public sentiment regarding the use of electric vehicles in Indonesia. The keyword used to collect the data will be "electric vehicles". The data collection process was conducted using the Google Colab

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platform to facilitate the retrieval of data in a significant amount through web scraping techniques using the "snscrape" library, resulting in more than 10,000 tweet data. The data crawling process took place on June 18, 2023, and it was estimated to require approximately 2-3 hours to complete. In this research, only the attributes listed in Table 1 will be used, and these attributes will be supplemented with the "gender" attribute.

2. Pre-Processing

After successfully obtaining the data, the next step is to perform data preprocessing to extract relevant features from each tweet. Pre-processing is done to clean the data from noise or irrelevant information[14]. Several pre-processing steps that will be carried out are as follows:

A. Filtering

Filtering is an important initial step in selecting the features to be used[15]. In the filtering process, features that do not have informative value or do not provide significant contributions will be removed. This is done to focus attention on the most relevant features and gain a better understanding of public sentiment regarding the use of electric vehicles. Detailed information can be found in Table 1.

Table 1. Attribute Filtering Result

No Attribute Description

1 Datetime The time and date when a tweet was posted

2 Username The Twitter username of the account that sent the tweet 3 Text The main text contained within a tweet

4 Gender The gender of the Twitter account users who send the tweets.

5 Location The geographic location of the user who sent the tweet

B. Cleaning

In this stage, special characters, punctuation marks, links, and irrelevant information are removed from the collected tweet data[15]. Data cleaning is performed to eliminate noise or disturbances in the tweet text, ensuring that the data used in the analysis is cleaner and more focused on relevant information.

Detailed information can be found in Table 2.

Table 2. Cleaning Result

No Before After

1

Setuju sih klo ada kendaraan listrik asalkan fasilitasnya menunjang kayak misal stasiun pengisian daya yg cukul mudah ~'

setuju sih klo ada kendaraan listrik asalkan fasilitasnya menunjang kayak misal stasiun pengisian daya yg cukul mudah

C. Gender Detection

Gender detection is an attribute that will be added to Table 1, which is the result of analyzing the usernames of users in the tweet data. Gender detection aims to identify the gender of users based on the usernames used in their social media accounts and can provide additional insights into potential different perspectives in the related sentiments[16]. In the process of detecting the gender of users, a common method used is by comparing the username with a gender name dictionary. If there is a match between the username and a name in the dictionary, the corresponding gender will be assigned. This method relies on the association

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between names and gender that is commonly observed. However, the results are not always 100% accurate due to variations in name usage and individual preferences. Detailed information can be found in Table 3.

Table 3. Gender Detection Result

1 sifananda female 2 Aa_AcepCepi male

After gender detection was performed, the gender attribute was added to Table 1. Initially, the gender attribute was not available during the data crawling process. However, after the gender detection was conducted, the gender attribute was identified and considered important in understanding the differences in perspectives between men and women regarding the use of electric vehicles. As a result, the gender attribute was added to Table 1 after the gender detection process was completed. By including the gender attribute, this research can identify specific trends and patterns for each gender group. For example, the research can determine whether men are more inclined towards the technical aspects and innovation of electric vehicles, while women are more interested in using vehicles that consider environmental impact and sustainability.

D. Case Folding

By performing case folding, all letter characters in the tweet text will be converted to lowercase without altering the meaning or sentiment contained within[17]. This helps to reduce unnecessary variations in letter casing in the tweet data and facilitates more consistent and accurate sentiment analysis, thus aiding the subsequent steps in the analysis, such as tokenization. The results of case folding can be seen in Table 2.

E. Tokenizing

The tokenizing process involves removing irrelevant characters such as spaces, punctuation marks, and special characters[18]. Next, the text is broken down into tokens based on spaces or predefined word separation rules. The result is a sequence of words that can be considered as basic units for further analysis.

Detailed information can be found in Table 4.

Table 4. Tokenizing Result

1

['setuju', 'sih', 'klo', 'ada', 'kendaraan', 'listrik', 'asalkan', 'fasilitasnya', 'menunjang', 'kayak', 'misal', 'stasiun', 'pengisian', 'daya', 'yg', 'cukul', 'mudah']

F. Stopwords Removal

Stopwords are commonly used and frequently occurring words in text that have little contribution to the meaning or important information in the analysis[19]. Stopwords can include words such as "and," "or," "that," "from," "to,"

and similar ones. By removing stopwords, researchers can focus on more informative and relevant words in identifying sentiment patterns related to the use of electric vehicles in Indonesia[20]. Detailed information can be found in Table 5.

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Table 5. Stopwords Result

1

['setuju', 'sih', 'klo', 'kendaraan', 'listrik', 'asalkan', 'fasilitasnya', 'menunjang', 'kayak', 'misal', 'stasiun', 'pengisian', 'daya', 'yg', 'cukul', 'mudah']

G. Stemming

Stemming is used to transform words into their base or root form. For example, words like "membaca" (reading), "membacaan" (reading material), and

"membaca-membaca" (reading repeatedly) will be transformed into the base form

"baca" (read)[21]. By applying stemming, researchers can simplify the word representation in the collected tweet text. Detailed information can be found in Table 6.

Table 6. Stemming Result

1

['tuju', 'sih', 'klo', 'kendara', 'listrik', 'asal', 'fasilitas', 'tunjang', 'kayak', 'misal', 'stasiun', 'isi', 'daya', 'yg', 'cukul', 'mudah']

3. Translated Text

Translated text refers to the process of translating the text with the aim of optimizing the polarity or sentiment results, enabling a better understanding of the views and perceptions of the community regarding the use of electric vehicles in Indonesia[21]. Detailed information can be found in Table 7.

Table 7. Translated Text Result

1

agree if there is an electric vehicle as long as the facilities support for example charging station that is quite easy

4. Polarity Intensity

By performing polarity intensity on the translated text, researchers can assign scores or values that reflect the level of positive, negative, or neutral sentiment in the text.

𝑃𝑜𝑙(𝑚_𝑖) = ∑∗

𝑘

𝑗=1

𝑠𝑐𝑜𝑟𝑒(𝑡𝑒𝑟𝑚_𝑗) ∗ 𝑤_{𝑝𝑜𝑠(𝑡𝑒𝑟𝑚}_𝑗₎

|𝑚_𝑖|

In the formula, there is a weighting factor (wpos(termj)) that depends on the part of speech (termj) of the word in the tweet. If the word falls into the categories of adverbs, verbs, or adjectives, the weighting factor (wpos(termj)) will be assigned a value greater than 1. However, if the word does not fall into those categories, the weighting factor (wpos(termj)) will be assigned a value of 1[22]. Detailed information can be found in Table 8.

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Table 8. Polarity Intensity Result

1 agree if there is an electric vehicle as long as the facilities

support for example charging station that is quite easy 0.8122 2 late to pay for electricity unplug it late to pay lot unplug

it late to pay spp diploma late madang catches cold late to fill up the gasoline the injection machine is broken late to pay vehicle tax fine late for work sim scorched gawe low installments installments are chased by the late

-0.7184

5. Labeling Data

By labeling the data based on the polarity intensity results, researchers can categorize the data into different sentiment categories, such as positive, negative, or neutral[23]. Information can be found in Table 8.

Table 9. Labeling Result

1 0.8122 positive 2 -0.7184 negative 3 0.0000 neutral

6. Modeling

In this stage, the processed data will be used to train a model using the Support Vector Machine (SVM) method with a hyperplane. SVM creates a hyperplane (e.g., a line or surface) to separate data between different classes. The goal is to find an optimal hyperplane that has the maximum margin between different classes in the data[24]. This margin represents the distance between the hyperplane and the closest data points from each class. By maximizing the margin, the model can reduce classification errors and have good ability to classify new data. This process will help analyze the perspectives and attitudes of the community towards the use of electric vehicles in Indonesia.

In the SVM method with a hyperplane, we search for a line or surface that can separate two groups of data. This line or surface is expressed in the form of w · x + b = 0, where w is the weight vector, x is the input feature vector, and b is the bias[25]. Classification is done using the decision function f(x) = sign(w · x + b), which yields a value of -1 if w · x + b < 0 and a value of 1 if w · x + b ≥ 0. The main objective is to maximize the margin, which is the distance between the line or surface and the closest data points from both groups. The penalty parameter C is used to control the trade-off between the maximum margin and the allowed number of classification errors.

7. Model Evaluation

The model evaluation process involves the use of relevant evaluation metrics to measure the performance of the model[26]. For example, commonly used evaluation metrics include accuracy, precision, recall, and F1-score. Accuracy measures how well the model can correctly classify sentiments. Precision measures how well the positive results classified by the model are actually correct, while recall measures how well the model can identify and capture all the true positive

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results. The F1-score is a combined measure of precision and recall. Detailed information can be found in Table 10.

Table 10. Confusion Matrix

Positive Negative Neutral

Positive TP FN FP

Negative FP TN FN

Neutral FN FP TN

𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 = 𝑇𝑃 + 𝑇𝑁 𝐴𝑚𝑜𝑢𝑛𝑡 𝑜𝑓 𝑑𝑎𝑡𝑎

𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 = 𝑇𝑃 𝑇𝑃 + 𝐹𝑃

𝑅𝑒𝑐𝑎𝑙𝑙 = 𝑇𝑃 𝑇𝑃 + 𝐹𝑁

𝐹1 − 𝑠𝑐𝑜𝑟𝑒 =2 ∗ (𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 ∗ 𝑅𝑒𝑐𝑎𝑙𝑙) 𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 + 𝑅𝑒𝑐𝑎𝑙𝑙 C. Result and Discussion

In this study, data from Twitter was collected using the crawling method with the "snscrape" library. The total number of collected data was more than 10,000.

The data then went through the pre-processing stage by selecting the required data samples. In the filtering process, the attributes "datetime" and "username" from Table 1 were used. Subsequently, the "username" attribute will be used as a replacement for the "gender" attribute, and the results will be displayed in Table 3.

The purpose of this replacement is to ensure the privacy of each user expressing their opinions on social media. Additionally, the "datetime" attribute is used to visualize the data, indicating the time when a tweet was posted. Further detailed information can be found in Table 11.

Table 11. Pre-processing Result and Labeling

location gender text_clean clean_translated sentiment Makassar male motor listrik diprediksi

booming tahun diprediksi menjadi momentum tepat untuk indonesia beralih menuju penggunaan kendaraan ramah lingkungan

belectric motorbikes are predicted to boom in predicted to be the right momentum for indonesia to switch towards using environmentally friendly vehicles

positive

Bekasi male saya juga sabar pak sembako pada naik listrik naik bbm naik pajak kendaraan selangit sabar saya

im also patient with basic food supplies for electricity fuel and vehicle taxes im as patient as am

neutral

Jakarta female perputaran ekonomi bisa hancur karna masalah ini listrik terjadi di jaman nya ga ada listrik ga ada air

the economic cycle can be destroyed because of this problem electricity occurred in that era there

negative

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pompa air ga ada listrik kendaraan listrik berhenti ga ada listrik gda kompor listrik gak nyala ga ada masak kelaperann akhirnya buka peralatan camping dan survival

was no electricity no water pump no electricity electric vehicles stopped

In Table 11, there are differences in opinions and perspectives among social media users regarding the use of electric vehicles. This indicates that there is still variation in perceptions and opinions towards electric vehicles. These differences can be attributed to factors such as knowledge, preferences, and individual experiences.

Table 12. Gender Tweet Amount

No Gender Sentiment Tweet Amount

1 male positive 646

negative 159

neutral 492

2 female positive 3013

negative 666

neutral 2044

In Table 12, there are differences in the number of tweets related to gender and sentiment towards electric vehicles. From the data, it can be concluded that female users are more active in expressing sentiment towards electric vehicles on social media compared to male users. This indicates that the role and contribution of female users in voicing their opinions and perspectives regarding electric vehicles on social media are crucial.

Figure 2. Gender Amount

Through the visualization in Figure 2, it can be observed that the number of female users is higher compared to male users. This indicates that females play a significant role and contribute significantly in expressing opinions and perspectives regarding the use of electric vehicles in Indonesia.

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Figure 3. Sentimen Polarity

Through the visualization in Figure 3, it can be observed that sentiments related to the use of electric vehicles on social media have varying proportions.

Positive sentiment covers the majority with a percentage of 51.3%, followed by neutral sentiment with 37.4%, and a lower proportion of negative sentiment, which is 11.2%. This indicates that the majority of social media users have a positive outlook on the use of electric vehicles, while a small portion holds negative views.

Figure 4. User Location

Through the visualization in Figure 4, It can be seen that there are 10 locations that contribute the most sentiments related to electric vehicles. Indonesia is the location with the highest number of users, totaling 3,305. DKI Jakarta is in second position with 2,614 users, followed by Makassar with 750 users, Tangerang with 280 users, and Karawang with 219 users. Furthermore, Yogyakarta has 115 users, while West Java has 60 users. Surabaya, Palembang, and Bandung each have 40, 38, and 30 users, respectively.

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Figure 5. Number of Tweets per Month and per Day

Through the visualization in Figure 5, it can be observed that there was a surge in social media user activity in providing sentiments related to electric vehicles in Indonesia in September 2019. During that month, the number of tweets with positive, negative, and neutral sentiments about electric vehicles reached its peak.

This indicates a high level of interest and attention from social media users towards electric vehicles during that period. One of the reasons could be the increasing awareness of environmental and sustainability issues, which has driven their interest in electric vehicles as an eco-friendly alternative. Additionally, factors such as product promotions or events related to electric vehicles, technological advancements, and changes in government policies can also influence the quantity and intensity of sentiments expressed by users on social media.

Figure 6. Male and Female Word Cloud

Through the word cloud visualization in Figure 6, it can be observed that the phrase "kendaraan listrik" (electric vehicles) is the most frequently used term in the provided sentiments. The word clouds divided by gender, male and female, show that both male and female users have a similarity in their usage of this term. This indicates that social media users consistently discuss and pay attention to electric vehicles in the context of the expressed sentiments. In addition, the word cloud analysis for the "male" gender category reveals a dominance of words related to

"vehicle battery," "vehicle development," and "vehicle programs." On the other hand, the word cloud for the "female" gender category shows a dominance of words related to "vehicle usage," "vehicle taxes," and "environmentally friendly." These findings indicate differences in focus and interests between men and women in the context of electric vehicle usage. Men tend to be more interested in technical aspects such as vehicle batteries, vehicle development, and related programs. Meanwhile, women tend to focus more on vehicle usage, including vehicle taxes, and environmental sustainability. These findings suggest a significant interest and

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attention towards electric vehicles among social media users, with distinct preferences that need to be considered in the development of inclusive marketing strategies and policies.

Table 13. Classification Model

In Table 13, the performance evaluation results of the SVM algorithm in classifying sentiments related to electric vehicles are presented. There are three sentiment categories: positive, negative, and neutral. The SVM algorithm achieves a high accuracy rate of 91% in classifying positive sentiments. However, for negative sentiments, the SVM algorithm has a lower accuracy rate with a precision of 69%.

Neutral sentiments also have a good accuracy rate with a precision of 88%. The SVM algorithm performs well in detecting positive and neutral sentiments with high recall rates of 90% and 94% respectively. However, the recall for negative sentiments is 55%, indicating a lower ability to detect negative sentiments. The F1 score, which combines precision and recall, shows a good balance between precision and recognition of positive and neutral sentiments with a score of 91%. The accuracy of the SVM algorithm in classifying positive sentiments is 88%.

Figure 7. Confusion Matrix

Through the confusion matrix visualization in Figure 7, it can be observed that in the first row of the matrix (True label 2), there are 31 data correctly classified as label 0 (True Negatives), 66 data correctly classified as label 1 (False Positives), and 920 data correctly classified as label 2 (True Positives). In the second row of the matrix (True label 1), there are 16 data correctly classified as label 0 (False Negatives), 728 data correctly classified as label 1 (True Positives), and 34 data correctly classified as label 2 (False Positives). As for the third row of the matrix (True label 0), there are 107 data correctly classified as label 0 (True Positives), 34

No Algorithm Sentiment Precision Recal F1 Accuracy

1 SVM positive 91% 90% 91% 88%

negative 69% 55% 61%

neutral 88% 94% 91%

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data correctly classified as label 1 (False Negatives), and 53 data correctly classified as label 2 (False Positives).

D. Conclusion

Based on the research conducted on the use of electric vehicles in Indonesia through sentiment analysis based on social media, it was found that public perceptions and views on the use of electric vehicles still vary. There are positive, negative, and neutral sentiments expressed through social media. Female social media users play a significant role in voicing their opinions and views on electric vehicles. They are more active in participating in discussions and expressing sentiments related to electric vehicles on social media. The locations with the most active social media users discussing electric vehicles are Indonesia, DKI Jakarta, Makassar, Tangerang, and Karawang. The peak of social media user activity in providing sentiments related to electric vehicles occurred in September 2019. This indicates a significant increase in interest and attention to electric vehicles during that period. The SVM algorithm shows good accuracy in classifying positive and neutral sentiments related to electric vehicles. However, its performance is lower in detecting negative sentiments. There are limitations in this research, such as the lack of data on the respondents' gender and age. Therefore, future studies are expected to address these limitations to develop a better understanding of public perceptions regarding the use of electric vehicles in Indonesia.

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