Development and Application of a Big Data Analysis-Based Procedure to Identify Concerns about Renewable Energy

Abstract

To achieve carbon neutrality by 2050, Korea has been expanding its investment in renewal energy distribution and technology development. However, with this rapid expansion of renewable energy, public concern about it has grown. This study developed and used a big data analysis-based procedure to analyze the questions registered on Naver, the largest portal site in Korea, from 2008 to 2020 to identify public concern over renewable energy. The big data analysis-based procedure consisted of two steps. The first was a frequency analysis to identify the most frequently registered words. The second was to classify questions using term frequency-inverse document frequency (TF-IDF) weight and cosine similarity based on word2vec. The analysis revealed the most frequently registered words related to renewable energy, such as “solar power,” “power generation,” “energy,” and “wind power.” It also revealed the most frequently registered questions, such as those related to solar panel installation, renewable energy generation methods, and certificates. To continue expanding renewable energy, it is becoming increasingly important to understand the public’s concerns and create a method to resolve their objections to renewable energy. It is expected that the procedure in this study may provide relevant insight for the method.

Highlights

• We developed a big data analysis-based procedure to identify public concerns.
• We applied the procedure to identify public concerns about renewable energy in Korea.
• We analyzed a total of 18,321 questions about renewable energy posted on a website.
• The analyses showed that the public was most interested in solar-related matters.

1. Introduction

According to the International Energy Agency (IEA), renewable energy is useful energy collected from renewable resources, which are naturally replenished, such as solar, wind, ocean, hydropower, and geothermal resources [1]. As such, the process of obtaining energy from nature has been in the spotlight in situations where significant environmental pollution is expected.

Recently, the Republic of Korea (ROK) adopted a new energy policy to promote the use of renewable energy to generate electricity. For this reason, the ROK government has been increasing its investment in the development of power generation technologies using renewable energy and focusing on the spreading of renewable energy facilities.

As the renewable energy promotion policy is implemented, public concern over renewable energy has naturally grown. There has been much research to understand the public perception of renewable energy from various perspectives. Hagen et al. conducted a survey using Internet panels randomly selected from Canada, the United States, and Mexico to identify the public’s perception of renewable energy due to climate change [2]. Ntanos et al. conducted a survey to understand the Greek people’s perception of renewable energy sources, and they performed a one-way analysis of variance and binary logit regression to evaluate the Greek people’s willingness to pay for the expansion of renewable energy sources [3]. Rogers et al. had semi-structured interviews with residents in a rural area in the UK to determine whether they would like to participate in a renewable energy project [4]. Stoutenborough et al. surveyed the US adults using a structured questionnaire to identify their perception of various energy options for power generation [5]. Jung et al. surveyed the residents of Helsinki, Finland, to identify the factors influencing the public perception of renewable energy technology and evaluated the survey results through stochastic multicriteria acceptability analysis [6]. Kardooni et al. surveyed citizens over 20 years of age in the four regions of Peninsular Malaysia using stratified probability sampling to understand public opinion on climate change and renewable energy [7]. Anderson et al. analyzed the International Renewable Energy Association/IEA global renewable energy policy database containing the results of surveys conducted from 1974 to 2015 to understand the relationship between governmental renewable energy policies and changes in public opinion on renewable energy in European countries [8]. Ribeiro et al. suggested the public perception assessment methodology to predict the public perception of renewable energy technologies using a regression model, and demonstrated its usefulness for hydro, wind, biomass, and solar energies in Portugal [9]. Dehler-Holland et al. developed a structural topic model to perform sentiment analysis for the 6645 newspaper articles on German Renewable Energy Act [10].

With the recent development of Internet search engines, some studies have used big data analysis techniques to understand the public opinion by analyzing texts in online spaces such as social network services, Internet cafés, blogs, and Internet websites. Kim et al. proposed a word network model to analyze users’ Reddit posts to investigate the public perception of renewable energy resources [11]. Li et al. collected tweets on Twitter about fossil fuels and renewable energy, analyzed them using the Valence Aware Dictionary and sEntiment Reasoner tool to understand public perception, and compared the analysis results for the three different regions [12]. Kim et al. collected tweets about solar energy generation in the United States, conducted sentiment analysis using the robustly optimized bidirectional encoder representations from transformers pretraining approach sentiment classification model, and compared them with the states’ renewable energy policies [13]. Loureiro et al. collected tweets about climate change in the UK and Spain and used the National Research Center Canada Emotion Lexicon sentiment dictionary to evaluate public preferences regarding the various energy policies [14]. Jain et al. performed classification and sentiment analysis of the tweets containing the hashtag ‘#RenewableEnergy’. To classify the tweets, the five types of machine learning (K Nearest Neighbor, Support Vector Machine, Naïve Bayes, Adaboost, and Bagging) were applied, and the support vector machine was found to be with the highest accuracy [15].

Many studies have analyzed public perception and acceptance of renewable energy expressed on various social networking services, but few studies have directly derived the public concerns. Therefore, in this study, a big data analysis-based procedure consisting of several statistical methods was developed to analyze the questions about renewable energy registered in the knowledge-sharing service Knowledge iN of Naver, one of the largest search engines in the ROK, to identify the public concerns about renewable energy. Our analysis period was from January 2008 to December 2020. Among the questions registered on the Knowledge iN service for this period, the questions containing the keywords “solar power” or “wind power” were crawled. Two types of analysis for the questions so extracted were performed in this study. First, a frequency analysis was done to identify the words most frequently mentioned in the questions. Second, the questions were grouped by topic using word network mapping, TF-IDF weights, and cosine similarity based on word2vec. Figure 1 shows the overall process of our analysis.

This paper is structured as follows: Section 2 introduces the big data and the analysis procedure. Section 3 presents the results of our analysis. Section 4 contains discussions of the analysis results, and Section 5 presents conclusions.

2. Materials and Methods

2.1. Web Scraping

The data for our analysis were the questions registered on Knowledge iN, a knowledge-sharing service of Naver, one of the largest search engines in the ROK. Knowledge iN allows any Naver user to register and answer any registered question. Knowledge iN was chosen for our analysis because the questions for a specific field show what the public is interested in or concerned about.

To analyze the Knowledge iN questions about renewable energy, all the questions registered from January 2008 to December 2020 were extracted using R, a big data analysis language. The keywords for the extraction were “solar power” and “wind power.” The extracted questions included at least one of the two keywords in the question title or content. The two keywords were chosen because “solar power” and “wind power” account for 85% or more of the total capacity in kW of renewable energy facilities in the ROK, and they can be seen as representing the total renewable energy in the ROK [16].

Among the extracted questions, there were questions that were not about “solar power” and “wind power” though they were crawled by using the two keywords. Those questions were not about ‘solar power’ or ‘wind power’ but contained at least one of the two keywords in the answers to them. Accordingly, the questions including at least one of the two keywords only in the title and content of the question were re-extracted.

2.2. Frequency Analysis

Frequency analysis was applied to identify the words most commonly mentioned in all the questions extracted in the previous step. Before the main analysis, the extracted questions were preprocessed by removing unnecessary parts of the questions through morpheme analysis. The morpheme analyzer used was Eunjeonhannip in the R package NLP4kec [17,18]. After extracting the nouns with morpheme analysis, special characters, numbers, and meaningless words were removed. Then, a dataset was created of the words and their frequency in the entire set of questions. The words were sorted by frequency in descending order. Finally, the 100 words with the highest frequency were listed.

2.3. Classification of the Questions

After the frequency analysis, the extracted questions were grouped by topic. First, a word network map was drawn based on the TF-IDF weights to create categories for grouping the questions by topic. The central words found to be related on the map were identified and regarded as the candidate categories for grouping the questions. Finally, the degree of association between the words in each question and the central words were scored, and the candidate categories with the highest scores were selected as the final categories.

2.3.1. Selection of the Central Words

The words extracted in Section 2.2 were used to draw a word network map showing the relationship between the words. TF-IDF analysis was applied to preprocess the words using the tm package [19]. Then, the words with a weight of 0.01 or less were excluded from further analysis due to their low frequency of appearance in the questions. TF-IDF is a statistical method to determine the importance of a specific word in a document by multiplying the TF value by the IDF value [20], where TF is the occurrence frequency of a specific word in a document and IDF is inversely proportional to the occurrence frequency of a specific word in a document, so the importance of a word with low frequency should not be underestimated [21]. TF-IDF can be calculated as [22]:

$$\mathrm{tf}\left(i, j\right)\mathrm{idf}\left(i, j\right)=\mathrm{tf}\left(i, j\right)\times log\left(\frac{N}{\mathrm{df}\left(j\right)}\right)$$

(1)

where $\mathrm{tf}\left(i, j\right)\mathrm{idf}\left(i, j\right)$ is the weight of term i in document j, N is the number of documents in the data set, $\mathrm{df}\left(j\right)$ is the document frequency of term i in the data set.

Finally, a word network map was drawn with the top 100 words. The line thickness between two words indicates the frequency of the two words appearing simultaneously in a document and the degree of relevance between them. In addition, the size of the circle is expressed as the sum of the number of lines connected to each word, and is a measure of “degree centrality” that evaluates the centrality by the number of lines. Groups with five or more other words connected to each other were identified. For each word in the group, the word with many connections to other words had a larger circle size. Then, the word with the largest size was chosen as the central word that represented the group.

2.3.2. Question Classification Using TF-IDF and Cosine Similarity

The central words for the groups selected in the previous section were regarded as the categories to classify the questions. Each question was scored based on the number of words similar to the central words mentioned in the question and assigned to the category with the highest score.

Cosine similarity and TF-IDF weights were used to score the questions. Cosine similarity was calculated using a function in the R package wordVectors [23]. Cosine similarity measures the degree to which two vectors are similar using the cosine value of the angle between the vectors [24]. That is, the degree of similarity between the two words is determined from the similarity of the directions of their two vectors as a value from −1 to +1. Where the cosine similarity of two vectors in completely opposite directions is −1, and that of two vectors in the same direction is +1 [25]. Cosine similarity can be calculated as [26,27]:

$$\mathrm{Cosine}\left(\stackrel{\rightharpoonup }{X},\stackrel{\rightharpoonup }{Y}\right)=\mathrm{Cosine}\left(\theta \right)=\frac{{\sum }_{i=1}^N{X}_i\times Y_i}{\sqrt{{\sum }_{i=1}^N{X}_i^2}\sqrt{{\sum }_{i=1}^N{Y}_i^2}}$$

(2)

where $\stackrel{\rightharpoonup }{X}$ and $\stackrel{\rightharpoonup }{Y}$ are N dimension vectors, and $\theta$ is the angle between X and Y.

To calculate the cosine similarity, a vectorized dataset is required. For this, word2vec was used [23], a tool to vectorize the words of a text to represent the semantic relationship between the words [28]. The number of dimensions of the word2vec training data was set to 100, and its window was set to 5. The window is the number of neighboring words referenced to predict the central word, with the degree of association of the two words used to calculate the distance between them [23].

Finally, each category was scored using the TF-IDF weights and the cosine similarity value obtained in the previous sections. The scoring was conducted as follows. A TF-IDF weight was given to the words similar to each category. The more often the words with high relevance to the category were mentioned in the questions, the higher the score became. Accordingly, the category with the highest score became the category that best represented all question categories.

A part of the computer code for the previous analyses is shown in

Table 1. Variable name and function of the code used for analysis.

Variable Name	Function
cosine_similarity	cosineSimilarity(wtv_model, wtv_model)
weight	cosine_similarity[row.names(cosine_similarity)%in%keyword,]
corp	readRDS(“corpus.RDS”)
tfidf	TermDocumentMatrix(corp, control=list(wordLenghths=c(2,Inf)))
weighing	function(x) weightTfIdf(x, normalize=TRUE)
tfidf	removeSparseTerms(tfidf, sparse=0.99)
tfidf_mat	as.matrix(tfidf)
score	weight%*%tfidf_mat
score	as.data.frame(score)
score	t(score)
textData_df	cbind(textData, score)

.

3. Results

3.1. Web Scraping

After searching for the questions posted on Knowledge iN using the keywords “solar power” and “wind power,” 101,042 questions were extracted, with an average of 7772 per year. Only those extracted questions that contained at least one keyword in both their title and content were screened for further analysis, resulting in 18,321 questions, with an average of 1409 per year. Figure 2 shows the annual number of questions about renewable energy used for our analysis. It also shows that the number of questions per year is gradually increasing. The year with the fewest questions was 2009 at 669. The year with the most questions was 2020, at 3380. This increase in the number of questions can be seen as an increase in public interest in renewable energy.

3.2. Frequency Analysis

Frequency analysis was applied to find the most frequently mentioned words in all the questions about renewable energy posted on Knowledge iN. The top 100 frequently mentioned words are listed in

Table 2. List of the top 100 words most frequently appearing in questions.

Number	Word	Frequency	Number	Word	Frequency
1	solar power	27,599	51	machine	1432
2	Generation	11,546	52	inverter	1388
3	Energy	11,493	53	product	1378
4	Electricity	9216	54	cell	1365
5	Installation	8926	55	reason	1350
6	wind power	7891	56	occurrence	1322
7	Use	5800	57	content	1296
8	Degree	5540	58	assumption	1295
9	Possible	4964	59	process	1292
10	Generator	4317	60	engineering	1291
11	Business	4158	61	equipment	1286
12	Request	3887	62	contract	1283
13	power plant	3545	63	voltage	1280
14	Thought	3536	64	electron	1274
15	Sun	3183	65	part	1261
16	Utilization	2992	66	university	1255
17	Charge	2856	67	certificate	1249
18	Method	2818	68	education	1221
19	College	2797	69	Changwon	1216
20	Sunlight	2655	70	principle	1189
21	Engineer	2577	71	facility	1184
22	Industry	2547	72	picture	1179
23	Need	2513	73	major	1156
24	Problem	2481	74	earth	1141
25	Time	2403	75	alternating current	1139
26	Battery	2291	76	design	1139
27	Production	2159	77	recommendation	1133
28	Cost	2039	78	nuclear power	1125
29	Panel	2011	79	general	1117
30	Person	2005	80	hydropower	1114
31	Explanation	1995	81	KEPCO	1113
32	Company	1969	82	study	1109
33	Technique	1943	83	Korea	1103
34	Enterprise	1912	84	corporation	1058
35	development	1871	85	building	1052
36	Case	1871	86	science	1046
37	Connection	1819	87	information	1034
38	Facilities	1799	88	capacity	1020
39	Over	1713	89	government	1018
40	electric power	1710	90	condition	1013
41	Nation	1684	91	research	1010
42	Vehicle	1677	92	permission	1005
43	Field	1663	93	price	994
44	Region	1660	94	exam	994
45	Environment	1620	95	resources	965
46	Module	1589	96	wind	952
47	House	1581	97	world	932
48	Support	1563	98	system	918
49	Efficiency	1492	99	way	917
50	Construction	1483	100	building	898

. As shown in the table, words related to power generation by renewable energy such as “solar power,” “power generation,” “energy,” and “wind power” appeared most often, followed by those related to “electricity,” “battery,” and “use.” In addition, words related to employment, such as “university,” “certificate,” “study,” “major,” “exam,” and “engineer,” appeared frequently. Words related to other energy resources, such as “nuclear power” and “hydropower,” were ranked in the top 100.

3.3. Classification of the Questions

3.3.1. Word Network Map

A word network map, which visually represents the relationship between the words, was drawn to select the categories to classify the questions. TF-IDF weight analysis was conducted to collect data for the word network map, gathering a total of 387 significant words. Using these significant words, a data frame was created to show the TF-IDF weights between pairs of words.

Table 3. Section of data frame showing TF-IDF weight values between pairs of words.

Word	Development	Month	Individual	Distance	Worry	Building	Build	Architecture	Search	Winter	Result
develop-ment	1.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
month	0.000	1.000	0.015	0.000	0.039	0.000	0.000	0.000	0.000	0.000	0.000
individual	0.000	0.015	1.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
distance	0.000	0.000	0.000	1.000	0.012	0.016	0.000	0.000	0.000	0.000	0.000
worry	0.000	0.039	0.000	0.012	1.000	0.000	0.000	0.012	0.000	0.027	0.000
building	0.000	0.000	0.000	0.016	0.000	1.000	0.000	0.040	0.000	0.000	0.000
build	0.000	0.000	0.000	0.000	0.000	0.000	1.000	0.012	0.000	0.000	0.000
architecture	0.000	0.000	0.000	0.000	0.012	0.040	0.012	1.000	0.000	0.000	0.000
search	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	1.000	0.000	0.054
winter	0.000	0.000	0.000	0.000	0.027	0.000	0.000	0.000	0.000	1.000	0.000
result	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.054	0.000	1.000

shows a section of the data frame with the TF-IDF weights between the pairs of words. Only the pairs with a weight greater than 0.01 are shown.

Figure 3 is a word network map consisting of the 100 words whose weights were in the top 100 among the word pairs in Table 3. In the figure, two words related to each other are connected by lines whose color intensity depends on the degree of association between the words. Words connected by a dark line are closely related, indicating a high degree of association, whereas a lighter line indicates a lower degree of association. Words not connected by lines are unrelated. The size of the circle means “degree centrality” and is expressed as the sum of the number of all lines connected to a word. In other words, the more lines connected to the word, the larger size of the circle is expressed.

Finally, in Figure 3, words connected by lines to five or more other words were grouped. Among the words in these groups, the central word was selected to represent each group, as summarized in

Table 4. List of relevant words and central words by group.

Group	Relevant Words	Central Word
1	Energy, geothermal power, bio, ocean, solar power, difference, distinction, wind power, wing, generator, new and renewable, renewable, hydrogen, nuclear power, thermal power, tidal power, hydropower, coal, petroleum, gas, city, boiler	Energy
2	Installation, sunlight, cost, rooftop, building, house, detached house	Installation
3	Graduation, university, major, engineering, electron, movement	University
4	Engineer, certificate, facilities, industry, qualification, career, task, acquisition, certified technician, exam	Engineer
5	Battery, charger, charge, controller, connection, assistance	Battery
6	Voltage, current, measurement, maximum, output	Voltage

. A total of six groups were identified with the central words “energy,” “installation,” “university,” “engineer,” “battery,” and “voltage.”

3.3.2. Question Classification

To classify the questions by the previously selected categories, the cosine similarity values between the six central words and 11584 total words were first calculated.

Table 5. Section of the matrix showing cosine similarity values between central words and the relevant words.

Word	Energy	Installation	University	Engineer	Battery	Voltage
solar power	0.358	0.608	0.175	0.278	0.407	0.312
generation	0.593	0.521	0.145	0.31	0.228	0.255
energy	1	0.279	0.226	0.279	0.225	0.17
electricity	0.426	0.466	0.188	0.351	0.33	0.299
installation	0.279	1	0.149	0.269	0.358	0.253
wind power	0.542	0.314	0.132	0.207	0.238	0.269
use	0.353	0.514	0.046	0.196	0.552	0.478
degree	0.264	0.521	0.085	0.17	0.346	0.309
possible	0.256	0.358	0.233	0.28	0.272	0.191
generator	0.387	0.468	0.07	0.155	0.335	0.337

shows a section of the cosine similarity matrix.

Using the TF-IDF weight values of all the words and the cosine similarity values between the words and the central words, each question was scored on relevance to each category.

Table 6. Selection of question scores for each category.

Category	Energy	Installation	University	Engineer	Battery	Voltage	Final Category	Max Score
1	1.606	0.982	0.357	0.636	0.665	0.638	Energy	1.606
2	1.499	0.875	0.419	0.685	0.604	0.562	Energy	1.499
3	0.652	0.958	0.319	0.566	0.559	0.516	Installation	0.958
4	0.733	0.542	0.264	0.374	0.506	0.467	Energy	0.733
5	0.847	0.911	0.359	0.462	1.103	0.976	Battery	1.103
6	0.606	0.541	0.433	0.552	0.338	0.277	Energy	0.606
7	0.558	1.103	0.228	0.463	1.335	1.061	Battery	1.335
8	0.257	0.257	0.105	0.182	0.310	0.278	Battery	0.310
9	1.063	1.030	0.297	0.553	0.743	0.742	Energy	1.063
10	0.768	0.421	0.381	0.387	0.328	0.356	Energy	0.768
11	1.836	1.351	0.386	0.744	0.746	0.407	Energy	1.836
12	1.995	1.182	0.547	0.852	1.017	0.714	Energy	1.995
13	0.452	0.532	0.263	0.339	0.248	0.256	Installation	0.532
14	2.740	1.067	0.533	0.994	0.900	0.702	Energy	2.740
15	0.553	0.568	0.236	0.396	0.374	0.365	Installation	0.568

shows a section of the question scores for each category.

Finally, each question was classified to the category with the highest score among all the categories. The classification results are summarized in

Table 7. Results of classification of questions by category.

Category	Number	Mean Score
Energy	5690	1.11
Installation	8598	0.97
University	160	0.85
Engineer	993	1.27
Battery	1978	1.20
Voltage	902	1.22

. The mean score in the third column is the average score of all questions classified to each category. Table 7 shows that the number of questions pertaining to the “installation” group was the highest, at 8598. Most questions in the “installation” group were about installing solar power facilities, installation location, and cost. The number of questions pertaining to the “energy” group was 5690, with the majority about wind power, hydropower, and nuclear power and the different power generation methods for each energy resource. Furthermore, there were 1978 questions in the “battery” group, 993 questions in the “engineer” group, 902 questions in the “voltage” group, and 160 questions in the “university” group.

In our analysis, a total of 18,321 questions were classified by category. The reason all the questions were classified is because the two keywords “solar power” and “wind power” are included in all the questions, and the related words of the group set as categories are also included.

Next, the questions with the highest score in each category were extracted.

Table 8. List of questions with the highest score for each category.

Category	Question	Max Score
Energy	Please tell me the characteristics and pros and cons of each form of energy generation, such as nuclear power, hydropower, tidal power, thermal power, and wind power.	2.75
Installation	I am curious about the construction cost of solar panels for a detached house.	2.63
University	Which universities and majors in the Republic of Korea study new and renewable energy?	2.03
Engineer	I want to become an expert in solar power, and I would like to obtain a certification as a solar power industry technician. What is the job market after getting a certificate?	2.92
Battery	The solar controller has a charging voltage of 12 V and a current of 10 A. I am trying to charge a battery with a 100 W, 12 V panel, but the current is about 8 A. Will it be charged?	3.27
Voltage	In the solar circuit, the output voltage is normal, but the maximum output current is weak. Please tell me some simple things you can do to increase the current output.	3.55

shows that the questions contained a number of the central words and the relevant words are summarized in Table 4.

The highest score for the “energy” category was 2.75, and the question with the highest score in this category was about the “characteristics, pros, and cons of different renewable energy resources.” The highest score for the “installation” category was 2.63, with the highest-scoring question about the “construction cost of individual solar panels.” The highest score for the “university” category was 2.03, with the highest-scoring question about “majors and domestic universities related to renewable energy.” The highest score for the “engineer” category was 2.92, and the question with the highest score was about the “acquisition of solar-related certificates and prospect of employment in this field.” The highest score for the “battery” category was 3.27, with the question with the highest score about “charging of solar power controller.” The highest score for the “voltage” category was 3.55, with the highest-scoring question about “electrical knowledge such as voltage and current.”

The original questions in Table 8 were written in Korean, and the structure and word order were slightly changed in translating them into English to convey the meaning more effectively.

4. Discussion

In the ROK, the current energy policy emphasizes the expansion of renewable energy to respond to the climate crisis. Thus, the current government is significantly expanding its investment in the expansion of renewable energy. As many articles about renewable energy are pouring in every day through various media, the public is naturally interested in renewable energy and expresses their opinions in various ways.

With the development of online media, many people are free to express their opinions by posting comments on Internet articles. In addition, on a specific website where knowledge can be shared, many users are free to ask and answer questions to address each other’s curiosity. There have been studies to analyze texts posted on social network services (SNS) such as Twitter and Reddit. Such studies included the analysis results of the regional perception of renewable energy [12], the regional perception of solar energy [13], and the difference in perception between the two countries on climate change [14]. These studies identified the emotional expressions SNS users wanted to share through SNS, but could not figure out what they were specifically curious about. Therefore, if the questions SNS users asked online and their answers are carefully analyzed, it is possible to identify the public’s interest and concerns specifically.

In this study, therefore, questions posted on the section of Knowledge iN in the portal site Naver were analyzed using R, a big data analysis language, to determine what the public is interested in regarding renewable energy. First, frequency analysis was done and found that words related to power generation by renewable energy appeared most often, followed by words related to charging, the use of renewable energy, electricity, employment, university, and other energy resources. Then, what the public was most interested in about renewable energy was found to be the use and principles of renewable energy and power generation by renewable energy. In addition, with the expansion of renewable energy in the ROK, the public interest in jobs in the renewable energy field, such as workplaces, employment, and certificates, has also increased.

Next, the extracted questions were classified by category on a specific topic. For this, a word network map was drawn to identify groups of words with high relevance, and then six categories were selected: “energy,” “installation,” “university,” “engineer,” “battery,” and “voltage.” Furthermore, the TF-IDF weight value and the word2vec-based cosine similarity were applied to assign a score according to how many related words in each category the questions contained. Finally, the categories with the highest scores were determined. Consequently, the most questions were found in the following categories: “installation,” “energy,” “battery,” “engineer,” “voltage,” and “university.”

Moreover, the question that received the highest score in each category was chosen. Related words were identified through the word network map, and the topics of the questions were also closely related to these words. As a result, 8598 questions were classified into the installation group that had the most questions. Next, the questions were sorted in the order of energy (5690), battery (1978), article (993), voltage (902), and university (160).

This analysis confirmed that the public was most interested in solar panel installation and its installation cost. In addition, people were interested in the characteristics and pros and cons of power generation by other renewable energy resources, as well as professions, including universities and majors related to renewable energy, and exams for certification. There were many questions about electrical knowledge, such as batteries, charging, voltage, and current.

Based on the analysis results, implementation strategies for renewable energy policy can be formulated to meet the needs of the public. For example, after confirming many questions related to solar panel installation, strategies such as developing detailed manuals for solar panel installation and subsidies for installation costs could be considered. It is also possible to develop such strategies as Internet articles or card news to introduce the characteristics and pros and cons of power generation using renewable energy resources. Furthermore, brochures to introduce renewable energy-related majors and universities can be produced.

At a moment when renewable energy has emerged as the biggest topic in the Korean energy industry, increasingly more questions and opinions are expected to come out. Accordingly, analysis should continue to accurately identify the public interest and concern and increase public acceptance of renewable energy. If areas of interest to the public are accurately identified and contents produced containing the answers to the public’s questions, mutual trust between government, the energy industry, and the public will naturally increase. In other words, grasping the public opinion as it changes over time and establishing an appropriate strategy accordingly will lead to a friendly environment for renewable energy as well. In this regard, our analysis methodology could be used as a tool to derive the basic data for formulating a plan or strategy for promoting renewable energy.

5. Conclusions

This study developed a big data analysis-based procedure consisting of several types of analysis to determine what the general public was most interested in regarding renewable energy. We applied TF-IDF, cosine similarity, and word2vec to identify topics in informal texts and to classify them into categories. In addition, a word network map that visually represents the relationship between words was presented. Therefore, the methodology presented in this paper could be used as analysis tool to derive the basic data for formulating a plan or strategy for promoting renewable energy.

As time goes by, more and more social network services and mass media are being created, and the user age is being diverse. In addition, it is important to select the appropriate social media most used by citizens of each country. Therefore, subsequent study will expand the research scope to the other social network services such as Instagram, Facebook, Twitter, and Blog.