Syntax
Literate: Jurnal Ilmiah
Indonesia p�ISSN: 2541-0849 e-ISSN: 2548-1398
Vol.
7, No. 12,
December 2022
OPTIMIZING
CONTENT MARKETING USING AUTOMATIC KEYWORD EXTRACTION TO GET TOPIC PREDICTION
Savitri Indriyani
Swiss
German University, Indonesia
Email:
[email protected]
Abstract
Digital news with a variety topics is abundant
on the internet. The problem is to classify news based on its appropriate
category to facilitate user to find relevant news rapidly. The manual
categorization of text documents requires a lot of financial and human
resources to do the process. In order to get so, topic modeling usually used to
classify documents. In the used topic models (LSA, LDA) each word in the corpus
of vocabulary is connected with one or more topics with a probability, as
estimated by the model. Many (LDA, LSA) models were built with different values
of coherence and pick the one that produces the highest coherence value. Based
on the result, we summarized some points, three models above can answer the
question in Research Question, those models can be
applied in the future to company�s automation prosess
of determining topic automatically. LDA using BOW and LSA using BOW would be
priority option to be applied.
Keywords: Topic Modelling, LDA, LSA, Bag of Words, TF-IDF.
Introduction
The importance of digital marketing has increased from time to time as
part of a marketing strategy that is now increasingly being practiced by any
organization, including startup company. Therefore, a digital marketing
strategy cannot be successful without having a quality content marketing.
Many companies are interested to increase the use of the content
marketing tool in their marketing policy, as they notice the limitations of the
traditional marketing communication strategy, as well as the huge opportunities
brought by digital marketing (Chen et al., 2017).
According to the statistics provided by the Content Marketing Institute,
the importance of content marketing is growing, as 70% of B2B marketers are
creating more content than they did one year ago (Vinodhini &
Chandrasekaran, 2012) and (Liu, 2012). This is explained by the fact that �60% of B2B
decision makers say branded content helps them make better purchase decisions,
while 61% of consumers are more likely to buy from companies that offer custom
content� .Content itself must be relevant to your audience and create a
powerful brand image, this become important because company need to win
customer�s trust and royalty (Oghaz et al., 2020). The importance of� valuable content is that company can
build interest that transforms into lasting relationships. Content marketing
can be transformed in many ways, for examples Infographics, webpages, podcast,
etc. Company can choose the media to publish the content such as Microblogs
like Twitter, social platforms like Facebook, or forums like LinkedIn
Discussions or facebook forums (Payak et al., 2020).
As a company that focuses on developing technology in agriculture, Biops Agrotekno Indonesia offers
the concept of precision farming (precision farming) (Thomas et al., 2016), which is a measurable agricultural concept that is
able to adjust the supply of water and nutrients according to plant needs that
can be done automatically and can be monitored in real time using applications
on smartphones (R�der et al., 2015).
Methods
The experiment was
performed on a dataset consisting news article from selected e-newspaper.� News article datasets originating from kompas.com and detik.com
which is some of the digital news site in Indonesian language which are sought
after news seekers (Loza et al., 2014). Data taken from the website are news published in
December 2021 and early 2022.
The data are collected
from selected article e-newspaper using its full content. Data collected using
web scraping method, and in this experiment Python was selected as a device for
executing web scraping. The dataset only consist the content of the articles. In
the text processing, the steps consist of these following steps : tokenizing,
remove punctuation, stopwords, and stemming (Onan et al., 2016).
Results and Discussion
A. Experiment
and Data Analysis
Corpus, dictionary, and a number of topics are needed to train the (LDA
and LSA) model, where each word in the corpus of vocabulary is then connected
with one or more topics with a probability which estimated by the model. LDA
and LSA model was built with various topics where each topic is a mixture of
keywords and each keyword contributes a certain weight to the topic (Keneshloo et al.,
2016).
The topic modeling experiment was the phase which carried out to form the
best topic model by conducting experiments on input parameters.
Before obtaining the topic modeling result, we have to specify the number
of topics. In this experiments, the best number of topics in each method was
obtained by calculating the coherence score with the range between 5 to 10
topics. The highest coherence score indicates the appropriate number of topics (Onan et al., 2016).
The size of each words presented the importance of the words in a
collection of texts. In addition, we determined the topic label based on the
word presented.
1.
Topic Modelling using LDA and Bag
of Words as Word Vectorizer
In our experiment, we tested coherence score by applying a different
number of topics t, t = 5,6,7,8,9,10 topics. Coherent score evaluation of topic
modelling performance using LDA and Bag of Words as Word Vectorizer
as shown by Figure 15 and the result of coherence score for each number of
topics as shown by the Table 1.
Figure 1. Coherent score of
Topic Modelling using LDA and Bag of Words
Output
of Coherent score of Topic Modelling using LDA and Bag of Words
|
Num Topics |
Coherent Value |
|
5 |
0.291444 |
|
6 |
0.326842 |
|
7 |
0.325318 |
|
8 |
0.309921 |
|
9 |
0.34935 |
|
10 |
0.347481 |
From the result of Table 4, num topics = 9
giving the highest score of Coherent score among other num
topics (Lee & Kim, 2008). Bon the result of coherent score, Topic Modelling
using LDA and Bag of Words simulated using number of topic = 9, which obtain
following topics as shown by Table 5. For each word value embedded to keyword,
we summarize the absolute value to Column Score. Result on the Table 2 sorted
descending by the score (Pilato &
Vassallo, 2014).
Topics
generated by LDA Topic modelling (BOW)
|
Opic |
Core |
Keyword + Score |
|
Topic: 6 |
0.140 |
0.030*"makmur"
+ 0.029*"program" + 0.012*"erick"
+ 0.012*"indonesia" + 0.010*"tembakau" + 0.010*"tingkat"
+ 0.010*"bumn" + 0.010*"thohir" + 0.009*"tanam"
+ 0.008*"pupuk" |
|
Topic: 3 |
0.132 |
0.023*"korban"
+ 0.018*"warga" + 0.017*"motor"
+ 0.014*"desa" + 0.013*"orang"
+ 0.010*"laku" + 0.010*"polisi" + 0.009*"rumah"
+ 0.009*"bawa" + 0.009*"kabupaten" |
|
Topic: 1 |
0.130 |
0.019*"bri" +
0.016*"umkm" + 0.016*"usaha" + 0.014*"tingkat"
+ 0.013*"erick" + 0.012*"persen" + 0.011*"jeruk"
+ 0.011*"dukung" + 0.010*"thohir" + 0.008*"tanam" |
|
Topic: 5 |
0.127 |
0.033*"pupuk"
+ 0.017*"indonesia" + 0.014*"teknologi" + 0.012*"program" +
0.011*"lahan" + 0.010*"subsidi" + 0.008*"karya"
+ 0.008*"update" + 0.007*"kembang"
+ 0.007*"hektar" |
|
Topic: 8 |
0.122 |
0.023*"hama" +
0.014*"tanam" + 0.013*"manfaat" + 0.012*"indonesia"
+ 0.012*"motor" + 0.012*"alami"
+ 0.011*"update" + 0.009*"buah"
+ 0.008*"curi" + 0.008*"rumah" |
|
Topic: 7 |
0.115 |
0.015*"harga"
+ 0.014*"pupuk" + 0.013*"pangan" + 0.013*"komoditas"
+ 0.012*"tingkat" + 0.012*"indonesia" + 0.011*"sektor"
+ 0.009*"program" + 0.008*"lahan"
+ 0.008*"sawit" |
|
Topic: 0 |
0.103 |
0.014*"tembakau"
+ 0.013*"indonesia" + 0.012*"tanah" + 0.010*"lahan"
+ 0.010*"air" + 0.010*"resap" +
0.009*"tingkat" +
0.009*"program" + 0.008*"sumur"
+ 0.008*"perintah" |
|
Topic: 2 |
0.100 |
0.017*"harga"
+ 0.016*"persen" + 0.010*"tingkat" + 0.010*"anj"
+ 0.009*"naik" + 0.008*"sawit" + 0.008*"gunung"
+ 0.008*"rumah" + 0.007*"panen" + 0.007*"curi" |
|
Topic: 4 |
0.098 |
0.015*"jokowi"
+ 0.011*"update" + 0.010*"bangun"
+ 0.010*"ekspor" + 0.009*"negara" + 0.009*"panen"
+ 0.009*"impor" + 0.009*"menteri" + 0.008*"bendung"
+ 0.008*"kabupaten" |
2. Topic
Modelling using LDA and TF-IDF as Word Vectorizer
In our experiment, we tested coherence score by
applying a different number of topics t, t = 5,6,7,8,9,10 topics. Coherent
score evaluation of topic modelling performance using LDA and TF-IDF as Word Vectorizer as shown by Figure 16 and the result of coherence
score for each number of topics as shown by the Table 2.
Figure
2. Coherent score of Topic Modelling using LDA and
TF-IDF
Output
of Coherent score of Topic Modelling using LDA and TF-IDF
|
Num Topics |
Coherent Value |
|
5 |
0. 408928 |
|
6 |
0. 451697 |
|
7 |
0. 479131 |
|
8 |
0. 449178 |
|
9 |
0. 492648 |
|
10 |
0. 473736 |
Based on the result of coherent score, Topic
Modelling using LDA and TF-IDF simulated using number of topic = 9, which
obtain following topics as shown by Table 7. For each word value embedded to
keyword, we summarize the absolute value to Column Score. Result on the Table
7� sorted descending by the score (Beliga et al., 2015) dan (Albalawi et al., 2020).
Topics generated by LDA Topic modelling (TF-IDF)
|
Topic |
Score |
Keyword + Score |
|
Topic: 8 |
0.052 |
0.009*"makmur" + 0.007*"program" + 0.006*"erick" + 0.006*"thohir"
+ 0.006*"ekspor" + 0.005*"sektor" + 0.004*"kompetisi"
+ 0.003*"bumn" + 0.003*"kopi" +
0.003*"rawan" |
|
Topic: 0 |
0.049 |
0.006*"jokowi" + 0.006*"bawang"
+ 0.006*"impor" + 0.005*"keluh" + 0.005*"telepon"
+ 0.005*"korban" + 0.004*"curi" + 0.004*"masuk"
+ 0.004*"tanggung" + 0.004*"putih" |
|
Topic: 6 |
0.048 |
0.006*"tembakau" + 0.006*"anj"
+ 0.006*"hama" + 0.005*"resap" + 0.005*"lahan"
+ 0.004*"inovasi" + 0.004*"kumbang" + 0.004*"sumur"
+ 0.004*"industri" +
0.004*"air" |
|
Topic: 1 |
0.047 |
0.007*"mosaik" + 0.005*"romawi"
+ 0.005*"vila" + 0.005*"pupuk" + 0.005*"temu"
+ 0.004*"jagung" + 0.004*"hiu" + 0.004*"subsidi"
+ 0.004*"subsektor" +
0.004*"sensor" |
|
Topic: 4 |
0.045 |
0.007*"motor" +
0.005*"mitra" + 0.005*"tembakau" + 0.004*"mata"
+ 0.004*"gawai" + 0.004*"yogyakarta" + 0.004*"curi"
+ 0.004*"probolinggo" + 0.004*"hama" + 0.004*"polisi" |
|
Topic: 3 |
0.043 |
0.006*"pupuk" + 0.005*"makmur"
+ 0.005*"padi" + 0.005*"bendung" + 0.004*"program" + 0.004*"produktivitas" + 0.004*"persen"
+ 0.004*"tingkat" + 0.003*"trenggalek" + 0.003*"harap" |
|
Topic: 2 |
0.042 |
0.006*"umkm" + 0.005*"pupuk"
+ 0.005*"bri" + 0.005*"organik" + 0.004*"persen"
+ 0.004*"anj" + 0.004*"bangun" + 0.003*"sektor"
+ 0.003*"ekspor" + 0.003*"padi" |
|
Topic: 7 |
0.040 |
0.008*"cabai" + 0.005*"pidekso"
+ 0.004*"beras" + 0.004*"panen" + 0.004*"waduk"
+ 0.003*"harga" +
0.003*"digital" + 0.003*"impor"
+ 0.003*"tanah" + 0.003*"wonogiri" |
|
Topic: 5 |
0.036 |
0.005*"pati" + 0.005*"nasi"
+ 0.005*"masak" +
0.003*"estate" + 0.003*"gula" +
0.003*"alat" + 0.003*"korban" + 0.003*"rempahrempah"
+ 0.003*"serangga" + 0.003*"rempah" |
3. Topic
Modelling using LSA and Bag of Words as Word Vectorizer
In our experiment, we tested coherence score by applying a different
number of topics t, t = 5,6,7,8,9,10 topics. Coherent score evaluation of topic
modelling performance using LSA and Bag of Words as Word Vectorizer
as shown by Figure 17 and the result of coherence score for each number of
topics as shown by the Table 5.
�
Figure 5 Coherent score of Topic Modelling using
LSA and Bag of Words
Based on the result of coherent score, Topic Modelling using LSA and Bag
of Words simulated using number of topic = 5, which obtain following topics as
shown by Table 9. For each word value embedded to keyword, we summarize the
absolute value to Column Score. Result on the Table 9 sorted descending by the
score.
4.
Topic Modelling using LSA and
TF-IDF as Word Vectorizer
In our experiment, we tested coherence score by applying a different
number of topics t, t = 5,6,7,8,9,10 topics. Coherent score evaluation of topic
modelling performance using LSA and TF-IDF as Word Vectorizer
as shown by Figure 18 and the result of coherence score for each number of
topics as shown by the Table 6.
Figure 6. Coherent score of
Topic Modelling using LSA and TF-IDF
Based on the result of coherent score, Topic Modelling using LSA and
TF-IDF simulated using number of topic = 5, which obtain following topics as
shown by Table 11. For each word value embedded to keyword, we summarize the
absolute value to Column Score. Result on the Table 11 sorted descending by the
score.
5.
Analysis of Topic Modelling Result
From Expert Analysis Team
The results from the modeling using four methods: (a) LDA and Bag of
Words as Word Vectorizer; (b) LDA and TF-IDF as Word Vectorizer; (c) LSA and Bag of Words as Word Vectorizer; (d) LSA and TF-IDF as Word Vectorizer;
have been analyzed by three people from BIOPS Agrotekno.
The people doing the analysis are the ones who are doing the SEO project of the
company. As the hashtag used in the research (#pertanian,
#petani, and #teknologi pertanian) are similar with some of the keywords that is
usually used in the company�s SEO project, the point of view could be used as
the expert reference. There are two parameters that is used to validate the
results. There are: (a) the relevance of keywords to the major topics; (b) the
coherence between words in one topic group.
Based on the result, There are some of the
keywords that are considered as irrelevant. Some of the irrelevant words are: �korban�, �motor�, �karya�, �curi�, �hiu� etc. These words are
not closely related to the groups of words the expert team expected. At first,
they thought that it could be that at the period of time where the articles are
taken, these words are actually related to the main topics. However, when they
analyzed the LSA and Bag of Words as Word Vectorizer,
the relevance rate wass high. The keywords resulted
from this method are more relevant compared to the other methods. Therefore,
they picked this method as the best method to give the most relevant results.
Nevertheless, there are still some keywords that is irrelevant resulted by this
method, yet the amount is fewer compared to others. (Note: this point of view
is only based on the scope used in the research. Change in parameters may also
change the result and the validation).
The Relevance from their opinion between the topic and the extracted
keywords as shown by Table 12, 13, 14, and 15. It represented by the color,
Green is Relevant (correlation High), Yellow is Not too relevant (correlation
Medium), and Red is Irrelevant (correlation Low).
They also analyze the coherence between words in one topic. From four
methods, they could say that again LSA and Bag of Words as Word Vectorizer method resulted the most coherence between
words. For example, the Topic 1: "pangan" +
"lahan" + "estate" + "harga" + "food" + "persen"
+ "ekspor" + "komoditas"
+ "program" + "indonesia". Even
only based on these set of words, they get a picture that the topic is around
the food estate program by Indonesian government to increase the food security
and export.
Based on the validation they already done,
they prefer the LSA and Bag of Words as Word Vectorizer
method as the best compared to the others. The conclusion is based on at least
two factors: Relevance and Coherence, combined with their knowledge in the
sectors. They believe that this modeling could help us
increasing the efficiency of our SEO projects, especially in the keywords
searching. In the future, before it can be used further, another validation
with other parameters (periods of time, topics, media sources, etc).
Conclusion
Based on the results that
got on in this research, we applied topic
modeling using Latent Dirichlet Allocation and Latent
Semantic Analysis to discover topics from selected article from e-newspaper,
which are Kompas.com and Detik.com. By applying topic modeling, we could find
several insightful topics that illustrate necessary information from the articles.
Based on the result, we
summarized some points, three models above can answer the question in Research
Question, those models can be applied in the future to company�s automation prosess of determining topic automatically. LDA using BOW
and LSA using BOW would be priority option to be applied.
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Copyright holder: Mufida�Dian Pertiwi, Fajar
Syaiful Akbar (2022) |
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First publication right: Syntax Literate: Jurnal Ilmiah Indonesia |
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