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The Response to the first reviewer's comments
1. The 1st comment:
Change the section 3 title from “The Problem” into a meaningful title that represents the section
content. For instance, “Target Market Classification”. Move the preprocessing, feature selection and
learning model development into section 3. In this section, you can start with Target Market, followed by
a series of process for target market classification. It will be good to start with the overview and then the
details of each step, including various algorithms for classifications.
Respond:
I have been doing repairs in accordance with the advice given by reviewer. Subtitles in section 3
has changed the originally "The Problem", became "Target Market Classification". The discussion in
section 3 has also been changed. In this section we discuss all things related to the target market, starting a
discussion of what the target market, a process that is carried out to classify the target market, and
discusses the algorithm used in the classification of the target market.
Modification:
Before modification
3.
The Problem
3.1 Target Market
Target market is a group of people who are targets of the marketing of a certain
product because they are considered to have relatively similar needs. By selecting
appropriate target markets a company can maintain the balance of human resources,
financial capacity, and material resources hence the right use of sources [3]. It is the
target market that serves as the basis for preparing a marketing strategy in terms of
product strategies, pricing, distribution, and promotion (marketing mix) [2].
After modification:
3
Target Market Classification
3.1 Target Market
Target market is a group of people who are targets of the marketing of a certain
product because they are considered to have relatively similar needs. By selecting
appropriate target markets a company can maintain the balance of human resources,
financial capacity, and material resources hence the right use of sources [3]. It is the
target market that serves as the basis for preparing a marketing strategy in terms of
product strategies, pricing, distribution, and promotion (marketing mix) [2].
2. The 2nd comment:
Section 4 focuses on the Evaluation. Usually starts with data, some detail experiment setup and finally
experiment results and discussion.
Respond:
Section 4 is focused to discuss the evaluation of the results. Starting from discussing the data used, to
discuss the final results of this study.
Modification:
Before modification
4
Experiment and Discussion
4.1
Problem Analysis
4.1.1 Dataset
The data used in this study are tweets derived from the followers of the account
@ptxyz. The first thing to do was to gather idusers of the followers of @ptxyz
account; the collected Idusers are 5000 id out of a total of 200,000 followers. This is
due to twitter’s policy of limiting the intake of idfollower of an account. From all of
the Ids obtained, 200 tweets were taken out of the most recently posted. Not all of
the data obtained were used as the dataset in this study. The data used in this study
were those manually labeled at the preprocessing stage.
After Modification
4.
Evaluation
4.1
Dataset
The data used in this study are tweets derived from the followers of the account
@ptxyz. The first thing to do was to gather idusers of the followers of @ptxyz
account; the collected Idusers are 5000 id out of a total of 200,000 followers. This is
due to twitter’s policy of limiting the intake of idfollower of an account. From all of
the Ids obtained, 200 tweets were taken out of the most recently posted. Not all of
the data obtained were used as the dataset in this study. The data used in this study
were those manually labeled at the preprocessing stage.
3. The 3rd comment:
revise the graphics and figures so that it is esthetically pleasant.
Respond:
Chart that I use with no color, just use patterns only with the intention that if later required doubling by
means of photocopy, then the graph will still be easily read. But indeed in terms of aesthetics become less
good. But at this time the improvement I have to change the graph becomes fullcolor.
Modification:
Before modification
0.865 0.865 0.865
0.9
0.8
0.805 0.805 0.805
0.757 0.757 0.757
0.7
Precision
CART
Recall
AdaBoostM1+CART
F-Measure
Bagging+CART
Figure 1. Precision, Recall and F-Measure in Sentiment Classification
After modification:
1
0.9
0.94 0.94
0.86 0.86 0.87
0.83 0.85 0.85
Precision
Recall
0.83
0.8
0.7
CART
AdaBoostM1+CART
F-Measure
Bagging+CART
Figure 2. Precision, Recall, and F-measure in Category Classification
4. The 4th comment:
It also still has “Bahasa Indonesia”, fix it. For precision, recall and f-measure, user 2 digits for decimal
point. For percentage value, use 1 digit for decimal point.
Respond:
Some use of Bahasa has been improved, as well as changing digit decimal numbers and percentages on
the research results.
5. The 5th comment:
Furthermore, add a few more recent relevant references (around 6 papers) from 2013 and 2014 from top
journal and prominent conferences
Respond:
For additional relating references , this time I just could add 2 paper. This is due to limited time to discuss
one by one relating reference and the difficulty of access the top journals. The paper added, namely:
1.
2.
L. Liu, Z. Yang, and Y. Benslimane, “Conducting Efficient and Cost-Effective Targeted
Marketing Using Data Mining Techniques,” 2013 Fourth Global Congress on Intelligent Systems,
pp. 102–106, Dec. 2013.
M. A. Khan and A. Khan, “A Novel Learning Method to Classify Data Streams in the Internet of
Things,” 2014 National Software Engineering Conference (NSEC), pp. 61–66, 2014.
The Response to the second reviewer's comments
1. The 1st comment:
This paper is not an original work, because there are so many papers use ensembl;e classification
method for tweets classification for examples:
a. Matthias Hagen, Martin Potthast, Micahel Buchner, Benno Stein” Twitter sentiment Detection
via Ensemble Classification using Averaged Confidence Scores” Advances in Information
Retrieval, Vol. 9022 of the seriesLecturer Notes in CS, pp. 741-754.
b. Euginio Martinezm Camara, et al.”Ensemble classifier for twitter sentiment Analysis”
c. Yun Wan, An Ensemble Sentiment Classification System of Twitter Data for Airline Services
Analysis” Master Thesis, Dalhousie University.
You have to elaborate more papers to convince us that your work has a significant contribution and
novelty. The difference between this paper and other paper is only in the data set used. This is not
significant and is not enough for the novelty of the work.
Respond :
This research clearly different from the other studies. One of the research mentioned by the reviewer
above using ensemble with Bayesian-voting technique, while the ensemble method used in this
research is by manipulating the training data, known as bagging and boosting. In addition this study
only process the data in the form of tweet Indonesian language that has its own difficulty level when
processing them in text mining. The focus of this study is to show effect given by ensemble method
in the terms of improving the accuracy of the classification.
2. The 2nd comment :
There are also many grammatical errors in this paper.
Respond :
Grammatical error has been corrected.