Text Mining, Data Mining, and Applications in Various Industries

This article discusses text mining and data mining, their differences, and the technologies and tools required for text mining. It also explores the applications of text mining in various industries such as risk management, knowledge management, cybercrime prevention, and customer service.

Business Intelligence

Contents
1. Answer for Q1..........................................................................................................................2
2. Answer for Q2..........................................................................................................................3
3. Answer for Q3..........................................................................................................................6
4. Answer of Q4.........................................................................................................................19
References......................................................................................................................................21
1

1. Answer for Q1
Text Mining
Text Analytics, otherwise called text mining, is the way toward looking at huge
accumulations of composed assets to create new data, and to change the unstructured text into
organized information for use in encourage investigation. Text mining distinguishes realities,
connections and declarations that would some way or another stay covered in the mass of printed
huge information. These certainties are removed and transformed into organized information, for
investigation, representation (e.g. through html tables, mind maps, graphs), joining with
organized information in databases or distribution centres, and further refinement utilizing
machine learning (ML) frameworks.
Difference between Text mining and Data Mining
Data mining is centredon data subordinate exercises, for example, bookkeeping, buying,
production network, CRM, and so on. The required data is anything but difficult to get to and
homogeneous. When calculations are characterized, the arrangement can be immediately
conveyed. The multifaceted nature of the data prepared make text mining ventures longer to
convey. Text mining checks a few go-between semantic phases of examination before it can
improve text (dialect speculating, tokenization, division, morpho-syntactic investigation,
disambiguation, cross-references, and so forth) (Mitsa, 2010). Next, significant terms extraction
and metadata affiliation steps handle organizing the unstructured substance to support area
particular applications. Additionally, tasks may include some heterogeneous dialects,
organizations or spaces. At long last, few organizations have their own scientific categorization.
Nonetheless, this is compulsory for beginning a text mining venture and it can take a couple of
months to be created
Technologies and tools required for text mining
1. Sentiment analysis tool
2. Topic modelling technique
3. Named entity recognition and event extraction technique
Applications
1 – Risk administration
2

Regardless of the business, insufficient hazard investigation is regularly a main source of
disappointment. This is particularly valid in the budgetary business where reception of Risk
Management Software in view of text mining innovation can drastically expand the capacity to
moderate hazard, empowering complete administration of thousands of sources and petabytes of
text reports, and giving the capacity to interface together data and have the capacity to get to the
correct data at the opportune time.
2 – Knowledge administration
Not having the capacity to discover imperative data rapidly is dependably a test while
overseeing vast volumes of text reports—simply ask anybody in the human services industry.
Here, associations are tested with a gigantic measure of data—many years of research in
genomics and sub-atomic methods, for instance, and in addition volumes of clinical patient
information—that could possibly be helpful for their biggest benefit focus: new item
improvement. Here, learning administration programming in light of text mining offer an
unmistakable and solid answer for the "data excess" issue.
3 – Cybercrime counteractive action
The unknown idea of the web and the numerous correspondence highlights worked
through it add to the expanded danger of web based wrongdoings. Today, text mining knowledge
and against wrongdoing applications are making web wrongdoing counteractive action less
demanding for any venture and law implementation or insight organizations.
4 – Customer mind benefit
Text mining, and characteristic dialect handling are visit applications for client mind.
Today, text investigation programming is much of the time received to enhance client encounter
utilizing distinctive wellsprings of important data, for example, reviews, inconvenience tickets,
and client call notes to enhance the quality, viability and speed in settling issues. Text
examination is utilized to give a quick, computerized reaction to the client, drastically lessening
their dependence accessible if the need arises focus administrators to take care of issues (Witten,
Frank, Hall & Pal, 2017).
3

2. Answer for Q2
Importance of Artificial Intelligence
Numerous organizations take up Artificial Intelligence (AI) innovation to attempt to
diminish operational costs, increment productivity, develop income and enhance client
encounter.
For most prominent advantages, organizations should take a gander at putting the full
scope of brilliant innovations - including machine learning, regular dialect preparing and then
some - into their procedures and items. In any case, even organizations that are new to AI can
receive significant benefits (Berlatsky, 2011).
AI affects business by conveying the correct AI innovation, your business may pick up
capacity to:
 spare time and cash via computerizing routine procedures and assignments
 increment profitability and operational efficiencies
 settle on quicker business choices in light of yields from psychological advancements
 stay away from mix-ups and 'human blunder', gave that keen frameworks are set up
appropriately
 utilize understanding to foresee client inclinations and offer them better, customized
involvement
 mine huge measure of information to create quality leads and develop your client
base
 accomplish cost funds, by upgrading your business, your workforce or your items
 increment income by recognizing and expanding deals openings
 develop aptitude by empowering examination and offering astute exhortation and
support
 As per an ongoing Infosys think about, the fundamental main thrust for utilizing AI in
business was contender advantage. From that point onward, the impetus originated
from:
 an official drove choice
 a specific business, operational or specialized issue
 an interior investigation
4

 client request
 a surprising answer for issue
 a branch of another undertaking
Artificial intelligence helps to transform companies
 proposals and substance curation
 personalization of news sustains
 example and picture acknowledgment
 dialect acknowledgment - to process unstructured information from clients and deals
prospects
 promotion focusing on and streamlined, ongoing offering
 information investigation and client division
 social semantics and feeling investigation
 robotized website composition
 prescient client benefit
These are just a portion of the cases of AI utilizes as a part of business. With the pace of
advancement expanding, there will probably be considerably more to come sooner rather than
later.
Limitations of artificial Intelligence
With the quick improvement of AI, various moral issues have sprung up. These include:
 the capability of computerization innovation to offer ascent to work misfortunes
 the need to redeploy or retrain representatives to keep them in occupations
 reasonable dissemination of riches made by machines
 the impact of machine association on human conduct and consideration
 the need to dispose of inclination in AI that is made by people
 the security of AI frameworks (eg self-sufficient weapons) that can conceivably
cause harm
 the need to relieve against unintended outcomes, as savvy machines are thought to
learn and grow autonomously
5

While these dangers can't be disregarded, it merits remembering that advances in AI can -
generally - make better business and better lives for everybody. On the off chance that actualized
capably, computerized reasoning has gigantic and helpful potential (Ennals, 2014).
3. Answer for Q3
Here, we will do the data mining analysis for bank data. The bank data contains the bank
data. The bank data analysis uses the J48 analysis in Weka data mining tool. Then, Do data
mining analysis by using the below steps and it shown below (Schémas commentés en santé
sécurité au travail , 2011).
First, Open Weka data mining tool. It is shown below
Click Explorer to Load the bank data set.
It is shown below.
6

Once successfully load the data.
After, clicks classify tab and click choose to select the trees.
Then, click the J48 to do the J48 analysis.
The J48 analysis for each attributes is shown below.
7

8

J48 Analysis for ID
9

J48 Analysis for Income
Correctly Classified Instances 228 38 %
Incorrectly Classified Instances 372 62 %
Kappa statistic 0.0233
Mean absolute error 0.3326
Root mean squared error 0.4748
Relative absolute error 97.8135 %
Root relative squared error 115.1822 %
10

Total Number of Instances 600
=== Detailed Accuracy By Class ===
TP Rate FP Rate Precision Recall F-Measure MCC ROC Area PRC
Area Class
0.617 0.610 0.451 0.617 0.521 0.007 0.495 0.447
INNER_CITY
0.243 0.244 0.288 0.243 0.263 -0.001 0.517 0.291 TOWN
0.188 0.075 0.321 0.188 0.237 0.141 0.576 0.246 RURAL
0.032 0.052 0.067 0.032 0.043 -0.028 0.522 0.108
SUBURBAN
Weighted Avg. 0.380 0.361 0.343 0.380 0.352 0.023 0.517 0.335
=== Confusion Matrix ===
a b c d <-- classified as
166 70 19 14 | a = INNER_CITY
110 42 13 8 | b = TOWN
53 19 18 6 | c = RURAL
39 15 6 2 | d = SUBURBAN
11

J48 Analysis for Save_Act
Correctly Classified Instances 431 71.8333 %
Incorrectly Classified Instances 169 28.1667 %
Kappa statistic 0.2968
Mean absolute error 0.3197
Root mean squared error 0.4565
Relative absolute error 74.6875 %
Root relative squared error 98.7075 %
12

Total Number of Instances 600
=== Detailed Accuracy By Class ===
TP Rate FP Rate Precision Recall F-Measure MCC ROC Area PRC
Area Class
0.430 0.152 0.559 0.430 0.486 0.302 0.738 0.504 NO
0.848 0.570 0.768 0.848 0.806 0.302 0.738 0.858 YES
Weighted Avg. 0.718 0.440 0.703 0.718 0.707 0.302 0.738 0.748
=== Confusion Matrix ===
a b <-- classified as
80 106 | a = NO
63 351 | b = YES
13

J48 Analysis for Mortgage
Correctly Classified Instances 393 65.5 %
Incorrectly Classified Instances 207 34.5 %
Kappa statistic 0.1149
Mean absolute error 0.4169
Root mean squared error 0.4904
Relative absolute error 91.8046 %
Root relative squared error 102.9264 %
14

Total Number of Instances 600
=== Detailed Accuracy By Class ===
TP Rate FP Rate Precision Recall F-Measure MCC ROC Area PRC
Area Class
0.898 0.799 0.678 0.898 0.772 0.137 0.568 0.704 NO
0.201 0.102 0.512 0.201 0.289 0.137 0.568 0.424 YES
Weighted Avg. 0.655 0.556 0.620 0.655 0.604 0.137 0.568 0.607
=== Confusion Matrix ===
a b <-- classified as
351 40 | a = NO
167 42 | b = YES
15

J48 Analysis for Sex
=== Stratified cross-validation ===
Correctly Classified Instances 321 53.5 %
Incorrectly Classified Instances 279 46.5 %
Kappa statistic 0.07
Mean absolute error 0.4994
Root mean squared error 0.5876
Relative absolute error 99.8804 %
Root relative squared error 117.5249 %
Total Number of Instances 600
16

=== Detailed Accuracy by Class ===
TP Rate FP Rate Precision Recall F-Measure MCC ROC Area PRC
Area Class
0.493 0.423 0.538 0.493 0.515 0.070 0.494 0.491
FEMALE
0.577 0.507 0.532 0.577 0.554 0.070 0.494 0.487 MALE
Weighted Avg. 0.535 0.465 0.535 0.535 0.534 0.070 0.494 0.489
=== Confusion Matrix ===
a b <-- classified as
148 152 | a = FEMALE
127 173 | b = MALE
J48 Analysis for Save_Act
17

Correctly Classified Instances 455 75.8333 %
Incorrectly Classified Instances 145 24.1667 %
Kappa statistic 0
Mean absolute error 0.3665
Root mean squared error 0.4281
Relative absolute error 99.8658 %
Root relative squared error 99.9998 %
Total Number of Instances 600
18

=== Detailed Accuracy By Class ===
TP Rate FP Rate Precision Recall F-Measure MCC ROC Area PRC
Area Class
0.000 0.000 0.000 0.000 0.000 0.000 0.489 0.238 NO
1.000 1.000 0.758 1.000 0.863 0.000 0.489 0.754 YES
Weighted Avg. 0.758 0.758 0.575 0.758 0.654 0.000 0.489 0.629
=== Confusion Matrix ===
a b <-- classified as
0 145 | a = NO
0 455 | b = YES
19

4. Answer of Q4
Dashboard
The above dashboard is outlined below.
A Sales Cycle is an impression of the time it takes to go through and through, from
potential chance to make a deal to a handshake and a paid receipt. In any case, amidst the
invigoration of making the deal, subtle elements get lost, efficiency isn't generally at its most
astounding and we won't not work as beneficially as we picture or expectation. Clearly,
shortening the Sales Cycle can fundamentally affect our main concern, put more cash in the bank
and guarantee a more joyful and more dedicated client base. Notwithstanding, before we can
upgrade the cycle, we should first comprehend it. Enter our business dashboard programming
and assemble our individual deals cycle length dashboard. We can begin with the fundamentals,
by first taking a gander at our present deals cycle length after some time to use as a benchmark
(Check-listes pour cadres dirigeants performants, 2012).
20

In this business dashboard layout, the length of a Sales Cycle is delineated as a business
pipe with four stages in making a deal; Opportunities, Proposals, Negotiations and Closings.
Each progression in this procedure takes a specific measure of time and the normal length of the
Sales Cycle is an impression of the normal time each stage should be finished, over all delegates
inside our business group.
In the wake of setting up and tweaking our own particular Sales Cycle stages, in
accordance with our business and specific item stock, we can begin evaluating our business
efficiency and that of every individual deals agent also. Is it true that one is rep fundamentally
beating other colleagues? Since we have that data readily available, we can bore down to
discover what is and isn't working. We can likewise utilize this business dashboard to track
singular rep's advance after some time. This assists with objective setting for people, and for the
business group all in all.
We definitely realize that the primary concern is to expand benefits and enhance
profitability; utilizing cutting edge Sales Cycle Length perceptions will help we promptly decide
the qualities and shortcomings in our business group and activities, and give we the data we have
to react as needs be.
References
Berlatsky, N. (2011). Artificial intelligence. Detroit: Greenhaven Press.
Ennals, J. (2014). Artificial Intelligence. Elsevier Science.
Mitsa, T. (2010). Temporal data mining. Boca Raton, FL: Chapman & Hall/CRC.
Tissot. (2011). Schémas commentés en santé sécurité au travail . Annecy-le-Vieux.
WEKA Business Media. (2012). Check-listes pour cadres dirigeants performants.
Zurich.
Witten, I., Frank, E., Hall, M., & Pal, C. (2017). Data mining. Amsterdam: Morgan
Kaufmann.
21