In collect their database. Natural language processing and

In
this study we propose an applied text mining approach for detecting suspicious
word and analyze information from data collection to visualization. The
approach follow steps of data collection, natural language processing, text
analysis and ontology, and presentation. We are focusing more on the domain
based ontology development because it perform function of text analysis such as
the topic extractions and classifications.Data collection

Firstly, step in the proposed text mining
approach is collecting several data from web application sources such as online
contents, social media data. There are many data collection methods that are
considered depending on data types needed to be collected. We used these types
of data collection together with the data Open API, database interface, and
manual submission.

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The crawling robot, a kind of search
engines in web portal site, Extract URLs they point to Places the extracted
URLs on a queue. Open API is easily in understanding a protocol to access a
database system and capture data. Especially, social network service such as
Facebook and Twitter provide their API to access and collect their database.

 

Natural language processing and ontology
developments

After data collection check condition of
gathered data and processed them to the next stage. This stage clean up the
data which have gathered may have too much garbage to analyze them so we have
to filter them. Filtering data is preprocessing in text mining approach. In
this work, natural language processing for data cleaning is conducted as
describing text sentence, removing stop-words and disabled letters (i.e. html
tags, punctuation, numbers, and emoticons), and transform countable data set
such as item document matrix or item list.

 Text
mining involves the language resources such as taxonomy, ontology, and
sentiment Dictionary, to obtain more accurate and efficient results. Ontology
development process is to help analyze text and multimedia data on domain
specific knowledge, emotions opinion, thought etc. in this regards our proposed
methodology which explain in 5 steps for domain specific ontology development
concludes determine scope,  consider recycle,
extract terms form source data, define taxonomy, validate the preliminary
ontology.

 

 Text Analytics

The following stage is text analysis to
mine concealed understanding from the gathered text information. There are
different text analysis, for example, domain extraction, classification, clustering,
sentiment analysis, time arrangement analysis.  For example, domain and buzz analysis are for
the most part related with domain interesting issues and society. Suspicious
message detection is a technique utilizing text classification consideration,
for example, lead based framework or machine learning algorithm.

Sentiment analysis tries to read the
particular mind, emotion and contemplated items about people events or product
services, with help of sentiment dictionary or machine learning approach.
However language sources are imperative in any way. Statistics is functional of
characters the dataset compare figures in the same collection and other revel association
among variables and calculate the future information.

 

 

Presentation and Visualization

After text data analysis we use
visualization techniques to present the text mining data results descriptions,
tables, graphs, diagrams, and images. Emerging of visualization in big-data
analysis is issues. Therefore, the last phase in the proposed methodology is
about presentation of text mining results using visualization and others. There
are simple one such as word cloud (tag cloud) to more complicating outputs such
as time series graph, network analysis graph, and Dendrogram. Indeed, effective
presentation is able to make text mining results easier to understand. In this
regard, the major purpose of the visualization is to make results simple,
clear, and easy to comprehend its meaning and use it for decision-making.