An Ngram-Based Approach to Determine Trends and Patterns in the Social Networks
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Date
2023
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE AFRICON
Abstract
The recent progress in computing has made it easier
to collect and store huge amounts of information in a text. The
growing size of text datasets in text mining and the high
dimensionality associated with knowledge discovery is a great
challenge that makes it difficult to classify documents into various
categories and sub- categories. This paper focuses on how text can
be mined from social networks and then categorized using n-grams
to determine specific trends and patterns. The main aim of
Knowledge Discovery is to extract knowledge from data in the
context of large databases. The volume of information that is
available is increasing every day. This data ranges from that used
in business transactions to scientific data, sensor data, pictures,
videos, etc. There is, therefore, a need for a system capable of
extracting the core of available information and automatically
generating reports, opinions, or summaries of data to aid
organizations in better decision-making. Knowledge Discovery is
a repetitive process where evaluation measures are often enhanced,
mining done on data can be refined, there is an integration of new
data, and the data is transformed to get accurate and more
appropriate results. The data collected from social networks need to
be filtered to capture specific text that will be useful to a PR brand
following what clients say about their products online. There is a
need for a technique that will provide a quick and precise way of
fetching specific text from huge amounts of data on social networks
to help analyze the feedback. This research analyzes the use of ngrams to fetch specific text from near-real-time customer feedback
that is in the form of large data on Twitter to help Public Relations
agencies determine the trends and patterns that will help them align
their brands with customer preferences.
Keywords—knowledge discovery, data mining, trends, and
patterns.
Description
Research
Keywords
TECHNOLOGY::Information technology