Campbesville University Text Mining Software & Sentimental Analysis Discussion

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Computer Science

Campbesville University

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What are the common challenges with which sentiment analysis deals? What are the most popular application areas for sentiment analysis? Why?

Go to teradatauniversitynetwork.com and find the case study named “eBay Analytics.” Read the case carefully and extend your understanding of it by searching the Internet for additional information, and answer the case questions.

Go to kdnuggets.com. Explore the sections on appli-cations as well as software. Find names of at least three additional packages for data mining and text mining

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Explanation & Answer

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Running Head: DATA MINING AND TEXT MINING SOFTWARE

Data Mining and Text Mining Software
Student’s Name
Institution

SENTIMENT ANALYIS
Some of the tools used for data mining and text mining encompass
AdvanceMiner, BayesianLab, BDB predictive workbench, and Text Sentiment
Visualizer (KDnuggets, 2020). Advanced miner is a powerful data analytic tool that
transforms, models, analyses, and reports on facets of data. BayesianLab uses
Bayesian networks to provide an intuitive mining tool for data preparation,
interpretation, cleaning, and clustering. It also uses machine learning to learn about
data analytics. BDB predictive workbench uses ML algorithms and the R
programming language to analyze data. R is a language designed for easy data
manipulation and extraction of common and complex statistical analytics. Text
Sentiment Visualizer uses a deep neural network to enhance learning comment
patterns and detecting sentiments. Ascribe is a blend of machine learning and natural
language processing that enhances advanced text analysis.

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SENTIMENT ANALYIS
References
KDnuggets. (2020). Software for analytics, data science, data mining, and machine
learning. https://www.kdnuggets.com/software/index.html


Running Head: SENTIMENT ANALYSIS APPLICATIONS

Sentiment Analysis applications
Student’s Name
Institution

SENTIMENT ANALYIS APPLICATIONS

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The most common sentiment analysis applications are the Voice of Market
(VOM) to determine how customers react to various products of a given company's
products or services. It is mostly used to extract the negative or positive feelings of
customers as this helps businesses improve their services and know the products that
are performing well and those that have problems. It also enhances the real-time
detection of sentiments allowing companies to detect problems long before they
negatively impact their services. Additionally, sentiment analysis can help manage
brand reputation (Zhang, Wu & Jiang, 2018). Managing a company's brand reputation
to remain competitive in the market. Furthermore, a bad reputation can lead to
customer churn and poor brand perception. Sentiment analysis captures the negative
comments and sentiments on a company's brand and helps them respond accordingly
to prevent further damage. Moreover, sentiment analysis is used to extract the
customer's voice to help identify market opportunities. It also allows for the
identification of functional and non-functional requirements for a product.

SENTIMENT ANALYIS APPLICATIONS

3

References
Zhang, W., Xu, M., & Jiang, Q. (2018, July). Opinion mining and sentiment analysis
in social media: challenges and applications. In International Conference on HCI
in Business, Government, and Organizations (pp. 536-548). Springer, Cham.


Running Head: SENTIMENT ANALYSIS

Sentiment Analysis
Student’s Name
Institution

SENTIMENT ANALYIS

2

Some of the common challenges of sentiment analysis include sarcasm detection.
Some people do not directly express their negative feelings in negative ways. At times,
people express their negative sentiments usi...


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