Text Mining: Unlocking Business Insights with Automation - Essay Sample

Introduction

The process of text mining is essential in modern business practices through ensuring that customer's needs are classified and that businesses identify the best goods and services for their clients. Most companies have encountered challenges in natural languages. Some tools like Sentimental analysis, Auto Model Rapid Miner, and Deep Learning Rapid Miner enable the user to get vital data from a massive document of comments automatically. These tools analyze and categorize the opinions expressed in a piece of text. In this essay, I focus on discussing what I have learned from the videos presented on Sentimental Analysis, Auto Model Rapid Miner, and Deep Learning Rapid Miner.

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Sentimental Analysis of 700000 Tweets from Super Bowl 50

In the video “Sentimental Analysis of 700000 tweets from super Bowl 50,", I have gained insights regarding advertising through obtaining the opinion of customers on social channels (Ott & Waldron, 2016). the video is an analysis of social content through a collection of tweets. It showcases how to grasp clients and improve the satisfaction of customers and optimize markets through analyzing texts. Also, it makes sense of the data in social media across various responses utilizing predictive modeling and sentimental analysis. Further, an individual can grasp the impacts of predictive texts on business opportunities and share and communicate insights of customers through the visualization of data. From the video, I have learned that at least 80 percent of data in business is unstructured and is contained in the endless text-based social conversations that happen every day(Ott & Waldron, 2016). through unlocking the hidden value of the text in the process of predictive analytics, and enterprise understands the needs and opinions of customers to make better and informed business decisions. Through focusing on 15 top brands in the determination of viewer sentiments and potential trend for adoption, the result is that what matters is not the report of the databases but how the business can combine that data with unstructured sources, to obtain a perfect picture of the general performance(Ott & Waldron, 2016).. these are made more comfortable through industry-leading predictive analytic tools including; the RapidMiner and state of the art tools like the Aylien text analysis extension.

An Introduction to Auto Model/ RapidMiner

from this video, I learn that RapidMiner introduces the Auto Model that is a new addition RapidMiner that accelerates the things done by data scientists in establishing machine models utilized in leaning (Mierswa, 2018). the video outlies that unlike the existing machine learning processes that are automated, the Auto Model is not a Black box that prevents data scientists from grasping how the model performs. Behind the scenes, Auto Model generates a Rapid Miner Studio procedure that enables scientists to fine-tune and test models before placing them into production. In the Video, Dr. Ingo Mierswa provides a detailed overview of the aspects of RapidMiner Auto Model that include; ability to highlight the features with the most significant impacts in the business objective, he suggests the best machine learning methods and automatically generating optimized cross-validating models and how built-in visualizations and interactive model simulators enable users to explore the model to see how it performs. Generally, I have learned that the Auto Model analyzes information automatically to identify the common problems in quality, including missing values, correlations, and stability (Mierswa, 2018). it removes problems with data columns through a single click by the user, and it extracts features, categorizing data with built-in sentiments language detection and analysis.

An Introduction to Deep Learning /Rapid Miner

in this video, I have learned the basic overview of the deep learning and its building blocks, as explained by Phillip Schlender. Also, I have learned the scope of deep learning applications, and how rapid leverages TensorFlow and other frameworks through its new Keras Extension in Rapid Miner Studio 7.6. I have identified that in the deep learning Rapid Miner, and there are different types of layers, including; fully connected, convolutional, recurrent, dropout, and pooling (Schlunder, 2017). the model is defined as a setup of layers or architecture where weights are obtained through optimization of a loss over multiple epochs. RapidMiner has features that can be chosen by the user in establishing their network, including the fully connected layer, basic optimization, advanced optimization, multi-threading, GPU support, and developed layers. It is advisable to start by using the basic optimization as the user proceeds to learn more about the other features. An individual should attempt using the deep learning Rapid Miner when; they are stuck with the current model whose accuracy is not high enough, when they have a massive amount of available training data and when there is a fixed specific use case. Also, when the notion of memory is required in understanding a context (Schlunder, 2017). these should be done to determine whether a person's infrastructure is suited for this model or if the potential ROI is high enough for the resource investment.

Conclusion

Sentimental analysis from super bowl 50, Auto Model Rapid Miner, and Deep Learning Rapid Miner are predictive models that utilize automated learning and data science practices. The Rapid Miner is a method of documenting and summarizing various sentiment analyses, and the first inbuilt uses the extension of Aylien Text Analysis. These models can be utilized in distinct forms of text documents, including data from twitter or comments posted don Facebook concerning services and products, to help in ensuring that end-users make correct decisions and that companies can improve in their services.

References

Schlunder, P. (2017). An Introduction to Deep Learning Rapid Miner (Video) YouTube. https://www.youtube.com/watch?v=rJCU8ODRwyg&t=3030s

Ott, T,. & Waldron M. (2016, Feb 18). Sentimental Analysis of 700, 000 tweets from Super Bowl 50-RapidMiner and Aylien (Video.) YouTube. https://www.youtube.com/watch?v=CJaqhYL3rY4

Mierswa, I. (2018, Feb 21). An Introduction to Auto Model Rapid Miner (Video.) YouTube. https://www.youtube.com/watch?v=uq36VLMju-4