Other
Text Mining and Sentimental Analysis

Question Description

Find a readily available sentiment text data set (see Technology Insights 7.2 (page 329) in your textbook(attached document) for a list of popular data sets) and download it into your computer. If you have an analytics tool that is capable of text mining, use that; if not, download RapidMiner (rapid-i.com) and install it. Also install the text analytics add-on for RapidMiner. Process the downloaded data using your text mining tool (i.e., convert the data into a structured form). Build models and assess the sentiment detection accuracy of several classification models (e.g., support vector machines, decision trees, neural networks, logistic regression, etc.). Write a detailed report where you can explain your findings and your experiences.

Unformatted Attachment Preview

Technology Insights 7.2 Large Textual Data Sets for Predictive Text Mining and Sentiment Analysis Congressional Floor-Debate Transcripts: Published by Thomas et al. (Thomas and B. Pang, 2006); contains political speeches that are labeled to indicate whether the speaker supported or opposed the legislation discussed. Economining: Published by Stern School at New York University; consists of feedback postings for merchants at Amazon.com. Cornell Movie-Review Data Sets: Introduced by Pang and Lee (Pang and Lee, 2008); contains 1,000 positive and 1,000 negative automatically derived document-level labels, and 5,331 positive and 5,331 negative sentences/snippets. Stanford—Large Movie Review Data Set: A set of 25,000 highly polar movie reviews for training, and 25,000 for testing. There is additional unlabeled data for use as well. Raw text and already processed bag-of-words formats are provided. (See: http:// ai.stanford.edu/~amaas/data/sentiment.) MPQA Corpus: Corpus and Opinion Recognition System corpus; contains 535 manually annotated news articles from a variety of news sources containing labels for opinions and private states (beliefs, emotions, speculations, etc.). Multiple-Aspect Restaurant Reviews: Introduced by Snyder and Barzilay (Snyder and Barzilay, 2007); contains 4,488 reviews with an explicit 1-to-5 rating ...
Purchase answer to see full attachment

Final Answer

Attached.

1

Text mining and sentimental Analysis
Name
Affiliation
Date

2

Abstract
Text mining is the process or method to derive data or information from unstructured text
and this deriving high-quality data which can be useful to some sources. Text mining
information output is used to derive patterns and helps in making decisions such as pattern
learning. It is used to solve business problems. The sentiment analysis is the processor methods
in which the text is studied in order to determine the emotion or sentiment related to the text and
computationally identifying and categorizing opinions expressed in a piece of text. This
determines if the expression is positive, negative, or neutral. In this project, we collected dataset
from Stanford. Edu website and we used Rstudio to run the data and perform text mining and
sentimental analysis. Hence, this project will illustrate various techniques that can be done in the
text mining process.
I.

Introduction

The data that is being used in th...

Super_Teach12 (2461)
Cornell University

Anonymous
Solid work, thanks.

Anonymous
The tutor was great. I’m satisfied with the service.

Anonymous
Goes above and beyond expectations !

Studypool
4.7
Trustpilot
4.5
Sitejabber
4.4

Brown University





1271 Tutors

California Institute of Technology




2131 Tutors

Carnegie Mellon University




982 Tutors

Columbia University





1256 Tutors

Dartmouth University





2113 Tutors

Emory University





2279 Tutors

Harvard University





599 Tutors

Massachusetts Institute of Technology



2319 Tutors

New York University





1645 Tutors

Notre Dam University





1911 Tutors

Oklahoma University





2122 Tutors

Pennsylvania State University





932 Tutors

Princeton University





1211 Tutors

Stanford University





983 Tutors

University of California





1282 Tutors

Oxford University





123 Tutors

Yale University





2325 Tutors