Text Mining and Sentimental Analysis

timer Asked: Mar 28th, 2019
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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.

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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 ...
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School: Cornell University


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Thanks, good work

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