The data should have at least 3,000 text documents and perform Unsupervised analysis using Rstudio with below methods:-
o Sentiment Analysis (Lexicon)
o Latent Semantic Analysis (LSA)
o Topic Models
The deliverable will include 4 components:
1. A written report that summarizes your key findings in a clear, concise manner to a business
o Describe how you chose your sample (Random? Convenience? Criteria-based?).
o Describe your text variable.
o Describe relevant non-text variables and include descriptive statistics and exploratory
D. Analysis Results
o Present your analysis findings in-depth.
o Summarize why you chose your analysis method, the analysis itself (including any
modeling decisions made) and the results.
o Present internal or external validation measures and accompanying plots, as necessary.
E. Discussion & Conclusion
o Discuss the high-level findings of your analysis in words.
o Conclude your report by drawing real-world connections to how your analysis can be used, with a focus on business implications.
-The written report should guide the content of your presentation, but you should focus on the highlevel information and insights.
3. An R Script file (.R) with all (annotated/commented!) code used to conduct your analysis.
4. An RData file (.RData) containing all objects, functions, values and data used.
Hi, I have worked on housing price prediction in R and sentiment analysis on tweets in Python. I'm also familiar with LSA and topic modeling. I would like to work on your project. Let me know if you want to discuss Plus
13 freelance font une offre moyenne de $303 pour ce travail
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With 2+ years as a statistician and piloting of successful text document analysis projects in R, I am an ideal candidate. I have experience with a variety of packages and data formats including Sentiment analysis, Late Plus
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