Text Mining Using R
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The session focused on Data Mining using R Language where I analyzed a large volume of text files to find out some meaningful insights using concepts like DocumentTermMatrix and WordCloud.
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This document provides an overview of text mining techniques and processes for analyzing Twitter data with R. It discusses concepts like term-document matrices, text cleaning, frequent term analysis, word clouds, clustering, topic modeling, sentiment analysis and social network analysis. It then provides a step-by-step example of applying these techniques to Twitter data from an R Twitter account, including retrieving tweets, text preprocessing, building term-document matrices, and various analyses.
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Becoming a successful data scientist requires a unique combination of technical skills, business acumen, and critical thinking ability. To achieve your career goals in this field, you need a structured plan or a data science roadmap that outlines the skills, tools, and knowledge required to succeed. In this blog, we’ll take a closer look at what a data science roadmap is, why it’s important, and how to create one that works for you.
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The document discusses various techniques for information retrieval and language modeling approaches to IR, including:
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Dual Embedding Space Model (DESM)
A fundamental goal of search engines is to identify, given a query, documents that have relevant text. This is intrinsically difficult because the query and the document may use different vocabulary, or the document may contain query words without being relevant. We investigate neural word embeddings as a source of evidence in document ranking. We train a word2vec embedding model on a large unlabelled query corpus, but in contrast to how the model is commonly used, we retain both the input and the output projections, allowing us to leverage both the embedding spaces to derive richer distributional relationships. During ranking we map the query words into the input space and the document words into the output space, and compute a query-document relevance score by aggregating the cosine similarities across all the query-document word pairs.
We postulate that the proposed Dual Embedding Space Model (DESM) captures evidence on whether a document is about a query term in addition to what is modelled by traditional term-frequency based approaches. Our experiments show that the DESM can re-rank top documents returned by a commercial Web search engine, like Bing, better than a term-matching based signal like TF-IDF. However, when ranking a larger set of candidate documents, we find the embeddings-based approach is prone to false positives, retrieving documents that are only loosely related to the query. We demonstrate that this problem can be solved effectively by ranking based on a linear mixture of the DESM and the word counting features.
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Slides from my keynote talk at the Recherche d'Information SEmantique (RISE) workshop at CORIA-TALN 2018 conference in Rennes, France.
(Abstract)
Neural Information Retrieval (or neural IR) is the application of shallow or deep neural networks to IR tasks. Unlike classical IR models, these machine learning (ML) based approaches are data-hungry, requiring large scale training data before they can be deployed. Traditional learning to rank models employ supervised ML techniques—including neural networks—over hand-crafted IR features. By contrast, more recently proposed neural models learn representations of language from raw text that can bridge the gap between the query and the document vocabulary.
Neural IR is an emerging field and research publications in the area has been increasing in recent years. While the community explores new architectures and training regimes, a new set of challenges, opportunities, and design principles are emerging in the context of these new IR models. In this talk, I will share five lessons learned from my personal research in the area of neural IR. I will present a framework for discussing different unsupervised approaches to learning latent representations of text. I will cover several challenges to learning effective text representations for IR and discuss how latent space models should be combined with observed feature spaces for better retrieval performance. Finally, I will conclude with a few case studies that demonstrates the application of neural approaches to IR that go beyond text matching.
Neural Models for Document Ranking
The document discusses a neural model called Duet for ranking documents based on their relevance to a query. Duet uses both a local model that operates on exact term matches between queries and documents, and a distributed model that learns embeddings to match queries and documents in the embedding space. The two models are combined using a linear combination and trained jointly on labeled query-document pairs. Experimental results show Duet performs significantly better at document ranking and other IR tasks compared to using the local and distributed models individually. The amount of training data is also important, with larger datasets needed to learn better representations.
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In this study we propose a new approach based on Pattern Structures, an extension of Formal Concept Analysis, to provide exploration over Linked Data through concept lattices. It takes RDF triples and RDF Schema based on user requirements and provides one navigation space resulting from several RDF resources. This navigation space provides interactive exploration over RDF data and allows user to visualize only the part of data that is interesting for her.
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This document presents the Duet model for document ranking. The Duet model uses a combination of local and distributed representations of text to perform both exact and inexact matching of queries to documents. The local model operates on a term interaction matrix to model exact matches, while the distributed model projects text into an embedding space for inexact matching. Results show the Duet model, which combines these approaches, outperforms models using only local or distributed representations. The Duet model benefits from training on large datasets and can effectively handle queries containing rare terms or needing semantic matching.
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R is a programming language and software environment for statistical analysis and graphics. It was created by Ross Ihaka and Robert Gentleman in the early 1990s at the University of Auckland, New Zealand. Some key points:
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Adarsh_Masekar(2GP19CS003).pptxAdarsh_Masekar(2GP19CS003).pptxAdarsh_Masekar(2GP19CS003).pptxAdarsh_Masekar(2GP19CS003).pptxAdarsh_Masekar(2GP19CS003).pptxAdarsh_Masekar(2GP19CS003).pptxAdarsh_Masekar(2GP19CS003).pptxAdarsh_Masekar(2GP19CS003).pptxAdarsh_Masekar(2GP19CS003).pptxAdarsh_Masekar(2GP19CS003).pptxAdarsh_Masekar(2GP19CS003).pptxAdarsh_Masekar(2GP19CS003).pptx
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Running Head: E-GRANT REQUIREMENTS 2
E-GRANT REQUIREMENTS 2
E-grant Requirements
Krishna Marepalli
170068
Harrisburg University
E-grant requirements
Business requirements
User requirements
System requirements
Non-functional requirements
Safe means of money transfer
The applicant requires to enter their banking details into the system.
The e-grant system should enable the user to enter their banking data.
Conform with financing p & ps
The state administrators require to send money to applicants (Little, 2016).
The e-grant system should also enable the user to select the account type they wish their money to be deposited.
Submission of applications
The applicant requires to sign in to the system at any time.
The system should allow the user to create an account and enter their data.
Conform with system processing p & ps
User should be in a position to select the required application from a list of applications.
The system should be user friendly and should allow them to navigate through the application process (Alla, Pazos & DelAguila, 2017).
The user requires to submit their applications
The system is required to send confirmatory messages to the applicants.
Implementation of a standard accreditation scale.
Administrators require to evaluate the applications.
The system should permit the administrators to access the applications at all times.
Conform with auditing p & ps
The system is required to store the applications in a systematic manner for easier retrieval.
Administrators require to turn down or approve applications.
The system should allow the administrators to carry out these approvals and rejections (Chari & Agrawal, 2018).
The system is required to allow for a comment section.
Administrators require to enter applications scores manually
The system should enable the administrators to enter the applications scores.
The system should enable the entered scores to be saved.
The system is also required to update the scores regularly and automatically.
References
Alla, S., Pazos, P., & DelAguila, R. (2017). The Impact of Requirements Management Documentation on Software Project Outcomes in Health Care. In IIE Annual Conference. Proceedings (pp. 1419-1423). Institute of Industrial and Systems Engineers (IISE).
Chari, K., & Agrawal, M. (2018). Impact of incorrect and new requirements on waterfall software project outcomes. Empirical Software Engineering, 23(1), 165-185.
Little, T. A. (2016). A Foundational Perspective on Core Competency Requirements for Project Management Initiatives.
This is a formula to calculate a loan payment. The input is the amount of the loan, the number of payments, and the interest rate.
payment = loan payment (a = loan amount, r = rate, n = periods)
The rate and periods should match each other – for example, if the period is a number of months, then the rate should be a monthly rate and the payment will be a monthly payment.
If you have the annual interest rate and ...
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resume_MH
Mengling Hettinger is applying for a data scientist position. She has a PhD in physics from Michigan State University and has worked as a data scientist at AT&T for 4 years. Her experience includes developing models for large datasets using tools like R, Python, Pig and Hive. She has strong programming, statistical analysis, and machine learning skills.
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DATA MINING USING R (1).pptx
The document discusses an orientation program on data mining using R programming. It covers various topics related to data science including data analysis, data mining, R programming, and basic R commands. Some key points:
- It discusses the differences between data, information, and knowledge. Data is processed to get information, and information combined with experience leads to knowledge.
- The steps in data analysis are explained as collect, clean, organize, explore, and model data to get insights and make decisions.
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- Basic R commands for vectors, importing/exporting CSV files, and coercion
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- 5. Data Structures in R
- 6. Machine Learning Linear Regression: ◾ Regression analysis is a form of predictive modeling technique which investigates the relationship between a dependent (target) and independent variable(s) (predictor). ◾ It falls under Supervised Learning technique. ◾ Here, we fit a curve / line to the data points, in such a manner that the differences between the distances of data points from the curve or line is minimized. ◾ In this technique, the dependent variable is continuous, independent variable(s) can be continuous or discrete, and nature of regression line is linear. ◾ Linear Regression establishes a relationship between dependent variable (Y) and one or more independent variables (X) using a best fit straight line (also known as regression line).
- 8. Data Visualization Using package “plotrix”, we will see the following visualizations in action: - 2D Pie Chart - 3D Pie Chart - Bar Chart
- 9. Text Mining ◾ Text Mining or Text Analytics is one of the branch of Data Analytics where we specifically look at the textual data. ◾ It is the process of extracting meaning insights from text (unstructured). ◾ We can analyze words, clusters of words used in documents using various algorithms/packages. ◾ In the most general terms, text mining will “turn text into numbers”.
- 10. Text Mining (contd.) ◾ CORPUS: A text corpus is a large and unstructured set of texts. ◾ Term Document Matrix and Document Term Matrix:
- 11. Thank You !