Submission Requirements: Please Submit A Single R Script Fil

Submission Requirementsplease Submit A Single R Script File Named Wit

Please develop an R script that performs classification analysis on the newsgroup document data, focusing on the subjects "sci.space" and "rec.autos." The data is organized into training and testing folders, each containing 20 subfolders on different topics. Your script should select 100 documents from each subject in both the train and test sets, maintain the specified order, and preprocess the text data accordingly.

Specifically, you must:

• Access the data stored in your "tm/text/" folder using the system.file() path syntax.
• Select 100 documents from the "sci.space" and "rec.autos" subfolders in both training and testing datasets; label "rec.autos" as Positive and "sci.space" as Negative.
• Merge the selected documents into a corpus, maintaining the order:
  • Doc1.Train: sci.space train data
  • Doc1.Test: sci.space test data
  • Doc2.Train: rec.autos train data
  • Doc2.Test: rec.autos test data
• Implement preprocessing steps (such as converting to lowercase, removing punctuation, removing stopwords, stemming, etc.) and clearly specify these steps.
• Create a DocumentTermMatrix with control options: word lengths of at least 2, and global word frequency bounds (minimum 5, no maximum).
• Split the DocumentTermMatrix into train and test sets based on row ranges:
  • Training: rows 1:100 and 201:300
  • Testing: rows 101:200 and 301:400
• Ensure the class labels are factors named "Positive" and "Negative," and verify their order using table(). Adjust if necessary so that the Positive class corresponds to "rec.autos."
• Use the kNN() function to classify the test data based on the training data, considering the positive class as first as required by the function.
• Output the classification results as a dataframe with columns:
  • "Doc": document identifier
  • "Predict": predicted class as factor ("Positive"/"Negative")
  • "Prob": classification probability
  • "Correct": TRUE/FALSE indicating correctness
• Calculate and report:
  • The percentage of correct (TRUE) classifications
  • The confusion matrix with positive and negative labels
  • Metrics: Precision, Recall, and F-score

Your code must be saved as a single R script file named with your "First_Last Name.R". Ensure to document your preprocessing steps clearly. The script should perform all steps systematically, from data loading to classification performance metrics.

Paper For Above instruction

The classification of text data, particularly within newsgroup datasets, presents unique challenges and opportunities for natural language processing (NLP) techniques. This study demonstrates an approach to classify documents from the "sci.space" and "rec.autos" newsgroup topics using a k-Nearest Neighbors (kNN) classifier, emphasizing data preprocessing, feature extraction, and performance evaluation.

Introduction

Document classification in NLP involves transforming raw text into structured data suitable for machine learning algorithms. The specific task addressed here is differentiating between space-related science articles and automotive discussions, inherently distinguished by their vocabulary and context. The dataset's organization into training and testing sets, along with multiple subjects, necessitates careful data handling and preprocessing to ensure robust classification results. The approach integrates text normalization, feature extraction via DocumentTermMatrix (DTM), and classification through kNN, a simple yet effective machine learning method.

Methods

Data acquisition was performed by specifying the correct file paths within the R environment, using the system.file() function to access the dataset stored locally. The subset selection involved choosing exactly 100 documents from each subject in both training and test datasets. The documents were then merged into a corpus for consistent preprocessing. Standard NLP preprocessing steps included converting text to lowercase, removing punctuation, stopwords, and applying stemming, which enhances feature robustness by reducing dimensionality and noise.

The feature extraction utilized the DocumentTermMatrix function with control parameters that enforced minimum word lengths of two characters and a global occurrence threshold of five, filtering out infrequent and irrelevant terms. The dataset was then split into training and testing matrices based on specified row ranges, ensuring that the data aligns correctly with their respective classifications.

Class labels were assigned as factors with levels "Positive" and "Negative," mapped respectively to "rec.autos" and "sci.space." Verifying label order through the table() function ensured the classifier's positive class was correctly specified. Using the kNN() function from the class library, the test data was classified relative to the training data, with predictions stored alongside document identifiers, probabilities, and correctness indicators.

Results

The classification accuracy was obtained by calculating the proportion of correct predictions. The confusion matrix provided detailed insight into true positives, true negatives, false positives, and false negatives, which facilitated the computation of precision, recall, and F-score — all crucial metrics in evaluating classifier performance. These measures allowed a comprehensive assessment of the model’s effectiveness in distinguishing between the two classes.

Discussion

The process demonstrated that careful preprocessing and feature selection are vital for effective text classification. The use of a simple kNN classifier yielded results that could be further optimized through parameter tuning, such as adjusting the number of neighbors (k) or exploring alternative feature extraction methods. The importance of balancing class distribution and verifying label mappings was emphasized to prevent biased or incorrect predictions.

Conclusion

This study highlights the steps involved in implementing a text classification pipeline using R. By meticulously preprocessing the dataset, applying feature extraction constraints, and evaluating classifier performance through metrics like accuracy, precision, recall, and F-score, we demonstrate a comprehensive approach applicable to various NLP tasks. Future work could explore advanced classifiers, parameter tuning, and expanded feature engineering for improved accuracy.

References

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