Assignment 1a: 25 Points, Due By 11:59 Pm

Assignment 1a 25 Points Due Date 95 1159pm Description I

In this assignment, you are going to write a python program to read and tokenize the data. The following is the training data format where the first column is the reviewer id, the second column indicates whether this review is fake or true, the third column represents whether the review is positive or negative, and the rest is the review. Your task is to learn whether the review is fake or true and positive or negative based on the review. Input Data 064BmtQ Fake Neg I was very disappointed with this hotel. I have stayed … 0Dh2p5S True Pos We stayed at the Palmer House Hilton … … Your first task is read the data into your python objects. • Extract the labels ['Fake', 'Neg'] • Extract each review I was very disappointed with … the chain's reputation. • Tokenize the sentences ['disappointed', 'hotel', 'stayed', 'swissotels', 'enjoyed', 'service', 'described', 'aloof', 'warmth', 'prolonged', 'checkin', 'procedure', 'woman', 'repeatedly', 'asked', 'provide', 'information', 'given', 'minutes', 'ago', 'precise', 'room', 'took', 'forever', 'pick', 'good', 'sign', 'way', 'busy', 'food', 'arrived', 'late', 'cold', 'man', 'tried', 'replace', 'hour', 'price', 'reduction', 'free', 'dessert', 'apologize', 'cleanliness', 'godly', 'knocked', 'door', '0800', 'despite', 'fact', 'doorknocker', 'requesting', 'sleeper', 'stay', 'clearly', 'did', 'help', 'build', 'chain', 'reputation'] • Store the extracted data to lists • Repeat it for all the data • Print out the first and the last labels from your stored list • Print out the first and the last tokens (reviews) from your stored list

Paper For Above instruction

This assignment aims to introduce fundamental text processing techniques in Python, focusing on reading, tokenizing, and organizing textual data related to reviews. The primary goal is to prepare the data effectively for subsequent analysis or machine learning tasks—specifically to classify reviews based on authenticity (fake or true) and sentiment (positive or negative). Implementing such procedures builds foundational skills in data preprocessing, which is crucial in natural language processing (NLP) applications.

The first step involves reading a training data file, which contains review records structured with reviewer IDs, labels for authenticity and sentiment, and the review text itself. Each line follows a fixed format: reviewer ID, authenticity label (Fake or True), sentiment label (Neg or Pos), and the review text. To handle this efficiently, the Python program must parse each line, extract relevant elements, and store them appropriately.

Data extraction begins by isolating the authenticity labels into a list, which can be used later for supervised learning models. Similarly, extracting review texts into a list facilitates text analysis, feature extraction, and model training. Tokenization is a critical step, breaking down review texts into individual words or tokens that represent meaningful units within the sentences. This process can be performed using Python’s string methods or dedicated NLP libraries like NLTK or spaCy, depending on scope.

Once the data is processed, the program outputs specific information to verify correctness: the first and last labels stored, and the first and last review texts. These outputs serve as checks to ensure data integrity and correct processing. This exercise not only helps in understanding data manipulation but also in preparing datasets for machine learning tasks such as classification models.

The overall approach involves reading the file line-by-line, parsing each line into components, storing the labels and reviews in lists, tokenizing the reviews, and performing small validation checks through print statements. Proper handling of whitespace, punctuation, and potential anomalies in the data ensures robustness of the implementation.

In summary, this assignment provides practical experience in text data preprocessing associated with sentiment analysis and fake review detection. Mastery of these techniques is essential for building effective NLP applications, and the skills developed here form the foundation for more advanced tasks such as feature engineering and model development.

At the end, your Python program should accomplish the following:

  • Read and parse the training data file
  • Extract authenticity labels ('Fake', 'True')
  • Extract review texts
  • Tokenize review texts into individual words
  • Store labels and tokenized reviews in separate lists
  • Print the first and last labels from the list
  • Print the first and last reviews from the stored list

References

  • Bird, S., Klein, E., & Loper, E. (2009). Natural Language Processing with Python. O'Reilly Media.
  • Jurafsky, D., & Martin, J. H. (2020). Speech and Language Processing (3rd ed.). Pearson.
  • Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction to Information Retrieval. Cambridge University Press.
  • nltk. (2023). Natural Language Toolkit. https://www.nltk.org/
  • SpaCy. (2023). Industrial-strength NLP in Python. https://spacy.io/
  • Leacock, C., & Chodorow, M. (2003). Combining Local Context and WordNet Similarity for Word Sense Identification. WordNet Consortium.
  • Press, W. H., Teukolsky, S. A., Vetterling, W. T., & Flannery, B. P. (2007). Numerical Recipes: The Art of Scientific Computing. Cambridge University Press.
  • Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
  • Kim, Y. (2014). Convolutional Neural Networks for Sentence Classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP).
  • Sarwar, W., Karypis, G., Konstan, J., & Riedl, J. (2001). Item-based Collaborative Filtering Algorithms. Proceedings of the 10th International Conference on World Wide Web.

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