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Natural Language Processing Assignment Help

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Frequently Asked Questions

Q. 1)    You have been provided with a dataset containing customer reviews for a range of products. Use text preprocessing techniques (e.g., stop word removal, stemming/lemmatization) to clean the data. Perform sentiment analysis to classify each review as positive, negative, or neutral. Apply topic modeling to identify the top N topics in the reviews. Explain the methodology for combining sentiment and topics to highlight major pain points and positive aspects. Justify your choice of N for topics.

Q. 2)    You are given a dataset containing news articles from different domains (e.g., politics, sports, technology). Preprocess the data to clean and tokenize the text. Use an appropriate NER model (e.g., SpaCy, Hugging Face Transformers) to extract entities such as PERSON, ORGANIZATION, LOCATION, and DATE. Justify your choice of the NER model. All results must be presented in a single Jupyter notebook.

Q. 3)    You have a dataset containing FAQs (questions and answers) for a customer support system. Use techniques such as cosine similarity (TF-IDF vectors) or semantic similarity (word embeddings). Build a text similarity model to match user queries with the most relevant FAQ. Use libraries such as gensim or sentence-transformers for embeddings. Present results and code in a single Jupyter notebook.

Q. 4)    You are given a dataset containing news articles labeled as fake or real. Preprocess the articles to clean and tokenize the text. Train a binary classification model (e.g., logistic regression, decision tree, or neural network) to classify articles as fake or real. Ensure reproducibility of results by providing all code and explanations in the notebook. Suggest how the model could be improved to handle new or unseen fake news.

Q. 5)    You are provided with a dataset of product reviews for multiple categories (e.g., electronics, clothing, books). Perform aspect-based sentiment analysis to identify sentiment towards specific product attributes (e.g., quality, price, design). Use topic modeling to extract relevant attributes for each product category. Use libraries such as Vader, TextBlob, or BERT for sentiment analysis. Explain the methodology and justify your findings

Q. 6)    You have been given a dataset of user queries and their corresponding intents (e.g., booking, cancellation, inquiry). Preprocess the text to handle variations in spelling, punctuation, and case. Train an intent classification model using machine learning or deep learning techniques.

Q. 7)    A dataset of news articles about a global event is provided. Combine titles and content of articles to identify the top N topics. Ensure N captures unique aspects of the event (e.g., causes, effects, reactions). Use visualizations to present the results.

Q. 8)    A dataset of movie reviews is provided, containing titles, summaries, and full reviews. Identify the top N topics using the full dataset. Focus on the most frequently discussed aspects of movies (e.g., plot, acting, direction). Explain the impact of combining information from summaries and full reviews.

Q. 9)    You have been provided with a dataset containing customer reviews for a product and corresponding technical specifications for the same product. Your objective is to analyze the dataset using topic modeling to identify the top N key topics discussed in the reviews and the specifications. Clean the text data to ensure the best results (e.g., remove stop words, punctuation, convert text to lowercase, etc.). Consider how to handle domain-specific words (e.g., brand names, product-specific terms) in your analysis. Use an appropriate topic modeling technique (e.g., Latent Dirichlet Allocation (LDA) or Non-Negative Matrix Factorization (NMF)) to analyze the cleaned text. Combine information effectively where necessary (e.g., from review titles and content or across similar topics in specifications). Present the top N most important topics for both customer reviews and technical specifications. For customer reviews, focus on the main pain points, preferences, or common suggestions from users. For technical specifications, highlight the most frequently emphasized features or capabilities. All results and corresponding code must be submitted in a single Jupyter Notebook (NLP_Assignment_Topic_Modeling.ipynb). Use libraries such as NLTK, spaCy, gensim, or scikit-learn as needed. Include visualizations to explain your findings (e.g., word clouds for each topic or bar charts of topic distributions). Justify how you selected the number of topics, N.

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