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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.
Objective: Clean the dataset, perform sentiment analysis, and topic modeling. Steps: Text Preprocessing: Remove stop words: Remove common words (e.g., “the,” “is,” etc.) that don’t contribute to meaning. Stemming/Lemmatization: Reduce words to their base form (e.g., “running” → “run”). Tokenization: Break text into words or sentences. Sentiment Analysis: Use a sentiment analysis tool or model (like TextBlob or VADER) to classify reviews as positive, negative, or neutral. Train a classifier or use a pre-trained model (e.g., BERT for sentiment). Topic Modeling: Use LDA (Latent Dirichlet Allocation) or NMF (Non-negative Matrix Factorization) to extract N topics. Choosing N: Select N based on domain knowledge and model coherence scores. Typically, start with a range of topics (e.g., 5 to 10). Combine Sentiment and Topics: Once the sentiment is classified, map topics to positive and negative sentiments to highlight pain points and positive aspects (e.g., "price" may be negative in reviews about high cost).
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.
Objective: Extract entities such as PERSON, ORGANIZATION, LOCATION, and DATE. Steps: Data Preprocessing: Clean and tokenize the text (lowercase, remove punctuation, etc.). NER using SpaCy/Hugging Face: Use spaCy or Hugging Face Transformers (BERT-based models) for Named Entity Recognition. Justification for NER model: SpaCy’s pre-trained models are fast and reliable for general-purpose NER. Hugging Face models may be better for complex entities or domain-specific data. Presentation: Display extracted entities in a table or bar chart (showing counts of each type of entity).
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.
Objective: Match user queries to the most relevant FAQs. Steps: Preprocessing: Clean the FAQs dataset by removing stop words, punctuation, and converting text to lowercase. Cosine Similarity or Embeddings: TF-IDF: Create vectors for each FAQ and user query using TF-IDF, then compute cosine similarity. Word Embeddings: Use sentence-transformers to convert queries and FAQs into vectors, then compute similarity. Present matches between queries and most relevant FAQ.
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.
Objective: Classify news articles as fake or real. Steps: Text Preprocessing: Tokenize the text and remove stop words. Feature Extraction: Convert text data into numerical form using TF-IDF or word embeddings. Model Training: Train a binary classification model (e.g., logistic regression, decision tree, or neural network) to classify articles. Evaluate using accuracy, precision, recall, and F1 score. Model Improvement: Consider techniques like data augmentation, ensemble models, or transfer learning (using pre-trained models) to improve performance on new or unseen data.
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
Objective: Identify sentiment towards product attributes (e.g., quality, price, design). Steps: Text Preprocessing: Clean the text data by removing stop words and performing lemmatization. Sentiment Analysis: Use a sentiment analysis library like VADER, TextBlob, or BERT to extract sentiment for different product aspects. Topic Modeling: Use LDA to extract key product attributes (e.g., “quality,” “price,” etc.). Methodology: Analyze sentiment for each attribute and map them to topics for a deeper understanding of customer sentiment.
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.
Objective: Classify user queries based on intents (e.g., booking, cancellation). Steps: Preprocessing: Normalize text (handling case, spelling variations, punctuation). Model Training: Use TF-IDF or word embeddings to represent user queries and train a classification model (e.g., logistic regression, neural network) to predict the intent. Evaluation: Use precision, recall, and F1 score for model evaluation.
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.
Objective: Identify top N topics from news articles about a global event. Steps: Text Preprocessing: Clean and tokenize text. Topic Modeling: Use LDA to extract topics. Select N based on the uniqueness of topics and coherence scores. Visualization: Present the topics using word clouds or bar charts for easy understanding.
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.
Objective: Identify topics discussed in movie reviews (e.g., plot, acting, direction). Steps: Text Preprocessing: Clean and tokenize text, merging titles and summaries with reviews. Topic Modeling: Use LDA or NMF for topic extraction. Explanation: Discuss the impact of including summaries with full reviews in identifying top topics.
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.
Objective: Identify top topics from customer reviews and technical specifications. Steps: Text Cleaning: Preprocess text (remove stop words, punctuation, lowercase). Topic Modeling: Use LDA or NMF to analyze customer reviews and technical specifications separately. Analysis: Focus on pain points and preferences in reviews, and highlight key features in technical specs. Domain-Specific Terms: Use techniques like custom stop words or domain-specific embeddings to handle brand names and product-specific terms effectively. Justifying N: Use model coherence scores or manual inspection to select the optimal number of topics.
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