SIC Summary Chapter 7 Unit 3 PDF

SIC Summary Chapter 7 Unit 3 Classification Analysis Classification Analysis is basically a task of assigning a document or text to one or more predefined categories based on its content. In NLP, classification involves assigning predefined categories (or labels) to text based on its conte...

SIC Summary Chapter 7 Unit 3 Classification Analysis Classification Analysis is basically a task of assigning a document or text to one or more predefined categories based on its content. In NLP, classification involves assigning predefined categories (or labels) to text based on its content, e.g., is an e-mail is spam or not. The objective of classification analysis is therefore that, given a set of categories and a text sample, to determine the category that best fits the content of the text. Classification analysis in NLP enables machines to automatically sort, label, or make decisions based on textual content. With the proliferation of digital text in our modern world, from social media to official documents, these techniques play a vital role in organizing and making sense of vast amounts of information. Classification Analysis Imagine you're sorting a stack of newspapers into various categories: "Sports", "Politics", "Entertainment", and "Business". You'll classify each newspaper based on its headlines or content. If a headline reads "Stock Market Hits New Highs", you'll likely place it in the "Business" pile. Data Point: A single newspaper article. Features: Words or phrases in the article. Categories: Sports, Politics, Entertainment, Business. Classification Analysis Key Components in NLP Classification: Text Representation: Texts are transformed into numerical formats for machines to understand. Techniques include Bag-of-Words (BoW), TF-IDF, and word embeddings. Feature Importance: In NLP, the frequency and context of words can be significant. For instance, the word "goal" might be crucial in classifying a text as "Sports". Training & Testing: Similar to general classification, we split our dataset into training and testing sets. The model learns from the training set and is evaluated on the testing set. Evaluation: Accuracy, precision, recall, and F1-score are common metrics to assess the performance of NLP classifiers. Classification Analysis Challenges in NLP Classification Analysis Data Quality and Quantity Classification models require large, high-quality labeled datasets to perform well. If the dataset contains errors, missing values, or inconsistencies, the model might learn incorrect patterns, which leads to poor data leads to overfitting or underfitting, affecting accuracy. Feature Representation It cases where text data must be converted into numerical features, and the choice of representation (e.g., Bag-of-Words, TF-IDF, word embeddings) significantly impacts model performance. Bag-of-Words, for example, ignores context, while embeddings like Word2Vec require significant computational resources. The impact is that this inadequate representation can lead to loss of information and poor classification. Classification Analysis Challenges in NLP Classification Analysis Imbalanced Datasets If some classes have significantly more samples than others, the model tends to favor the dominant class. A dataset with 90% "spam" and 10% "not spam" emails may classify almost all emails as spam, which leads to biased predictions and poor performance on minority classes. Lack of Domain Adaptability NLP models trained on general-purpose datasets may struggle with domain-specific language. A classifier which is trained on news articles, for example, may fail to classify medical or technical texts accurately. This means that these models require additional training on domain-specific data, which may not always be available. Classification Analysis Challenges in NLP Classification Analysis Out-of-Vocabulary Words (OOV) Traditional models often fail to handle words not seen during training. Slang, typos, or new terms (e.g., "finfluencer") can confuse the model. This results in reduced performance on unseen or evolving vocabulary. Computational Costs Advanced NLP models like transformers require significant computational resources and time. Training a BERT-based classifier, for example, on a large dataset can take days on high-end GPUs. This high costs make it challenging for small organizations or individuals to implement classification analysis on a large scale. Refer to the document “Coding Examples Chapter 7 Unit 3” and the section “Slide 7: Classification Analysis Example” for a code snippet and associated explanation as far as Tokenization is concerned. Classification Analysis NLP Classification Example - Sentiment Analysis One popular application of classification in NLP is sentiment analysis. Objective: Given a product review, classify it as "Positive", "Neutral", or "Negative". Example: Review: "The battery life of this phone is fantastic!" Classification: Positive Classification Analysis Sentiment analysis is a common task in NLP, and there are libraries designed to make it simple. The TextBlob library in Python is commonly used for sentiment analysis, which provides easy sentiment analysis capabilities. It assigns a polarity score to a sentence, where: Positive polarity (> 0) indicates positive sentiment. Negative polarity (< 0) indicates negative sentiment. Neutral polarity (≈ 0) indicates a neutral sentiment. Classification Analysis Challenges in Sentiment Analysis (some of the examples here have been discussed already but is included for the sake of completeness) Context Understanding Sentiment depends heavily on the context of a sentence, which can be subtle or complex. For example, the sentence "I loved the food, but the service was awful." contains mixed sentiments. Such mixed sentiments affect models that they might incorrectly assign an overall sentiment. Sarcasm Detection Sarcasm and irony are difficult for models to detect. For example, the sentence "Oh great, another delay in delivery!" expresses negative sentiment despite positive wording. This can lead to misclassification of sentiments. Classification Analysis Ambiguity and Neutral Sentiments Neutral statements or ambiguous phrases can be hard to classify. For example, the sentence: "The product arrived." is neutral but might be incorrectly classified as positive or negative. Neutral and ambiguous statements can reduces classification accuracy in fine-grained analysis. Cultural and Linguistic Variations Sentiment varies across cultures and languages, even for similar expressions. For example, a phrase like "Not bad" may be neutral in one culture but positive in another. To address such issues, sentiment analysis models may require localization and retraining for different languages or regions. Classification Analysis Domain-Specific Challenges Sentiments expressed in specialized domains may require unique interpretations. The sentence "The pain was manageable.“, for example, may be positive in a medical context but negative in a general context. Models trained on generic datasets struggle with domain-specific nuances. Handling Negations Negations change the sentiment of a sentence, which can be hard to parse. Consider the sentence "The movie was not good" is negative, but simple models may misinterpret it as positive. This can result in sentiment misclassification. Classification Analysis Evolving Language Sentiment can be tied to slang, emojis, or informal language that evolves over time. Consider the sentence "This is 🔥" (positive) or "meh" (negative) may not be understood by outdated models. The evolving nature of language means that models require frequent updates to handle new trends. Subjectivity in Sentiment Sentiment is inherently subjective and varies between individuals. Consider a review like "The food was spicy“, which could be positive or negative, depending on the person's preferences. Subjectivity in sentiment limits the accuracy of universal sentiment analysis models. Classification Analysis Addressing These Challenges Better Data: Use diverse and high-quality datasets. Apply data augmentation techniques to handle imbalances. Advanced Models: Use context-aware models like BERT or GPT for better understanding of ambiguity and polysemy. Train models specifically for domain-specific applications. Preprocessing: Handle negations, slang, and out-of-vocabulary words through tokenization and embeddings. Localization: Customize models for specific languages, cultures, or regions. Continuous Learning: Regularly update models to handle evolving language and new trends. Classification Analysis Real-world NLP Classification Applications Spam Detection: Classifying emails as spam or not based on their content. Topic Labeling: Identifying the main topic of news articles or blogs. Language Detection: Determining the language of the text. Intent Recognition: In chatbots, determining the user's intent from their message. Refer to the document “Coding Examples Chapter 7 Unit 3” and the section “Slide 15: Sentiment Analysis Example” for a code snippet and associated explanation as far as Tokenization is concerned. Classification Analysis Topic Modelling Topic modeling is an unsupervised machine learning technique used in Natural Language Processing (NLP) to identify hidden topics or themes in a collection of text documents. It works by analyzing word patterns and grouping words that frequently appear together, revealing the underlying structure of the data. Two popular topic modelling approaches are Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA). Classification Analysis Topic Modelling Latent Semantic Analysis (LSA) converts text data into a matrix where rows represent documents, and columns represent words. LSA uses Singular Value Decomposition (SVD) (recall that it is a mathematical technique, to reduce the dimensionality of the matrix). LSA groups words and documents into "topics" based on patterns in the reduced matrix. Imagine boiling down a book to a summary. LSA condenses large amounts of text into core ideas without fully understanding their meaning. Strengths: Simple to implement. Captures some semantic relationships. Weaknesses: Struggles with polysemy (multiple meanings of words). Assumes linear relationships, which limits its ability to capture complex patterns. Classification Analysis Topic Modelling Latent Dirichlet Allocation (LDA) views documents as mixtures of topics and topics as mixtures of words. LDA assigns probabilities to words belonging to specific topics and documents belonging to a mix of topics. It then iteratively adjusts these probabilities to improve topic assignments. Think of a soup where different ingredients (words) are mixed to create different flavors (topics). LDA identifies which ingredients are dominant in each flavor. Strengths: LDA generates probabilistic results, making it easier to interpret the strength of topics. Works well on larger datasets. Weaknesses: LDA assumes words in a document are independent (bag-of-words assumption). Requires significant tuning to get meaningful topics. Classification Analysis Topic Modelling Aspect LSA LDA Singular Value Probabilistic modeling Mathematical Basis Decomposition (SVD) with Dirichlet priors Topics with Topics as word Output probabilities for each clusters word Better topic Simple and efficient Strength coherence and for small datasets interpretability Struggles with Computationally Weakness polysemy and sparse expensive, requires data tuning Context Awareness Low Higher compared to LSA Classification Analysis Topic Modelling LSA is therefore best for small, simpler datasets; works by reducing dimensionality mathematically. LDA is preferred for larger datasets; provides a probabilistic framework that assigns words and documents to multiple topics. Classification Analysis Topic Modelling Challenges and Limitations of Topic Modeling Interpretability of Topics: Topics generated may not always make sense or align with human understanding. An example would be that a topic modelling algorithm may mix unrelated words if the dataset is noisy. Solution: Manually review and label topics, fine-tune preprocessing steps, or experiment with the number of topics. Classification Analysis Topic Modelling Challenges and Limitations of Topic Modeling Determining the Optimal Number of Topics: Deciding how many topics to generate can be subjective and requires trial and error can be challenging. Solution: Use evaluation metrics like coherence scores or perplexity to determine the best number. Handling Sparse Data Topic modeling struggles with sparse datasets where documents contain few words. Solution: Use advanced models like BERTopic or augment the dataset. Classification Analysis Topic Modelling Capturing Context Traditional models like LDA and LSA struggle to capture semantic context (e.g., polysemy). Solution: Use contextual embeddings (e.g., BERT) for better topic extraction. Dependence on Preprocessing Results are sensitive to how the text is preprocessed (e.g., stopword removal, stemming). Solution: Experiment with preprocessing techniques and avoid over-simplifying text. Classification Analysis Topic Modelling Scalability The challenge is that processing large datasets can be computationally expensive. Solution: Use scalable frameworks (e.g., Gensim, Spark). Classification Analysis Topic Modelling How to Address These Challenges Advanced Models: Use transformer-based models (e.g., BERTopic, BERT embeddings) to address context and semantic relationships. Domain-Specific Training: Train on data tailored to your domain (e.g., healthcare, finance). Hybrid Approaches: Combine topic modeling with clustering techniques (e.g., k-means with LDA). Hyperparameter Tuning: Experiment with the number of topics, iterations, and alpha/beta hyperparameters for LDA. Evaluation Metrics: Use coherence scores and human validation to assess topic quality. Classification Analysis Topic Modelling Real-World Applications of Topic Modeling Customer Feedback Analysis: Extract key themes from customer reviews to identify issues or popular features. Content Recommendation: Group similar articles or documents to recommend content based on user interest. Social Media Insights: Analyze trends or public sentiment by extracting themes from social media posts. Legal Document Analysis: Summarize large volumes of legal text into key topics for case research. Academic Research: Group similar research papers or extract main themes from large datasets. Classification Analysis Topic Modelling Refer to the document “Coding Examples Chapter 7 Unit 3” and the section “Slide 21: Topic Modelling Example” for a code snippet and associated explanation as far as Tokenization is concerned. Questions?