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Presentación titulada 'Tarea 3 – NLP y Representación de Texto – Patricio Gajardo González'. Contiene 5 diapositivas en formato 16:9, estilo creativo-profesional con gradientes azul y violeta, íconos y elementos visuales de inteligencia artificial y ciencia de datos. Slide 1: Portada con título, subtítulo, objetivo y autor. Slide 2: Definición del problema – clasificación de sentimiento en tweets de aerolíneas. Slide 3: Descripción del dataset Airline Sentiment Analysis (Kaggle). Slide 4: Justificación de la elección del dataset y compatibilidad con arquitecturas (Modelos 1, 2 y 3). Slide 5: Compatibilidad con las arquitecturas, explicando TF-IDF, embeddings, LSTM y BERT.
Overview of NLP Task 3 on sentiment classification of ~14K airline tweets from Kaggle dataset. Covers problem definition, dataset details, justification, and compatibility with TF-IDF, embeddings, LST
This title slide, labeled "Portada," features the main heading "Tarea 3 – NLP y Representación de Texto" (Assignment 3 – NLP and Text Representation). Its subtitle reads "Clasificación de Sentimiento en Tweets" (Sentiment Classification in Tweets).
Clasificación de Sentimiento en Tweets.
The slide defines the problem as sentiment classification in airline tweets, detecting positive, negative, or neutral emotions. It focuses on analyzing user opinions about air services using NLP techniques on short, contextual text.
The slide describes a Kaggle dataset of ~14,000 tweets about US airlines, with columns for text, sentiment (positive/negative/neutral), airline, and reason. Sentiments are 62% negative, making it ideal for training NLP models.
The slide justifies selecting a public, clean dataset focused on real text, with an ideal size for efficient prototyping and relevance to common social media analysis problems. It is fully compatible with Models 1 (TF-IDF), 2 (Embeddings+LSTM), and 3 (BERT).
Source: Airline Sentiment Analysis (Kaggle)
The left column covers Models 1 (TF-IDF bag-of-words vectorization for simple, efficient numeric features) and 2 (embeddings + LSTM for contextual tweet sequences and temporal dependencies). The right column describes Model 3 (pre-trained BERT transformer embeddings for deep bidirectional sentiment analysis on airline datasets).
| Modelos 1 y 2 | Modelo 3 (BERT) |
|---|---|
| Modelo 1 (TF-IDF): Vectorización bag-of-words para features numéricas simples y eficientes. Modelo 2: Embeddings + LSTM para procesar secuencias contextuales, capturando dependencias temporales en tweets. | Embeddings pre-entrenados de transformer para comprensión profunda del sentimiento mediante atención bidireccional y contexto rico en el dataset de aerolíneas. |
Source: Airline Sentiment Analysis (Kaggle)
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