{"id":34167,"date":"2025-10-13T11:10:40","date_gmt":"2025-10-13T09:10:40","guid":{"rendered":"https:\/\/www.codemotion.com\/magazine\/?p=34167"},"modified":"2025-10-13T11:30:57","modified_gmt":"2025-10-13T09:30:57","slug":"primeros-pasos-en-pln-y-el-analisis-de-sentimientos","status":"publish","type":"post","link":"https:\/\/www.codemotion.com\/magazine\/es\/inteligencia-artificial\/primeros-pasos-en-pln-y-el-analisis-de-sentimientos\/","title":{"rendered":"Primeros Pasos en PLN y el An\u00e1lisis de Sentimientos"},"content":{"rendered":"\n<p>\u00bfAlguna vez te has preguntado c\u00f3mo las m\u00e1quinas logran \u201cleer\u201d y \u201centender\u201d lo que sientes? En la era digital, la cantidad de texto que generamos es asombrosa: tweets, rese\u00f1as, comentarios\u2026 Detr\u00e1s de cada palabra, hay una <strong>opini\u00f3n<\/strong> o una <strong>emoci\u00f3n<\/strong>. El <strong>Procesamiento de Lenguaje Natural (PLN) o NLP (Natural Language Processing)<\/strong> es el campo de la Inteligencia Artificial que nos permite desbloquear ese tesoro de datos. Y la mejor forma de empezar es con el <strong>An\u00e1lisis de Sentimientos<\/strong> (tambi\u00e9n conocido como <em>Opinion Mining<\/em>).<\/p>\n\n\n\n<h2 class=\"wp-block-heading has-text-align-center\" id=\"h-que-es-el-analisis-de-sentimientos\"><strong>\u00bfQu\u00e9 es el An\u00e1lisis de Sentimientos?<\/strong><\/h2>\n\n\n\n<p>El <strong>An\u00e1lisis de Sentimientos<\/strong> es la aplicaci\u00f3n del PLN que busca identificar, extraer, cuantificar y estudiar los estados afectivos y la informaci\u00f3n subjetiva. El <strong><a href=\"https:\/\/www.codemotion.com\/magazine\/es\/inteligencia-artificial\/deep-learning-y-redes-neuronales-una-guia-completa\/\">NLP<\/a><\/strong> es el puente entre el lenguaje humano y las m\u00e1quinas. En t\u00e9rminos sencillos, es ense\u00f1ar a un sistema inform\u00e1tico a determinar si un fragmento de texto expresa una opini\u00f3n <strong>positiva<\/strong>, <strong>negativa<\/strong> o <strong>neutral<\/strong>.<\/p>\n\n\n\n<h2 class=\"wp-block-heading has-text-align-center\" id=\"h-conceptos-basicos-antes-de-nbsp-empezar\"><strong>Conceptos b\u00e1sicos antes de&nbsp;empezar<\/strong><\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Tokenizaci\u00f3n<\/strong>: dividir un texto en palabras o frases.<\/li>\n\n\n\n<li><strong>Lematizaci\u00f3n\/Stemming<\/strong>: reducir palabras a su forma base (ej. <em>corriendo<\/em> \u2192 <em>correr<\/em>).<\/li>\n\n\n\n<li><strong>Vectorizaci\u00f3n<\/strong>: transformar texto en n\u00fameros para que un modelo pueda procesarlo.<\/li>\n\n\n\n<li><strong>Clasificaci\u00f3n<\/strong>: asignar etiquetas (positivo, negativo, neutral) a un texto.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading has-text-align-center\" id=\"h-la-triada-emocional-nbsp\"><strong>La Tr\u00edada Emocional&nbsp;<\/strong><\/h2>\n\n\n\n<p>En su forma m\u00e1s b\u00e1sica, el an\u00e1lisis de sentimientos clasifica el texto en una de estas tres categor\u00edas:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Positivo:<\/strong> Expresa satisfacci\u00f3n, agrado, apoyo, etc. (<em>Ej.: \u201cEste producto es incre\u00edble y super\u00f3 mis expectativas.\u201d<\/em>)<\/li>\n\n\n\n<li><strong>Negativo:<\/strong> Expresa insatisfacci\u00f3n, disgusto, cr\u00edtica, etc. (<em>Ej.: \u201cEl servicio fue lento y la calidad es decepcionante.\u201d<\/em>)<\/li>\n\n\n\n<li><strong>Neutral&nbsp;:<\/strong> Expresa hechos, informaci\u00f3n objetiva o una opini\u00f3n sin carga emocional clara. (<em>Ej.: \u201cLa reuni\u00f3n est\u00e1 programada para el martes a las 10 a.m.\u201d<\/em>)<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading has-text-align-center\" id=\"h-tu-ruta-de-inicio-en-pln-con-analisis-de-sentimientos\"><strong>Tu Ruta de Inicio en PLN con An\u00e1lisis de Sentimientos<\/strong><\/h2>\n\n\n\n<p>Empezar en el mundo del PLN puede parecer abrumador, pero el An\u00e1lisis de Sentimientos ofrece un camino claro y gratificante. \u00a1Aqu\u00ed te muestro los pasos fundamentales!<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-1-preprocesamiento-del-texto-la-limpieza-nbsp-inicial\"><strong>1. Preprocesamiento del Texto: La Limpieza&nbsp;Inicial<\/strong><\/h3>\n\n\n\n<p>Antes de que una m\u00e1quina pueda \u201centender\u201d el texto, este debe ser limpiado y estandarizado. Este paso es <strong>cr\u00edtico<\/strong>:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Tokenizaci\u00f3n:<\/strong> Dividir el texto en unidades m\u00e1s peque\u00f1as llamadas <em>tokens<\/em> (palabras o frases). <em>Ej.:<\/em> \u201cLa comida es buena.\u201d \u2192 [\u2018La\u2019, \u2018comida\u2019, \u2018es\u2019, \u2018buena\u2019, \u2018.\u2019]<\/li>\n\n\n\n<li><strong>Eliminaci\u00f3n de <em>Stop Words<\/em><\/strong>: Descartar palabras comunes que no a\u00f1aden significado emocional (ej. \u2018el\u2019, \u2018la\u2019, \u2018un\u2019, \u2018y\u2019).<\/li>\n\n\n\n<li><strong>Lematizaci\u00f3n\/Stemming:<\/strong> Reducir las palabras a su ra\u00edz o forma base para que el modelo las reconozca como la misma entidad. <em>Ej.:<\/em> \u2018corriendo\u2019, \u2018correr\u00e1\u2019 \u2192 \u2018corr\u2019 (Stemming) o \u2018correr\u2019 (Lematizaci\u00f3n).<\/li>\n<\/ul>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter\"><a class=\"alt=&quot;Modelo Naive Bayes aplicado al an\u00e1lisis emocional del texto&quot;\" href=\"https:\/\/cdn.you.com\/youagent-images\/gpt-image-1\/53d761f9-6ebb-4c4b-90a1-430026ad6441.png\" target=\"_blank\" rel=\" noreferrer noopener\"><img decoding=\"async\" src=\"https:\/\/cdn-images-1.medium.com\/max\/800\/1*baWw3NUjxy9fNREqLBdaGA.png\" alt=\"\"\/><\/a><\/figure><\/div>\n\n\n<h3 class=\"wp-block-heading\" id=\"h-2-representacion-del-texto-de-palabras-a-nbsp-numeros\"><strong>2. Representaci\u00f3n del Texto: De Palabras a&nbsp;N\u00fameros<\/strong><\/h3>\n\n\n\n<p>Las computadoras solo entienden n\u00fameros. Debemos convertir nuestros <em>tokens<\/em> en un formato num\u00e9rico que el algoritmo de <em>Machine Learning<\/em> pueda procesar.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Bag-of-Words (BoW):<\/strong> Un m\u00e9todo simple donde se cuenta la frecuencia de cada palabra en el documento. Esto ignora el orden, pero conserva la informaci\u00f3n de <em>qu\u00e9<\/em> palabras est\u00e1n presentes.<\/li>\n\n\n\n<li><strong>TF-IDF (Frecuencia de T\u00e9rmino\u200a\u2014\u200aFrecuencia Inversa de Documento):<\/strong> Asigna pesos a las palabras. Un peso alto significa que la palabra es importante en un documento espec\u00edfico, pero no es com\u00fan en toda la colecci\u00f3n. Esto es genial para resaltar t\u00e9rminos clave.<\/li>\n\n\n\n<li><strong>Word Embeddings (Embeddings de Palabras):<\/strong> T\u00e9cnicas m\u00e1s avanzadas (como <strong>Word2Vec<\/strong> o modelos basados en <strong>Transformers<\/strong> como <strong>BERT<\/strong>) que mapean palabras en vectores de alta dimensi\u00f3n. Estos vectores capturan el <strong>contexto sem\u00e1ntico<\/strong> de la palabra, \u00a1permitiendo a la m\u00e1quina entender que \u2018rey\u2019 y \u2018reina\u2019 est\u00e1n relacionados!<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-3-modelado-elige-tu-algoritmo-nbsp\"><strong>3. Modelado: Elige tu Algoritmo&nbsp;<\/strong><\/h3>\n\n\n\n<p>Con los datos listos, es hora de entrenar al modelo de clasificaci\u00f3n:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Enfoque Basado en Lexicones:<\/strong> Utiliza listas predefinidas de palabras que ya est\u00e1n etiquetadas como positivas o negativas (<em>lexicones<\/em>). Simplemente se suman los pesos de las palabras del texto. \u00a1Es simple y r\u00e1pido!<\/li>\n\n\n\n<li><strong>Enfoque Basado en <em>Machine Learning<\/em>:<\/strong> Entrenar un clasificador (como <strong>Regresi\u00f3n Log\u00edstica<\/strong>, <strong>Na\u00efve Bayes<\/strong>, o <strong>Support Vector Machines<\/strong>) en un conjunto de datos previamente etiquetado (ej. rese\u00f1as que <em>ya<\/em> sabemos si son positivas o negativas). Es m\u00e1s preciso, pero requiere datos de entrenamiento.<\/li>\n\n\n\n<li><strong>Enfoque Basado en <em>Deep Learning<\/em>:<\/strong> Usar Redes Neuronales Recurrentes (RNNs) o, m\u00e1s popularmente, modelos basados en <strong>Transformers<\/strong>. Estos son el <em>estado del arte<\/em> y ofrecen la mayor precisi\u00f3n al capturar matices complejos y el orden de las palabras.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading has-text-align-center\" id=\"h-herramientas-esenciales-para-nbsp-empezar\"><strong>Herramientas Esenciales para&nbsp;Empezar<\/strong><\/h2>\n\n\n\n<p>No tienes que empezar desde cero. El ecosistema de PLN es robusto y amigable para principiantes:<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter\"><img loading=\"lazy\" decoding=\"async\" width=\"710\" height=\"301\" src=\"https:\/\/www.codemotion.com\/magazine\/wp-content\/uploads\/2025\/10\/1F4O9DCx3ievOFHWKi2zi7Q.jpg\" alt=\"\" class=\"wp-image-34206\" srcset=\"https:\/\/www.codemotion.com\/magazine\/wp-content\/uploads\/2025\/10\/1F4O9DCx3ievOFHWKi2zi7Q.jpg 710w, https:\/\/www.codemotion.com\/magazine\/wp-content\/uploads\/2025\/10\/1F4O9DCx3ievOFHWKi2zi7Q-300x127.jpg 300w\" sizes=\"auto, (max-width: 710px) 100vw, 710px\" \/><\/figure><\/div>\n\n\n<h3 class=\"wp-block-heading\" id=\"h-ejemplo-1-analisis-de-sentimientos-con-nbsp-textblob\"><strong>Ejemplo 1: An\u00e1lisis de sentimientos con&nbsp;<\/strong><code><strong>TextBlob<\/strong><\/code><\/h3>\n\n\n\n<p><code>TextBlob<\/code> es una librer\u00eda sencilla para empezar en NLP.<\/p>\n\n\n<pre class=\"wp-block-code\" aria-describedby=\"shcb-language-1\" data-shcb-language-name=\"PHP\" data-shcb-language-slug=\"php\"><span><code class=\"hljs language-php\">from textblob import TextBlob\n\n<span class=\"hljs-comment\"># Ejemplos de frases<\/span>\nfrases = &#91;\n    <span class=\"hljs-string\">\"Me encanta este producto, es incre\u00edble!\"<\/span>,\n    <span class=\"hljs-string\">\"El servicio fue terrible, no lo recomiendo.\"<\/span>,\n    <span class=\"hljs-string\">\"Est\u00e1 bien, pero podr\u00eda ser mejor.\"<\/span>\n]\n\n<span class=\"hljs-keyword\">for<\/span> frase in frases:\n    blob = TextBlob(frase)\n    sentimiento = blob.sentiment.polarity  <span class=\"hljs-comment\"># valor entre -1 (negativo) y 1 (positivo)<\/span>\n    \n    <span class=\"hljs-keyword\">if<\/span> sentimiento &gt; <span class=\"hljs-number\">0<\/span>:\n        etiqueta = <span class=\"hljs-string\">\"Positivo\"<\/span>\n    elif sentimiento &lt; <span class=\"hljs-number\">0<\/span>:\n        etiqueta = <span class=\"hljs-string\">\"Negativo\"<\/span>\n    <span class=\"hljs-keyword\">else<\/span>:\n        etiqueta = <span class=\"hljs-string\">\"Neutral\"<\/span>\n    \n    <span class=\"hljs-keyword\">print<\/span>(f<span class=\"hljs-string\">\"Texto: {frase}\"<\/span>)\n    <span class=\"hljs-keyword\">print<\/span>(f<span class=\"hljs-string\">\"Polaridad: {sentimiento:.2f} \u2192 {etiqueta}\\n\"<\/span>)<\/code><\/span><small class=\"shcb-language\" id=\"shcb-language-1\"><span class=\"shcb-language__label\">Code language:<\/span> <span class=\"shcb-language__name\">PHP<\/span> <span class=\"shcb-language__paren\">(<\/span><span class=\"shcb-language__slug\">php<\/span><span class=\"shcb-language__paren\">)<\/span><\/small><\/pre>\n\n\n<p><strong><em>Explicaci\u00f3n:<\/em><\/strong> Lo anterior con <code>sentiment.polarity<\/code> devuelve un n\u00famero entre -1 y 1. Seg\u00fan el valor, clasificamos el texto en positivo, negativo o neutral.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter\"><a class=\"alt=&quot;Visualizaci\u00f3n de polaridad en PLN y an\u00e1lisis de sentimientos&quot;\" href=\"https:\/\/cdn.you.com\/youagent-images\/gpt-image-1\/f3fdd166-26aa-4ff4-a6f5-14555b3902de.png\" target=\"_blank\" rel=\" noreferrer noopener\"><img decoding=\"async\" src=\"https:\/\/cdn-images-1.medium.com\/max\/800\/1*nMn9KcMyd5qOuv93qQSm-Q.png\" alt=\"\"\/><\/a><\/figure><\/div>\n\n\n<h3 class=\"wp-block-heading\" id=\"h-ejemplo-2-usando-nltk-y-un-clasificador-naive-nbsp-bayes\"><strong>Ejemplo 2: Usando <\/strong><code><strong>NLTK<\/strong><\/code><strong> y un clasificador Naive&nbsp;Bayes<\/strong><\/h3>\n\n\n\n<p>Si quieres un poco m\u00e1s de control, puedes entrenar tu propio modelo.<\/p>\n\n\n<pre class=\"wp-block-code\" aria-describedby=\"shcb-language-2\" data-shcb-language-name=\"PHP\" data-shcb-language-slug=\"php\"><span><code class=\"hljs language-php\">import nltk\nfrom nltk.corpus import movie_reviews\nimport random\n\n<span class=\"hljs-comment\"># Descargar dataset de rese\u00f1as de pel\u00edculas<\/span>\nnltk.download(<span class=\"hljs-string\">'movie_reviews'<\/span>)\n\n<span class=\"hljs-comment\"># Crear dataset con etiquetas<\/span>\ndocumentos = &#91;(<span class=\"hljs-keyword\">list<\/span>(movie_reviews.words(fileid)), category)\n              <span class=\"hljs-keyword\">for<\/span> category in movie_reviews.categories()\n              <span class=\"hljs-keyword\">for<\/span> fileid in movie_reviews.fileids(category)]\n\nrandom.shuffle(documentos)\n\n<span class=\"hljs-comment\"># Extraer las 2000 palabras m\u00e1s frecuentes<\/span>\nall_words = nltk.FreqDist(w.lower() <span class=\"hljs-keyword\">for<\/span> w in movie_reviews.words())\npalabras_caracteristicas = <span class=\"hljs-keyword\">list<\/span>(all_words)&#91;:<span class=\"hljs-number\">2000<\/span>]\n\ndef extractor_caracteristicas(doc):\n    palabras_doc = set(doc)\n    <span class=\"hljs-keyword\">return<\/span> {palabra: (palabra in palabras_doc) <span class=\"hljs-keyword\">for<\/span> palabra in palabras_caracteristicas}\n\n<span class=\"hljs-comment\"># Entrenar clasificador<\/span>\ncaracteristicas = &#91;(extractor_caracteristicas(d), c) <span class=\"hljs-keyword\">for<\/span> (d, c) in documentos]\ntrain_set, test_set = caracteristicas&#91;<span class=\"hljs-number\">100<\/span>:], caracteristicas&#91;:<span class=\"hljs-number\">100<\/span>]\nclasificador = nltk.NaiveBayesClassifier.train(train_set)\n\n<span class=\"hljs-comment\"># Evaluar<\/span>\n<span class=\"hljs-keyword\">print<\/span>(<span class=\"hljs-string\">\"Precisi\u00f3n:\"<\/span>, nltk.classify.accuracy(clasificador, test_set))\n\n<span class=\"hljs-comment\"># Probar con un texto nuevo<\/span>\ntexto = <span class=\"hljs-string\">\"This movie was fantastic! I loved it.\"<\/span>\n<span class=\"hljs-keyword\">print<\/span>(clasificador.classify(extractor_caracteristicas(texto.split())))<\/code><\/span><small class=\"shcb-language\" id=\"shcb-language-2\"><span class=\"shcb-language__label\">Code language:<\/span> <span class=\"shcb-language__name\">PHP<\/span> <span class=\"shcb-language__paren\">(<\/span><span class=\"shcb-language__slug\">php<\/span><span class=\"shcb-language__paren\">)<\/span><\/small><\/pre>\n\n\n<p><strong><em>Explicaci\u00f3n:<\/em><\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Usamos un <a href=\"https:\/\/www.codemotion.com\/magazine\/es\/inteligencia-artificial\/machine-learning-para-principiantes-iniciar-y-dominar-la-ia\/\">dataset<\/a> de rese\u00f1as de pel\u00edculas ya etiquetadas.<\/li>\n\n\n\n<li>Extraemos palabras frecuentes como caracter\u00edsticas.<\/li>\n\n\n\n<li>Entrenamos un clasificador Naive Bayes.<\/li>\n\n\n\n<li>Probamos con un texto nuevo para ver si lo clasifica como positivo o negativo.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-que-sigue-despues-de-estos-primeros-nbsp-pasos\"><strong>\u00bfQu\u00e9 sigue despu\u00e9s de estos primeros&nbsp;pasos?<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Explorar modelos pre-entrenados como <strong>BERT<\/strong> o <strong>DistilBERT<\/strong> con <code>transformers<\/code>.<\/li>\n\n\n\n<li>Aplicar an\u00e1lisis de sentimientos en <strong>redes sociales<\/strong> (ej. tweets).<\/li>\n\n\n\n<li>Combinar NLP con <strong>visualizaci\u00f3n de datos<\/strong> para mostrar tendencias de opini\u00f3n.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading has-text-align-center\" id=\"h-el-reto-real-superando-la-ironia-y-la-negacion\"><strong>\u00a1El Reto Real: Superando la Iron\u00eda y la Negaci\u00f3n!<\/strong><\/h2>\n\n\n\n<p>El PLN no es perfecto y el espa\u00f1ol, con su riqueza, presenta desaf\u00edos \u00fanicos:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>Sarcasmo e Iron\u00eda:<\/strong> Un modelo simple puede leer \u201cEl tel\u00e9fono es tan r\u00e1pido como un caracol\u201d y clasificarlo como neutral o incluso positivo (si solo mira \u2018r\u00e1pido\u2019). \u00a1El contexto es crucial!<\/li>\n\n\n\n<li><strong>Doble Negaci\u00f3n:<\/strong> Frases como \u201cNo me disgusta del todo\u201d son complicadas de interpretar correctamente.<\/li>\n\n\n\n<li><strong>Ambig\u00fcedad:<\/strong> \u201cMe gust\u00f3 la trama, pero el final fue aburrido.\u201d (Implica tanto positivo como negativo). Aqu\u00ed el an\u00e1lisis por frases (<em>Aspect-Based Sentiment Analysis<\/em>) es la soluci\u00f3n.<\/li>\n<\/ol>\n\n\n\n<p>El An\u00e1lisis de Sentimientos no es solo un ejercicio acad\u00e9mico; es una herramienta de negocios <strong>poderos\u00edsima<\/strong>. Permite a las empresas escuchar la \u201cvoz del cliente\u201d a escala masiva y en tiempo real, mejorando productos y servicios. Es la puerta de entrada perfecta al mundo del NLP:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Es intuitivo (todos entendemos qu\u00e9 es positivo o negativo).<\/li>\n\n\n\n<li>Tiene aplicaciones inmediatas en marketing, soporte, educaci\u00f3n y comunidades.<\/li>\n\n\n\n<li>Te permite escalar desde librer\u00edas simples como <code>TextBlob<\/code> hasta modelos de \u00faltima generaci\u00f3n como <strong>transformers<\/strong>.<\/li>\n<\/ul>\n\n\n\n<p>El lenguaje humano es complejo, pero con estas herramientas ya tienes un mapa para empezar a explorarlo. \u00a1As\u00ed que desempolva tu Python y sum\u00e9rgete! El mundo de las palabras te espera.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter\"><a class=\"alt=&quot;Ejemplo de an\u00e1lisis de sentimientos con PLN usando TextBlob&quot;\" href=\"https:\/\/cdn.you.com\/youagent-images\/gpt-image-1\/fc4d5aa2-db1b-4a9f-a713-a9c7dc07ca73.png\" target=\"_blank\" rel=\" noreferrer noopener\"><img decoding=\"async\" src=\"https:\/\/cdn-images-1.medium.com\/max\/800\/1*zNlrC8A4bf1WGL5spxmQLg.png\" alt=\"\"\/><\/a><\/figure><\/div>","protected":false},"excerpt":{"rendered":"<p>\u00bfAlguna vez te has preguntado c\u00f3mo las m\u00e1quinas logran \u201cleer\u201d y \u201centender\u201d lo que sientes? En la era digital, la cantidad de texto que generamos es asombrosa: tweets, rese\u00f1as, comentarios\u2026 Detr\u00e1s de cada palabra, hay una opini\u00f3n o una emoci\u00f3n. El Procesamiento de Lenguaje Natural (PLN) o NLP (Natural Language Processing) es el campo de&#8230; <a class=\"more-link\" href=\"https:\/\/www.codemotion.com\/magazine\/es\/inteligencia-artificial\/primeros-pasos-en-pln-y-el-analisis-de-sentimientos\/\">Read more<\/a><\/p>\n","protected":false},"author":313,"featured_media":34207,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_editorskit_title_hidden":false,"_editorskit_reading_time":0,"_editorskit_is_block_options_detached":false,"_editorskit_block_options_position":"{}","_uag_custom_page_level_css":"","_genesis_hide_title":false,"_genesis_hide_breadcrumbs":false,"_genesis_hide_singular_image":false,"_genesis_hide_footer_widgets":false,"_genesis_custom_body_class":"","_genesis_custom_post_class":"","_genesis_layout":"","footnotes":""},"categories":[10610,10598],"tags":[10664,12922],"collections":[12986,12990],"class_list":{"0":"post-34167","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-aprendizaje-automatico","8":"category-inteligencia-artificial","9":"tag-ia","10":"tag-machine-learning-es","11":"collections-ai-es","12":"collections-machine-learning-es","13":"entry"},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v26.9 (Yoast SEO v26.9) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>PLN y An\u00e1lisis de Sentimientos: Gu\u00eda para Principiantes<\/title>\n<meta name=\"description\" content=\"Aprende PLN y an\u00e1lisis de sentimientos desde cero. 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Descubre herramientas como TextBlob y NLTK para clasificar emociones en texto.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.codemotion.com\/magazine\/es\/inteligencia-artificial\/primeros-pasos-en-pln-y-el-analisis-de-sentimientos\/\" \/>\n<meta property=\"og:site_name\" content=\"Codemotion Magazine\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/Codemotion.Italy\/\" \/>\n<meta property=\"article:published_time\" content=\"2025-10-13T09:10:40+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-10-13T09:30:57+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.codemotion.com\/magazine\/wp-content\/uploads\/2025\/10\/1Jfw42TrB3CXP7AMCuL1-Qw.png\" \/>\n\t<meta property=\"og:image:width\" content=\"800\" \/>\n\t<meta property=\"og:image:height\" content=\"582\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Orli Dun\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@CodemotionIT\" \/>\n<meta name=\"twitter:site\" content=\"@CodemotionIT\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Orli Dun\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"6 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/www.codemotion.com\/magazine\/es\/inteligencia-artificial\/primeros-pasos-en-pln-y-el-analisis-de-sentimientos\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/www.codemotion.com\/magazine\/es\/inteligencia-artificial\/primeros-pasos-en-pln-y-el-analisis-de-sentimientos\/\"},\"author\":{\"name\":\"Orli Dun\",\"@id\":\"https:\/\/www.codemotion.com\/magazine\/#\/schema\/person\/37ca255c359cc54110ac89eb4fa7db42\"},\"headline\":\"Primeros Pasos en PLN y el An\u00e1lisis de Sentimientos\",\"datePublished\":\"2025-10-13T09:10:40+00:00\",\"dateModified\":\"2025-10-13T09:30:57+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/www.codemotion.com\/magazine\/es\/inteligencia-artificial\/primeros-pasos-en-pln-y-el-analisis-de-sentimientos\/\"},\"wordCount\":1141,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/www.codemotion.com\/magazine\/#organization\"},\"image\":{\"@id\":\"https:\/\/www.codemotion.com\/magazine\/es\/inteligencia-artificial\/primeros-pasos-en-pln-y-el-analisis-de-sentimientos\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/www.codemotion.com\/magazine\/wp-content\/uploads\/2025\/10\/1Jfw42TrB3CXP7AMCuL1-Qw.png\",\"keywords\":[\"IA\",\"Machine Learning\"],\"articleSection\":[\"Aprendizaje autom\u00e1tico\",\"Inteligencia Artificial\"],\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/www.codemotion.com\/magazine\/es\/inteligencia-artificial\/primeros-pasos-en-pln-y-el-analisis-de-sentimientos\/#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.codemotion.com\/magazine\/es\/inteligencia-artificial\/primeros-pasos-en-pln-y-el-analisis-de-sentimientos\/\",\"url\":\"https:\/\/www.codemotion.com\/magazine\/es\/inteligencia-artificial\/primeros-pasos-en-pln-y-el-analisis-de-sentimientos\/\",\"name\":\"PLN y An\u00e1lisis de Sentimientos: Gu\u00eda para Principiantes\",\"isPartOf\":{\"@id\":\"https:\/\/www.codemotion.com\/magazine\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\/\/www.codemotion.com\/magazine\/es\/inteligencia-artificial\/primeros-pasos-en-pln-y-el-analisis-de-sentimientos\/#primaryimage\"},\"image\":{\"@id\":\"https:\/\/www.codemotion.com\/magazine\/es\/inteligencia-artificial\/primeros-pasos-en-pln-y-el-analisis-de-sentimientos\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/www.codemotion.com\/magazine\/wp-content\/uploads\/2025\/10\/1Jfw42TrB3CXP7AMCuL1-Qw.png\",\"datePublished\":\"2025-10-13T09:10:40+00:00\",\"dateModified\":\"2025-10-13T09:30:57+00:00\",\"description\":\"Aprende PLN y an\u00e1lisis de sentimientos desde cero. 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