#NLTK

[pythonprogrammingsnippets]

python iterative monte carlo search for text generation using nltk

You are playing a game and you want to win. But you don't know what move to make next, because you don't know what the other player will do. So, you decide to try different moves randomly and see what happens. You repeat this process again and again, each time learning from the result of the move you made. This is called iterative Monte Carlo search. It's like making random moves in a game and learning from the outcome each time until you find the best move to win.

Iterative Monte Carlo search is a technique used in AI to explore a large space of possible solutions to find the best ones. It can be applied to semantic synonym finding by randomly selecting synonyms, generating sentences, and analyzing their context to refine the selection.

# an iterative monte carlo search example using nltk # https://pythonprogrammingsnippets.tumblr.com import random from nltk.corpus import wordnet # Define a function to get the synonyms of a word using wordnet def get_synonyms(word): synonyms = [] for syn in wordnet.synsets(word): for l in syn.lemmas(): if '_' not in l.name(): synonyms.append(l.name()) return list(set(synonyms)) # Define a function to get a random variant of a word def get_random_variant(word): synonyms = get_synonyms(word) if len(synonyms) == 0: return word else: return random.choice(synonyms) # Define a function to get the score of a candidate sentence def get_score(candidate): return len(candidate) # Define a function to perform one iteration of the monte carlo search def monte_carlo_search(candidate): variants = [get_random_variant(word) for word in candidate.split()] max_candidate = ' '.join(variants) max_score = get_score(max_candidate) for i in range(100): variants = [get_random_variant(word) for word in candidate.split()] candidate = ' '.join(variants) score = get_score(candidate) if score > max_score: max_score = score max_candidate = candidate return max_candidate initial_candidate = "This is an example sentence." # Perform 10 iterations of the monte carlo search for i in range(10): initial_candidate = monte_carlo_search(initial_candidate) print(initial_candidate)

output:

This manufacture Associate_in_Nursing theoretical_account sentence. This fabricate Associate_in_Nursing theoretical_account sentence. This construct Associate_in_Nursing theoretical_account sentence. This cathode-ray_oscilloscope Associate_in_Nursing counteract sentence. This collapse Associate_in_Nursing computed_axial_tomography sentence. This waste_one's_time Associate_in_Nursing gossip sentence. This magnetic_inclination Associate_in_Nursing temptingness sentence. This magnetic_inclination Associate_in_Nursing conjure sentence. This magnetic_inclination Associate_in_Nursing controversy sentence. This inclination Associate_in_Nursing magnetic_inclination sentence.

[theharrymanback]

El arte de escribir bien (y algunas estadísticas al respecto)

El arte de escribir bien (y algunas estadísticas al respecto)

Desde hace un tiempo, muy a menudo me sorprendo a mí mismo maravillado frente a un texto. Pero no porque la historia que cuenta sea impresionante o me esté dando información súper interesante. A medida que mi lista de lecturas ha ido creciendo, también lo ha hecho mi gozo al encontrar pasajes particularmente bien escritos. Además, durante los últimos años he invertido muchas horas intentando…

[gudguy1a]

Resource punkt_tab not found - for NLTK (NLP)

Over the past few years, I see that quite a few folks are STILL getting this error in Jupyter Notebook. And it means, again, a lot of troubleshooting on my part. The instructor for the course I am taking (one of several Gen AI courses) did not get that error or they have an environment set up for that specific .ipynb file. And as such, they did not comment on it. After I did that last…

[josegremarquez]

El concepto de los diccionarios de sentimientos y cómo son fundamentales en el análisis de sentimientos.

¿Qué son los diccionarios de sentimientos y cómo funcionan? Imagina un diccionario, pero en lugar de definir palabras, clasifica las palabras según la emoción que expresan. Estos son los diccionarios de sentimientos. Son como una especie de “tesauro emocional” que asigna a cada palabra una puntuación que indica si es positiva, negativa o neutral. ¿Cómo funcionan? Lexicón: Contienen una extensa…

[soumenatta]

In this tutorial, we will explore how to perform sentiment analysis using Python with three popular libraries — NLTK, TextBlob, and VADER.

[semperintrepida]

Not me out here writing a program to log into my AO3 account and perform a natural language sentiment analysis of the comments in my inbox to identify trolls without having to read their garbage...

[linogram]

in what situation is the word "oh" a proper noun

[ithaca-my-beloved]

Can't believe I had to miss my morphology lecture because comp sci has no concept of timetables

[techinfotrends]

Want to make NLP tasks a breeze? Explore how NLTK streamlines text analysis in Python, making it easier to extract valuable insights from your data. Discover more https://bit.ly/487hj9L

[evilplumpie]

part of speech tagging? oh man sorry i though u meant piece of shit tagging

[pythonprogrammingsnippets]

python keyword extraction using nltk wordnet

import re # include wordnet.morphy from nltk.corpus import wordnet # https://pythonprogrammingsnippets.tumblr.com/ def get_non_plural(word): # return the non-plural form of a word # if word is not empty if word != "": # get the non-plural form non_plural = wordnet.morphy(word, wordnet.NOUN) # if non_plural is not empty if non_plural != None: # return the non-plural form # print(word, "->", non_plural) return non_plural # if word is empty or non_plural is empty return word def get_root_word(word): # return the root word of a word # if word is not empty if word != "": word = get_non_plural(word) # get the root word root_word = wordnet.morphy(word) # if root_word is not empty if root_word != None: # return the root word # print(word, "->", root_word) word = root_word # if word is empty or root_word is empty return word def process_keywords(keywords): ret_k = [] for k in keywords: # replace all characters that are not letters, spaces, or apostrophes with a space k = re.sub(r"[^a-zA-Z' ]", " ", k) # if there is more than one whitespace in a row, replace it # with a single whitespace k = re.sub(r"\s+", " ", k) # remove leading and trailing whitespace k = k.strip() k = k.lower() # if k has more than one word, split it into words and add each word # back to keywords if " " in k: ret_k.append(k) # we still want the original keyword k = k.split(" ") for k2 in k: #if not is_adjective(k2): ret_k.append(get_root_word(k2)) ret_k.append(k2.strip()) else: # if not is_adjective(k): ret_k.append(get_root_word(k)) ret_k.append(k.strip()) # unique ret_k = list(set(ret_k)) # remove empty strings ret_k = [k for k in ret_k if k != ""] # remove all words that are less than 3 characters ret_k = [k for k in ret_k if len(k) >= 3] # remove words like 'and', 'or', 'the', etc. ret_k = [k for k in ret_k if k not in ["and", "or", "the", "a", "an", "of", "to", "in", "on", "at", "for", "with", "from", "by", "as", "into", "like", "through", "after", "over", "between", "out", "against", "during", "without", "before", "under", "around", "among", "throughout", "despite", "towards", "upon", "concerning", "of", "to", "in", "on", "at", "for", "with", "from", "by", "as", "into", "like", "through", "after", "over", "between", "out", "against", "during", "without", "before", "under", "around", "among", "throughout", "despite", "towards", "upon", "concerning", "this", "that", "these", "those", "is", "are", "was", "were", "be", "been", "being", "have", "has", "had", "having", "do", "does", "did", "doing", "will", "would", "shall", "should", "can", "could", "may", "might", "must", "ought", "i", "me", "my", "mine", "we", "us", "our", "ours", "you", "your", "yours", "he", "him", "his", "she", "her", "hers", "it", "its", "they", "them", "their", "theirs", "what", "which", "who", "whom", "whose", "this", "that", "these", "those", "myself", "yourself", "himself", "herself", "itself", "ourselves", "yourselves", "themselves", "whoever", "whatever", "whomever", "whichever", "whichever" ]] return ret_k def extract_keywords(paragraph): if " " in paragraph: return paragraph.split(" ") return [paragraph]

example usage:

the_string = "Jims House of Judo and Karate is a martial arts school in the heart of downtown San Francisco. We offer classes in Judo, Karate, and Jiu Jitsu. We also offer private lessons and group classes. We have a great staff of instructors who are all black belts. We have been in business for over 20 years. We are located at 123 Main Street." keywords = process_keywords(extract_keywords(the_string)) print(keywords)

output:

# output: ['jims', 'instructors', 'class', 'lesson', 'all', 'school', 'san', 'martial', 'classes', 'karate', 'great', 'lessons', 'downtown', 'private', 'arts', 'also', 'locate', 'belts', 'business', 'judo', 'years', 'located', 'main', 'street', 'jitsu', 'house', 'offer', 'staff', 'group', 'heart', 'instructor', 'belt', 'black', 'francisco', 'jiu']

[codezup]

"A Practical Guide to Natural Language Processing with Python and NLTK"

Introduction “A Practical Guide to Natural Language Processing with Python and NLTK” is a comprehensive tutorial that covers the fundamentals of natural language processing (NLP) using Python and the Natural Language Toolkit (NLTK). This guide is designed for beginners and intermediate learners who want to learn how to perform various NLP tasks, such as text preprocessing, sentiment analysis,…

[softloomtraining]

Python in Finance: From Risk Analysis to Stock Predictions

Introduction

The financial world is rapidly evolving, with technology playing an integral role in shaping its future. Python, a versatile and powerful programming language, has emerged as a favourite among finance professionals for its ease of use and extensive libraries. From analyzing financial risks to predicting stock market trends, Python empowers professionals to make data-driven decisions efficiently. This blog explores how Python is transforming the finance sector, with practical applications ranging from risk management to algorithmic trading.

How Python is Used in Finance

Risk Analysis

Data Visualization: Python libraries like Matplotlib and Seaborn are used to create compelling visualizations, enabling better insights into potential risks.

Risk Modeling: Libraries such as NumPy and Pandas help in creating stochastic models and performing Monte Carlo simulations for risk assessment.

Stress Testing: Python is employed to simulate worst-case scenarios, allowing firms to prepare for adverse financial conditions.

2. Stock Market Predictions

Time-Series Analysis: Python excels in analyzing historical stock data using libraries like Statsmodels and Pandas.

Machine Learning Models: With libraries such as Scikit-learn and TensorFlow, Python is used to create predictive models for stock price movements.

Sentiment Analysis: NLP tools like NLTK and spaCy help analyze public sentiment from news articles and social media to gauge market trends.

3. Algorithmic Trading

Python enables the development of automated trading bots that execute trades based on predefined strategies. Libraries like Zipline and Backtrader are popular choices for this purpose.

4. Portfolio Optimization

Python's optimization libraries, such as SciPy and PyPortfolioOpt, assist in constructing optimal investment portfolios by balancing risk and returns.

Why Python is the Ideal Choice for Finance

Ease of Use: Python's syntax is intuitive and easy to learn, making it accessible even to non-programmers.

Extensive Libraries: Its robust ecosystem of libraries caters specifically to financial tasks.

Integration: Python integrates seamlessly with other tools and databases commonly used in finance.

Community Support: A vast community ensures that there’s ample documentation and support for tackling challenges.

Challenges in Using Python in Finance

While Python offers immense benefits, challenges like processing large-scale financial data in real-time and ensuring the security of applications must be addressed. Leveraging cloud computing and secure coding practices can mitigate these issues.

Conclusion

Python's impact on the finance industry is undeniable, offering solutions that are both innovative and practical. From mitigating risks to forecasting market trends, Python empowers professionals to stay ahead in the dynamic financial landscape. Its simplicity and versatility make it an ideal tool not just for seasoned professionals but also for beginners looking to break into the world of finance and technology. Learning Python is an excellent starting point for anyone interested in leveraging data-driven insights to make smarter financial decisions. As the language continues to evolve, its relevance in finance is set to grow, making it a must-learn for aspiring financial analysts and tech enthusiasts alike.

[atplblog]

Price: [price_with_discount] (as of [price_update_date] - Details) [ad_1] An easy-to-follow, step-by-step guide for getting to grips with the real-world application of machine learning algorithmsKey FeaturesExplore statistics and complex mathematics for data-intensive applications Discover new developments in EM algorithm, PCA, and bayesian regression Study patterns and make predictions across various datasets Book DescriptionMachine learning has gained tremendous popularity for its powerful and fast predictions with large datasets. However, the true forces behind its powerful output are the complex algorithms involving substantial statistical analysis that churn large datasets and generate substantial insight. This second edition of Machine Learning Algorithms walks you through prominent development outcomes that have taken place relating to machine learning algorithms, which constitute major contributions to the machine learning process and help you to strengthen and master statistical interpretation across the areas of supervised, semi-supervised, and reinforcement learning. Once the core concepts of an algorithm have been covered, you'll explore real-world examples based on the most diffused libraries, such as scikit-learn, NLTK, TensorFlow, and Keras. You will discover new topics such as principal component analysis (PCA), independent component analysis (ICA), Bayesian regression, discriminant analysis, advanced clustering, and gaussian mixture. By the end of this book, you will have studied machine learning algorithms and be able to put them into production to make your machine learning applications more innovative.What you will learnStudy feature selection and the feature engineering process Assess performance and error trade-offs for linear regression Build a data model and understand how it works by using different types of algorithm Learn to tune the parameters of Support Vector Machines (SVM) Explore the concept of natural language processing (NLP) and recommendation systems Create a machine learning architecture from scratchWho this book is forMachine Learning Algorithms is for you if you are a machine learning engineer, data engineer, or junior data scientist who wants to advance in the field of predictive analytics and machine learning. Familiarity with R and Python will be an added advantage for getting the best from this book. Publisher ‏ : ‎ Packt Publishing; 2nd ed. edition (30 August 2018) Language ‏ : ‎ English Paperback ‏ : ‎ 522 pages ISBN-10 ‏ : ‎ 1789347998 ISBN-13 ‏ : ‎ 978-1789347999 Item Weight ‏ : ‎ 900 g Dimensions ‏ : ‎ 23.49 x 19.05 x 2.74 cm Country of Origin ‏ : ‎ India [ad_2]

[starseedfxofficial]

Mastering WTI Trading with Sentiment Analysis Algorithms WTI and Sentiment Analysis Algorithms: The New Frontier of Trading Trading WTI crude oil is like playing chess against a grandmaster—it’s all about anticipating the next move. Enter sentiment analysis algorithms, the unsung heroes of modern trading. These algorithms are your backstage pass to understanding market sentiment and gaining a strategic edge in WTI trading. In this guide, we’ll blend humor, advanced insights, and actionable strategies to make sentiment analysis your new secret weapon. Why Sentiment Analysis Matters in WTI Trading WTI crude oil prices are notoriously influenced by geopolitical tensions, supply-demand imbalances, and market sentiment. Understanding these emotional undercurrents can give you a decisive edge. Sentiment analysis algorithms analyze data from news outlets, social media, and financial reports to gauge market sentiment. Imagine having a lie detector for the market—that’s sentiment analysis. It helps you decipher whether traders are bullish, bearish, or just plain confused. The Inner Workings of Sentiment Analysis Algorithms So, how do these algorithms work their magic? - Data Collection: They scrape data from multiple sources, including social media, news headlines, and financial forums. - Text Processing: Using Natural Language Processing (NLP), they break down text into analyzable components. - Sentiment Scoring: Each piece of text is assigned a sentiment score, ranging from highly positive to highly negative. - Trend Identification: Algorithms aggregate scores to detect shifts in market sentiment. Think of these steps as a detective piecing together clues to solve a mystery—only the mystery is whether oil prices are about to soar or crash. How to Combine Sentiment Analysis with WTI Trading To truly capitalize on sentiment analysis, you need to pair it with technical and fundamental analysis. Here’s how: Step 1: Identify Key Sentiment Triggers Watch for events that influence sentiment, such as: - OPEC meetings - Geopolitical tensions - Inventory reports Pro Tip: Use sentiment analysis to detect early reactions to these events. If social media buzz spikes after an OPEC announcement, the market might be gearing up for a big move. Step 2: Overlay Sentiment with Technical Levels Combine sentiment scores with key technical levels like support and resistance. For example, if sentiment turns bullish and WTI is approaching a strong support level, it’s a good time to consider going long. Step 3: Monitor Sentiment Trends Sentiment analysis isn’t just about snapshots; it’s about trends. Use algorithms to track whether sentiment is consistently shifting in one direction or oscillating. Real-World Example: Sentiment Meets WTI Let’s say OPEC announces production cuts. Sentiment analysis algorithms detect a sharp increase in bullish tweets and news articles about rising oil prices. At the same time, WTI is trading near a critical resistance level. This convergence signals a potential breakout, and savvy traders position themselves accordingly. Avoiding Pitfalls in Sentiment Analysis While sentiment analysis is powerful, it’s not infallible. Here’s how to sidestep common pitfalls: - Over-Reliance: Don’t let sentiment override your technical and fundamental analysis. - Lagging Data: Ensure your sentiment data is real-time to avoid acting on outdated information. - Noise vs. Signal: Algorithms can sometimes mistake noise for actionable data. Use filters to prioritize high-quality sources. Tools and Platforms for Sentiment Analysis To get started, here are some tools that excel in sentiment analysis: - Trading Platforms: Bloomberg Terminal and MetaTrader offer integrated sentiment indicators. - Social Media Scrapers: Tools like Dataminr and Hootsuite track social media sentiment in real-time. - Custom Algorithms: Develop your own using Python libraries like NLTK or spaCy. Why WTI and Sentiment Analysis Are a Perfect Match WTI is one of the most sentiment-driven assets in the Forex market. From geopolitical headlines to inventory data, market sentiment can swing wildly. By incorporating sentiment analysis algorithms, you gain the ability to anticipate these shifts and act before the broader market catches on. Mastering WTI trading with sentiment analysis algorithms is like adding a turbocharger to your trading engine. It’s not just about what the charts say; it’s about understanding the emotional currents driving market movements. So, are you ready to harness the power of sentiment and take your trading to the next level? —————– Image Credits: Cover image at the top is AI-generated Read the full article