#latent dirichlet allocation

[covid-safer-hotties]

Also preserved in our archive

A new study by researchers at Zhejiang University has highlighted the disproportionate health challenges faced by sexual and gender-diverse (SGD) individuals during the COVID-19 pandemic. By analyzing over 471 million tweets using advanced natural language processing (NLP) techniques, the study reveals that SGD individuals were more likely to discuss concerns related to social connections, mask-wearing, and experienced higher rates of COVID-19 symptoms and mental health issues than non-SGD individuals. The study has been published in the journal Health Data Science.

The COVID-19 pandemic has exposed and intensified health disparities, particularly for vulnerable populations like the sexual and gender-diverse (SGD) community. Unlike traditional health data sources, social media provides a more dynamic and real-time reflection of public concerns and experiences. Zhiyun Zhang, a Ph.D. student at Zhejiang University, and Jie Yang, Assistant Professor at the same institution, led a study that analyzed large-scale Twitter data to understand the unique challenges faced by SGD individuals during the pandemic.

To address this, the research team used NLP methods such as Latent Dirichlet Allocation (LDA) models for topic modeling and advanced sentiment analysis to evaluate the discussions and concerns of SGD Twitter users compared to non-SGD users. This approach allowed the researchers to explore three primary questions: the predominant topics discussed by SGD users, their concerns about COVID-19 precautions, and the severity of their symptoms and mental health challenges.

The findings reveal significant differences between the two groups. SGD users were more frequently involved in discussions about "friends and family" (20.5% vs. 13.1%) and "wearing masks" (10.1% vs. 8.3%). They also expressed higher levels of positive sentiment toward vaccines such as Pfizer, Moderna, AstraZeneca, and Johnson & Johnson. The study found that SGD individuals reported significantly higher frequencies of both physical and mental health symptoms compared to non-SGD users, underscoring their heightened vulnerability during the pandemic.

"Our large-scale social media analysis highlights the concerns and health challenges of SGD users. The topic analysis showed that SGD users were more frequently involved in discussions about 'friends and family' and 'wearing masks' than non-SGD users. SGD users also expressed a higher level of positive sentiment in tweets about vaccines," said Zhiyun Zhang, the lead researcher. "These insights emphasize the importance of targeted public health interventions for SGD communities."

The study demonstrates the potential of using social media data to monitor and understand public health concerns, especially for marginalized communities like SGD individuals. The results suggest the need for more tailored public health strategies to address the unique challenges faced by SGD communities during pandemics.

Moving forward, the research team aims to develop an automated pipeline to continuously monitor the health of targeted populations, offering data-driven insights to support more comprehensive public health services.

More information: Zhiyun Zhang et al, Sexual and Gender-Diverse Individuals Face More Health Challenges during COVID-19: A Large-Scale Social Media Analysis with Natural Language Processing, Health Data Science (2024). DOI: 10.34133/hds.0127 spj.science.org/doi/10.34133/hds.0127

[greatonlinetrainingsposts]

Machine Learning in SAS: An Overview of Techniques and Real-World Applications

Machine learning is transforming industries around the world, and SAS programming stands out as a powerful tool for implementing machine learning techniques, particularly for enterprises focused on large-scale data and analytics-driven insights. SAS has been a leader in statistical analysis for decades, and its continued evolution makes it an ideal platform for businesses looking to leverage machine learning capabilities effectively.

In this article, we’ll explore some core machine learning techniques that SAS programming supports, the unique advantages SAS brings to machine learning, and several real-world applications that showcase its versatility across industries like finance, healthcare, and retail.

Why Use SAS Programming for Machine Learning?

SAS programming is renowned for its comprehensive suite of data analytics tools and extensive support for advanced statistical methods, making it particularly useful for machine learning. For businesses that prioritize data security, large-scale data processing, and consistent compliance, SAS offers a trusted platform with robust machine learning algorithms.

The advantage of using SAS programming for machine learning lies in its combination of analytical power, ease of integration with other data systems, and compatibility with both open-source and proprietary tools. SAS supports Python and R integration, allowing data scientists to leverage additional libraries while benefiting from SAS’s data management strengths.

Key Machine Learning Techniques in SAS

SAS programming provides an array of machine learning techniques that can support predictive modeling, clustering, natural language processing, and more. Here’s a look at some of the primary techniques you can use within SAS programming for machine learning:

1. Supervised Learning (Predictive Modeling)

- Overview: Supervised learning involves using labeled data to train models that can make predictions or classifications. In SAS programming, supervised learning algorithms are robustly supported, allowing users to build and deploy predictive models efficiently.

- Common Algorithms: Linear regression, decision trees, support vector machines (SVM), and neural networks are some popular options.

- Application: Predicting customer churn, credit scoring, and demand forecasting are common use cases that utilize supervised learning in SAS programming.

2. Unsupervised Learning (Clustering and Association Analysis)

- Overview: Unsupervised learning deals with data that lacks labeled responses, which makes it ideal for discovering hidden patterns. Clustering and association analysis are often used for market segmentation and recommendations.

- Common Techniques: k-means clustering, hierarchical clustering, and association rule mining are commonly applied within SAS programming’s unsupervised learning capabilities.

- Application: Retailers frequently use clustering to segment customers based on purchasing behavior, while financial firms use association analysis to identify patterns in transactions.

3. Natural Language Processing (NLP)

- Overview: NLP is essential for analyzing unstructured text data, and SAS programming provides a set of tools for handling tasks like sentiment analysis, topic modeling, and text summarization.

- Common Techniques: Sentiment analysis, text parsing, and latent Dirichlet allocation (LDA) are NLP techniques available in SAS programming.

- Application: SAS programming can analyze customer feedback, social media content, and surveys to help businesses understand sentiment and emerging trends.

4. Time Series Forecasting

- Overview: Time series forecasting is used to predict future values based on historical data patterns, making it invaluable for applications where timing and trend analysis are crucial.

- Common Techniques: ARIMA (AutoRegressive Integrated Moving Average), exponential smoothing, and seasonal decomposition are available in SAS programming for time series analysis.

- Application: Time series forecasting is highly beneficial in inventory management, economic forecasting, and sales predictions.

5. Deep Learning

- Overview: Deep learning algorithms like neural networks and convolutional neural networks (CNNs) allow for complex pattern recognition and are well-suited for tasks involving image and audio data.

- Common Techniques: Multilayer perceptrons, CNNs, and recurrent neural networks (RNNs) are supported in SAS programming for deep learning applications.

- Application: Deep learning models can be applied in fraud detection, image recognition in medical diagnostics, and product recommendation systems.

Real-World Applications of Machine Learning in SAS Programming

SAS programming is applied across various industries for machine learning-driven solutions, helping companies make data-informed decisions and automate critical business processes.

1. Finance: Credit Scoring and Risk Management

- Financial institutions rely on machine learning for predictive analytics, particularly in credit scoring and fraud detection. SAS programming enables these organizations to implement complex models that assess credit risk based on multiple factors like transaction history and financial behavior.

Example: By using logistic regression and decision tree models, a bank can predict the likelihood of loan default, allowing for better risk management.

2. Healthcare: Predictive Diagnostics and Patient Management

- In healthcare, SAS programming helps providers utilize patient data for predictive diagnostics, treatment personalization, and operational efficiency. With supervised learning, healthcare professionals can assess the probability of disease occurrence and predict patient outcomes.

Example: SAS programming can be used to develop predictive models for patient readmission rates, aiding hospitals in proactive patient care and resource planning.

3. Retail: Customer Segmentation and Personalized Marketing

- Machine learning in SAS programming supports customer segmentation, which helps retailers understand consumer behavior and tailor marketing strategies. SAS’s clustering and association analysis capabilities allow for precise segmentation based on purchasing patterns and preferences.

Example: Retailers can target segmented customer groups with personalized product recommendations, improving engagement and sales.

4. Manufacturing: Predictive Maintenance and Quality Control

- SAS programming’s time series forecasting and anomaly detection capabilities are highly valuable in manufacturing, where predictive maintenance can prevent equipment failures and minimize downtime.

Example: Manufacturing companies use SAS programming to predict machine failure by analyzing historical operational data, allowing for timely maintenance and reduced disruptions.

5. Telecommunications: Customer Churn Prediction

- Customer retention is a key focus for telecom companies. SAS programming’s predictive modeling capabilities allow telecom providers to identify customers at risk of churning and take preemptive measures.

Example: By using logistic regression models, telecom companies can predict churn likelihood and create retention campaigns for high-risk customers.

SAS Online Training for Machine Learning

For those looking to deepen their understanding of SAS programming and its machine learning capabilities, SAS online training offers comprehensive resources for learners at all levels. Whether you're starting from scratch or looking to enhance your skills, SAS online training programs provide access to expert-led courses and hands-on exercises. By enrolling in SAS programming tutorial sessions, you can gain in-depth knowledge about various machine learning techniques, algorithms, and real-world applications that are essential in the modern data landscape.

Additionally, for individuals seeking an extensive and structured learning experience, a SAS programming full course can guide you through everything from the basics of data analysis to advanced machine learning applications, preparing you for real-world challenges in data science and machine learning.

The Future of Machine Learning in SAS Programming

As SAS programming continues to evolve, its integration with open-source languages like Python and R enhances flexibility, making it an attractive platform for businesses that want to blend SAS’s capabilities with the vast libraries available in open-source environments. Moreover, SAS Viya, the cloud-enabled, open analytics platform, allows organizations to deploy models faster, scale machine learning applications, and enable cross-functional collaboration.

In addition to ongoing advancements, SAS has also been expanding its support for deep learning and neural networks, making it a powerful tool for tackling increasingly complex machine learning problems. With its robust data processing abilities and strong focus on enterprise security, SAS programming is well-positioned to support industries aiming to harness the full potential of machine learning.

Conclusion

Machine learning in SAS programming offers powerful techniques and a reliable platform for implementing predictive models, uncovering insights, and optimizing business processes across a variety of industries. From customer segmentation and churn prediction to predictive maintenance and patient management, SAS programming’s machine learning tools help organizations make data-driven decisions and gain a competitive edge. As technology and data demands continue to grow, SAS remains a trusted partner for machine learning applications, offering both stability and innovation for data-driven enterprises.

[jorgemarquet]

Individual suicide risk factors with resting-state brain functional connectivity patterns in bipolar disorder patients based on latent Dirichlet allocation model - ScienceDirect

[putnamspuppeteer]

The top 10 words taken from the top 20 most prevalent topics (as determined by latent Dirichlet allocation) from a data set of 6400 songs by black metal, death metal, doom metal, and power metal bands.

[blogchaindeveloper]

Natural Language Processing (NLP) and Machine Learning

In artificial intelligence, integrating machine learning (ML) with natural language processing (NLP) has sped up the development of intelligent systems that can understand and produce language like humans. To provide a comprehensive understanding of the junction of NLP and ML, this essay delves into the technical intricacies underpinning them.

Natural Language Processing (NLP) Definition

Natural language processing, or NLP, is a branch of artificial intelligence (AI) that studies human-computer interaction. By enabling machines to comprehend, interpret, and produce language equal to humans, the goal is to bridge the gap between human communication and computer capabilities.

Machine Learning's Place in NLP

Machine Learning (ML), a paradigm that enables computers to recognize patterns and make predictions from data without explicit programming, is the foundation of natural language processing (NLP). Machine learning algorithms are essential to giving natural language processing (NLP) systems the ability to understand nuances in language, adapt to different environments, and improve over time.

Natural Language Processing Foundations

NLP and linguistics

Language comprehension requires an understanding of linguistics. NLP algorithms use phrase parsing, semantic meaning extraction, and speech recognition components grounded on linguistic principles. For NLP systems to process language effectively, they must be able to understand syntactic and semantic structures.

Preparing Text and Tokenization

In natural language processing (NLP), one of the initial tasks is to break up text into smaller units called tokens. Tokenizing language is required to understand and analyze it. Lemmatization and stemming are two text preparation techniques that further purify the data, reducing dimensionality and enhancing the performance of NLP models.

Recognition of Named Entities (NER)

One of the most essential parts of NLP is the ability to identify entities in text, such as individuals, places, and organizations. NER is a subtask that helps extract meaningful information from unstructured text using machine learning (ML) techniques to discover and classify items automatically.

NLP's Machine Learning Algorithms

Enforced Education for Text Categorization

In NLP, supervised learning is a standard method, particularly for problems like text classification. Algorithms can be trained to classify text into preset categories using labeled datasets. Sentiment analysis, spam detection, and subject categorization all use this extensively.

Unsupervised Education for Topic Modeling and Clustering

Unsupervised learning is valuable when there is a lack of labeled data. Clustering algorithms assemble comparable documents, revealing latent patterns in extensive text collections. Without classification, topic modeling methods like Latent Dirichlet Allocation (LDA) can reveal underlying document themes.

Sequential Data: Recurrent Neural Networks (RNNs)

Since RNNs are a neural network intended for sequential data, language-related tasks fit them well. Their ability to identify sequence word dependencies benefits text production, machine translation, and language modeling applications.

Transformers: A New Chapter in NLP

Models like BERT (Bidirectional Encoder Representations from Transformers) or GPT (Generative Pre-trained Transformer) illustrate how the advent of transformers changed the paradigm in NLP. These models effectively capture contextual information through the use of attention mechanisms, enabling a deeper understanding of linguistic nuances.

Contextual Representations in Two Ways: BERT

BERT, a pre-trained transformer model, excels in contextualized word embeddings. Because BERT considers the entire context of a word within a sentence in both directions, it does astonishingly well in tasks like named entity recognition, text summarization, and question answering.

Transformer with the help of GPT (Generative Pre-training)

GPT, on the other hand, focuses on creating tasks. Thanks to comprehensive and varied textual training, GPT generates coherent and appropriate writing for the situation. This has important implications for understanding natural language and the production of original literature.

Integration Challenges for NLP and ML

Polysemy and Ambiguity

There are several difficulties since natural language is inherently ambiguous and polysemy. Words frequently have several meanings depending on the context, which makes it difficult for machine learning models to identify the intended meaning correctly.

Insufficient Contextual Knowledge

Although progress has been made in capturing contextual information using transformers such as BERT, gaining a thorough knowledge of context in highly dynamic interactions still needs to be solved. Real-world interactions frequently contain nuanced details that are hard for robots to understand fully.

Bias in Data and Ethical Issues

Bias in NLP systems is a concern because ML models are trained on massive datasets. Biased data can result in unfair results since it might reinforce and magnify pre existing societal biases. Addressing these ethical issues is essential to developing and implementing NLP applications responsibly.

Progress and Upcoming Paths

NLP Transfer Learning

In NLP, transfer learning has become a potent technique that allows models trained on one task to be optimized for a related one. This method significantly decreases the labeled data required for each unique application, increasing NLP systems' effectiveness.

NLP in several modes

As AI systems develop, the incorporation of several modalities, including text, graphics, and audio, is becoming increasingly crucial. To enable more adaptable and human-like interactions, multimodal natural language processing (NLP) seeks to create models to understand and produce content across various communication modalities.

NLP's Explainable AI

NLP models' interpretability is becoming increasingly well-known, particularly in crucial fields like banking and healthcare. Explainable AI techniques aim to show how and why specific decisions are made in complicated natural language processing (NLP) models.

NLP and ML applications

Chatbots & Virtual Assistants

The successful fusion of NLP with ML is demonstrated by the broad use of virtual assistants such as Siri, Alexa, and Google Assistant. These systems demonstrate the valuable applications of language processing technologies by comprehending user inquiries, acquiring pertinent information, and carrying out commands.

Business Insights using Sentiment Analysis

Companies use sentiment analysis, a branch of natural language processing (NLP), to determine how the public feels about their goods and services. Social media posts, customer evaluations, and news stories can all be analyzed to gain insightful information that can improve consumer happiness and strategic decision-making.

Interpretation of Languages and Intercultural Dialogue

NLP greatly aids in breaking linguistic barriers. Globally accessible machine translation models, like Google Translate, use complex algorithms to translate text between languages, promoting cross-cultural dialogue and cooperation.

AI Qualifications and Experience

AI Accreditation for NLP Professionals

In the quickly changing fields of AI and NLP, certificates are essential for individuals who want to show their knowledge. The Blockchain Council certification provides a thorough curriculum for NLP engineers that addresses advanced principles, real-world applications, and ethical considerations.

Certification for AI Developers: A Path to NLP Expertise

Getting an AI developer certification is wise for developers who want to venture into natural language processing. These certificates, such as the ones provided by the Blockchain Council, attest to an individual's competence in creating language models, implementing NLP algorithms, and solving real-world problems.

Expert in Certified Chatbots: Understanding Conversational Artificial Intelligence

A certified chatbot expert who has expertise in this subject has created conversational agents utilizing NLP and ML. The Blockchain Council's certification program enables professionals to design, develop, and improve chatbots for various applications, including customer service and virtual assistants.

In summary

Natural language processing and machine learning work together to provide computers with the ability to understand, interpret, and produce language similar to that of humans. The marriage of NLP and ML has always been innovative, from linguistic foundations to the transformative potential of transformer models. 

Addressing challenges like data bias and ethical dilemmas becomes increasingly vital as we manage language complexity. Future advancements in multimodal NLP, transfer learning, and explainable AI certification should advance the field and bring us one step closer to the objective of building brilliant and compassionate computers. Participating in recognized certification programs, such as those offered by the Blockchain Council, gives people a structured way to advance their skills and contribute to the rapidly changing fields of NLP and AI.

[leedsomics]

G-S-M: A Comprehensive Framework for Integrative Feature Selection in Omics Data Analysis and Beyond

The treatment of human diseases is a major research question in many fields related to medicine. It has become clear that patient stratification is of utmost importance so that patients receive the best possible treatment. Bio/disease markers are critical to achieve stratification. Markers can come from many different sources such as genomics, transcriptomics, and proteomics. Establishing markers from such measurements often involves data analysis, machine learning, and feature selection. Traditional feature selection techniques often rely on the estimation of individual feature importance or significance by assigning a score to each feature, disregarding the inter-feature relationships. In contrast, the G-S-M (grouping scoring modeling) approach considers a group of features as a set that is organized based on prior knowledge. This approach takes into account the interdependence among features, providing a more meaningful evaluation of feature relevance and utility. Prior knowledge can encompass much compiled information such as microRNA-target interactions and protein-protein interactions. Here we present a new tool called G-S-M that presents the generalization of our previous works such as maTE, CogNet, and PriPath. The G-S-M tool combines machine learning and prior knowledge to group and score features based on their association with a binary-labeled target such as control and disease. This approach is unique in that computational and domain knowledge is utilized concurrently. Embedded feature selection, repeatedly employing machine learning during the selection process results in the identification of the most discriminative groups. Furthermore, the G-S-M tool allows for a more holistic understanding of the underlying mechanisms of a given system to be achieved through the combination of machine learning and prior domain knowledge, which can lead to new insights and discoveries. The implementation of the G-S-M workflow is freely available for download from our GitHub repository: https://github.com/malikyousef/The-G-S-M-Grouping-Scoring-Modeling-Approach. With this generalized approach we aim to make the feature selection approach available to a broader audience and hope it will be employed in medical practice. An example of such an approach is the TextNetTopics that is based on the G-S-M approach. TextNetTopics uses Latent Dirichlet Allocation (LDA) to detect topics of words, where those topics serve as groups. In the future, we aim to extend the approach to enable the incorporation of multiple lines of evidence for biomarker detection and patient stratification via combining multi-omics data. http://dlvr.it/T4xbPW

[itiswellknownthat]

"It is well known that BERTopic is effective for extracting more detailed topics compared to other topic modeling methods such as Latent Dirichlet Allocation."

https://doi.org/10.1140/epjds/s13688-023-00445-y

[tyrionlannistersblog]

Machine Learning Techniques in Natural Language Processing (NLP)

Natural Language Processing (NLP) is a fascinating subfield of artificial intelligence (AI) that focuses on enabling machines to understand, interpret, and generate human language. It has a wide range of applications, from chatbots and virtual assistants to language translation and sentiment analysis. Machine learning techniques play a pivotal role in advancing NLP and making it increasingly powerful. In this article, we'll explore the essential machine learning techniques used in Natural Language Processing.

Machine learning Techniques in NLP

Text Classification

Text classification is one of the fundamental NLP tasks, and machine learning techniques are extensively used to categorize text into predefined classes or categories. Some common applications include spam email detection, sentiment analysis, and topic classification. Supervised learning algorithms, such as Naive Bayes, Support Vector Machines (SVM), and deep learning models like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), are widely employed for text classification.

Named Entity Recognition (NER)

NER is the process of identifying and classifying entities within text, such as names of people, organizations, locations, and dates. This is a crucial task for various applications, including information retrieval, question-answering systems, and document summarization. Conditional Random Fields (CRF), Hidden Markov Models (HMM), and deep learning architectures like Bidirectional LSTMs are used to perform NER.

Sentiment Analysis

Sentiment analysis, alternatively referred to as opinion mining, encompasses the task of assessing the sentiment or emotional tone conveyed in a given piece of text, be it positive, negative, or neutral. This is valuable for businesses wanting to understand customer feedback and social media monitoring. Supervised machine learning algorithms and lexicon-based approaches are frequently used in sentiment analysis.

Language Modeling

Language modeling is the task of predicting the probability of a sequence of words in a given context. It forms the foundation for various NLP tasks, including speech recognition, machine translation, and speech generation. N-grams, Hidden Markov Models (HMMs), and more recently, Transformer models like GPT (Generative Pre-trained Transformer) have revolutionized language modeling.

Word Embeddings

Word embeddings are a crucial part of modern NLP, representing words as dense vectors in a continuous space. Word2Vec, GloVe (Global Vectors for Word Representation), and fastText are popular techniques for learning word embeddings using machine learning development services.

Sequence-to-Sequence Models

Sequence-to-sequence models have gained significant popularity in NLP for tasks like machine translation, text summarization, and chatbot responses. These models, typically based on recurrent or attention-based neural networks, take input sequences and generate output sequences. The attention mechanism, introduced in models like the Transformer, has greatly improved the quality of sequence-to-sequence tasks.

Topic Modeling

Topic modeling techniques aim to discover the underlying themes or topics in a collection of documents. Latent Dirichlet Allocation (LDA) is one of the most well-known algorithms for topic modeling, clustering documents into topics based on the distribution of words within them.

Part-of-Speech Tagging

Part-of-speech tagging involves assigning grammatical categories (e.g., noun, verb, adjective) to words in a sentence. It's essential for tasks like parsing and information extraction. Hidden Markov Models (HMMs) and Conditional Random Fields (CRFs) are commonly used for part-of-speech tagging.

Speech Recognition

Speech recognition involves the conversion of spoken language into textual form. It's a vital technology for applications like voice assistants and transcription services. Deep learning techniques, especially deep neural networks and recurrent neural networks, have significantly improved the accuracy of speech recognition systems.

Coreference Resolution

Coreference resolution deals with identifying when different expressions in text refer to the same entity. This is critical for understanding the meaning of text and is used in applications like question-answering systems and document summarization.

Challenges in NLP

While machine learning techniques have significantly advanced NLP, challenges persist in the field. Some of the notable challenges include:

Ambiguity: Natural language is inherently ambiguous, making it challenging for models to understand context and meaning accurately.

Lack of Data: Developing robust NLP models often requires vast amounts of data, which can be a limitation for certain languages and domains.

Domain-Specific Language: NLP models may struggle to understand domain-specific jargon or colloquial language.

Bias and Fairness: NLP models can inherit biases from their training data, leading to biased predictions and decisions.

Multilingual Processing: Developing NLP models that work well across multiple languages is a complex task.

Future Directions

Natural Language Processing (NLP) is a field that is rapidly advancing with continuous research and development. The future of NLP is likely to involve more advanced machine learning techniques, enhanced multilingual processing, better model interpretability, and increased fairness and accountability in NLP systems. The growing availability of pre-trained models, like BERT and GPT, is making NLP more accessible and powerful.

Conclusion

In conclusion, machine learning services have transformed Natural Language Processing and enabled a wide range of applications in understanding and generating human language. These techniques continue to advance, making NLP more accessible and accurate. As NLP technology progresses, it holds the potential to further improve human-computer interactions, language translation, content summarization, and more. The integration of machine learning in NLP is helping bridge the gap between human communication and AI, making our interactions with technology more intuitive and natural.

[craigbrownphd]

Topic Modeling Using Latent Dirichlet Allocation (LDA) https://www.analyticsvidhya.com/blog/2023/02/topic-modeling-using-latent-dirichlet-allocation-lda/?utm_source=dlvr.it&utm_medium=tumblr

[datascience-from-the-trenches]

Connection between the musical and lyrical content of metal music.

A topic model was first constructed using Latent Dirichlet Allocation (LDA), and the perceived musical hardness/heaviness and darkness/gloominess were extracted using audio feature models.

Positive correlations were found between musical hardness and darkness and textual topics dealing with ‘brutal death’, ‘dystopia’, ‘archaisms and occultism’, ‘religion and satanism’, ‘battle’ and ‘(psychological) madness’, while there is a negative associations with topics like ‘personal life’ and ‘love and romance’.

Source: https://arxiv.org/pdf/1911.04952v2.pdf

https://arxiv.org/pdf/1911.04952v2.pdf

[program-800]

Exploring D:BH Fics (Part 7)

In this post I’ll quickly run through the preprocessing and training of the topic model mentioned here.

Recap: Data was scraped from AO3 in mid-October. I removed any fics that were non-English, were crossovers and had less than 10 words. A small number of fics were missed out during the scrape - overall 13933 D:BH fics remain for analysis.

I then dropped all non-rated fics. I also dropped a Choose Your Own Adventure fic that had 1244 chapters, leaving 12646 D:BH fics for modeling.

Part 1: Publishing frequency for D:BH with ratings breakdown Part 2: Building a network visualisation of D:BH ships Part 3: Topic modeling D:BH fics (retrieving common themes) Part 4: Average hits/kudos/comment counts/bookmarks received (split by publication month & rating) One-shots only. Part 5: Differences in word use between D:BH fics of different ratings Part 6: Word2Vec on D:BH fics (finding similar words based on word usage patterns) Part 7: Differences in topic usage between D:BH fics of different ratings Part 8: Understanding fanon representations of characters from story tags Part 9: D:BH character prominence in the actual game vs AO3 fics

Preface I’ve talked about retrieving the topics of fics automatically (using Latent Dirichlet Allocation, LDA) before in Part 3. I do something similar here (topic modeling still), but with a different algorithm called Structural Topic Modeling (STM). The basic idea is that you may have metadata (e.g. published date, author) that you want to link to each document. This metadata in turn may affect topic prevalence (i.e., how much a topic is used) and/or the vocabulary of the topic.

In this analysis, I assumed that the rating of the fic would affect topic prevalence. I typically work in Python and am complete garbage at R. Unfortunately the package is only available in R so after a lot of suffering, here it is.

1. Preprocessing The bulk of the preprocessing was done previously in Python for my LDA post. I simply reused that (only nouns and verbs, no names, no common words). Note that I chunked each one-shot/chapter into chunks of 1000~2000 words as with the LDA post for better training, so I went from 12646 fics to 45022 chunks (documents).

Using the quanteda library in R, I then stemmed the words in the documents (i.e., cut the words into their pseudo-roots, it gives you ugly results like dancing/dance to danc) for standardisation. I also created bigrams (collocations of two words that occur frequently together) for each document.

I kept only words that appeared in at least 1% and no more than 70% of the documents. After this, 15 documents had to be dropped since they became empty after cleaning.

2. Selecting the STM model As with LDA, STM can help you automatically retrieve topics, but it needs you to input the number of topics you want. R is admittedly pretty rad with the tools it has for this sort of thing. I ran for 5 to 50 topics in steps of 5, so a model for 5 topics, then one for 10 topics, 15 topics...all the way up to 50 topics. All the models had (1) the document’s rating (i.e., the fic rating) and (2) the number of months elapsed from the release of the game when the fic was originally published as covariates affecting the prevalence of the topics in each document.

Following that, I plotted the exclusivity score against the semantic coherence of each model. Ideally you want higher semantic coherence (i.e., less negative numbers on the x-axis, words in a topic typically co-occur within one document) and still maintain good exclusivity (i.e., higher numbers on the y-axis, topics that are more distinguishable from each other).

These two qualities are a trade-off; it’s easy to get higher semantic coherence just by having topics dominated by really frequently used words that will definitely appear together - but then the topics become pretty much meaningless and indistinguishable from each other. We want a model that’s somewhere in the middle for both these qualities.

Based on the plot, the topic numbers of 25~30 seem like a good choice. I went with the 30-topic model.

After selecting the model, it was a simple matter of generating the charts with the inbuilt functions from the stm package.

Final notes The thing that bugs me the most is metadata selection. How can you tell if you’ve selected ‘good’ metadata to model with the documents? How is model fit evaluated and how do you know how useful the model is in real-world scenarios? I’m pretty sure these must be covered somewhere in papers, so I’ll just keep looking.

[thatwarellp-blog]

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·         Technical SEO:Thatware works with the concept of conversion rate optimization-CRO-If any individual or organization feels that they donot have enough conversions of leads from the footfalls on their website, it’s for sure because of low CRO.Thatware has a comprehensive 24-step CRO Strategy.To know more details and in-depth in the 24 steps adopted for CRO, navigate directly to the website of Thatware by clicking this hyperlink.

·         Data driven marketing: Thatware uses customer reviews and customer support conversations to extract data for planning the marketing strategy.Data-driven marketing is based on the concept of building insights pulled from the analysis of big data, collected through consumer interactions and engagements, to form predictions about future behaviors.

·         Advanced social media optimization: Thatware works with a shared responsibility towards your profit contribution.It adopts advanced tools for Social media optimization (SMO) and uses a number of outlets to generate publicity and  increase the awareness of a product, service brand or event.Our goal is shared for both SEO AND SMM -To increase web-traffic and lead conversions.

·         End to end sales cycle:First time ever Thatware has innovated to combine Artificial intelligence for enhancing online marketing. It goes a step further in analysis in the ways which comes inclusive in your entire package of service delivery with an extremely affordable pricing.The package would include complete product or service analysis,detailed organization structure analysis, comprehensive pricing strategy,exhaustive sales strategy,full sales team module of hiring-training-management,reporting and metrics with P&L Analysis,sales strategy consulting,distribution channel management,expansion planning and execution,customer acquisition-retention-monetizing,sales head services.So go ahead and contact Thatware for expert professional services worry free since the motto is to keep you ahead in business using smart ways !

 ·         Word press development:It won’t be wrong to mention here that Thatware is rated as the best SEO service provider by clients,for an instance ,look at the client testimonials on their website for a detailed view.Thatware deploys a team of dedicated and certified wordpress developers and assigns them to projects taken up for quick and updated service delivery and maintainence.

 ·         Google analytics and google tag manager consulting:Thatware combines the 6 crucial components of Google tag manager quite effectively to produce desired results.Thatware helps clients streamline collaboration,helps them Access third-party tags for convenient data tracking,check for errors to fix problems fast,add and update tags in an instant.With the strong and rich knowledge of a developer background of technicians , GTM can be well imbibed for clients.So if you lack time for high-intensity data migration for your large website -Call TuhinBanik -the founder of Thatware -and your problem is fixed.

·         R programming and consultancy:Thatware works with a highly qualified and dedicated team of coders to ensure quality service delivery. These experts demonstrate qualities imbibed through many years of experience and consulting for different clients – so be rest assured!

·         Neural networks and deep learning service:Tuhin works with a team of experienced deep learning experts who provide 100% dedicated support to client requirements and queries.They deploy a full-proof model with higher efficiency and best ROC Curve within competitive industrial sector.

·         Artificial intelligence as a service(AlaaS):Thatware founded by TuhinBanik is completely an artificial intelligence based company backed by a strong team of data scientists.The team relies on automation enabled using latest technologies and enhancing SEO services with artificial intelligence.

 Still wondering which agency would be best for you ?

Well of course the one which enables a good balance of all the aspects of SEO and SMM by following a holistic approach.

[davidrussellschilling]

Using Vector Space Models

Using Vector Space Models

C O N T E N T S:

KEY TOPICS

I?ve written a number of convenience functions and object oriented syntax for some of the most useful operations you might perform the actual vector space that a WEM puts out. 12 So the basic operations of a vector space model can be expressed through straightforward arithmetic.(More…)

• Experiments with 3 semantic space models: WN, a thesaurus, and a topic based model.

[blogchaindeveloper]

Natural Language Processing (NLP) and Machine Learning: An Overview

The combination of machine learning (ML) and natural language processing (NLP) has accelerated the development of intelligent systems that can comprehend and produce language similar to that of humans in the field of artificial intelligence. This essay dives into the technical details that support NLP and ML in an attempt to present a thorough overview of their intersection.

Defining Natural Language Processing (NLP)

The study of how computers and human language interact is the focus of the artificial intelligence (AI) subfield of natural language processing, or NLP. The intention is to close the gap between human communication and computer capabilities by enabling machines to understand, interpret, and produce language that is similar to that of humans.

The Role of Machine Learning in NLP

At the basis of NLP lies Machine Learning (ML), a paradigm that empowers computers to identify patterns and generate predictions from data without explicit programming. Machine learning algorithms play a crucial role in providing NLP systems with the capacity to comprehend subtleties of language, adjust to various settings, and enhance their performance through repeated learning.

Foundations of Natural Language Processing

Linguistics and NLP

Understanding linguistics is essential to understanding language. NLP algorithms use parts of speech recognition, sentence parsing, and semantic meaning extraction based on linguistic principles. It is essential for NLP systems to comprehend syntactic and semantic structures in order to process language efficiently.

Tokenization and Text Preprocessing

Dissecting text into smaller pieces, or tokens, is one of the first steps in natural language processing (NLP). To grasp and analyze language structure, tokenization is necessary. Text preprocessing methods, such as lemmatization and stemming, further clean the data, lowering dimensionality and improving the effectiveness of NLP models.

Named Entity Recognition (NER)

Identifying entities such as names, locations, and organizations within text is a critical aspect of NLP. NER is a sub-task that employs ML algorithms to automatically detect and classify entities, contributing to the extraction of meaningful information from unstructured text.

Machine Learning Algorithms in NLP

Supervised Learning for Text Classification

Supervised learning is a prevalent approach in NLP, especially for tasks like text classification. With labeled datasets, algorithms can learn to categorize text into predefined classes. This is widely applied in sentiment analysis, spam detection, and topic categorization.

Unsupervised Learning for Clustering and Topic Modeling

In scenarios where labeled data is scarce, unsupervised learning comes into play. Clustering algorithms group similar documents, uncovering hidden patterns within large text corpora. Topic modeling, exemplified by techniques like Latent Dirichlet Allocation (LDA), uncovers underlying themes in a collection of documents without prior categorization.

Recurrent Neural Networks (RNNs) for Sequential Data

RNNs are a class of neural networks designed for sequential data, making them well-suited for language-related tasks. Their ability to capture dependencies between words in a sequence is invaluable for applications like language modeling, machine translation, and text generation.

Transformers: Revolutionizing NLP

A paradigm change in NLP was brought about by the introduction of transformers, as demonstrated by models such as BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer). By utilizing attention mechanisms, these models are able to efficiently capture contextual information, which facilitates a more profound comprehension of linguistic subtleties.

Two-way Contextual Representations: BERT

The contextualized word embeddings are where BERT, a pre-trained transformer model, shines. BERT performs remarkably well in tasks like named entity recognition, text summarization, and question answering because it takes into account the complete context of a word within a sentence in both directions.

Transformer using Generative Pre-training (GPT)

Conversely, GPT concentrates on generating tasks. Through extensive and varied textual training, GPT produces logical and contextually appropriate writing. This has significant ramifications for both the creation of original texts and the comprehension of natural language.

Challenges in NLP and ML Integration

Ambiguity and Polysemy

The inherent ambiguity and polysemy of natural language pose significant challenges. Words often have multiple meanings depending on context, making it intricate for ML models to discern the intended sense accurately.

Lack of Contextual Understanding

While transformers like BERT have made strides in capturing contextual information, achieving a deep understanding of context in highly dynamic conversations remains a challenge. Real-world interactions often involve subtle nuances that are difficult for machines to grasp completely.

Data Bias and Ethical Concerns

The reliance on large datasets for training ML models raises concerns about bias in NLP systems. Biased data can perpetuate and amplify existing societal biases, leading to unfair outcomes. Addressing these ethical concerns is crucial for the responsible development and deployment of NLP applications.

Advancements and Future Directions

Transfer Learning in NLP

Transfer learning has emerged as a powerful technique in NLP, enabling models trained on one task to be fine-tuned for another related task. This approach significantly reduces the need for extensive labeled data for every specific application, enhancing the efficiency of NLP systems.

Multimodal NLP

As AI systems evolve, the integration of multiple modalities, such as text, images, and speech, becomes increasingly important. Multimodal NLP aims to develop models that can comprehend and generate content across different modes of communication, paving the way for more versatile and human-like interactions.

Explainable AI in NLP

The interpretability of NLP models is gaining prominence, especially in critical applications like healthcare and finance. Explainable AI techniques aim to demystify the decision-making process of complex NLP models, providing insights into how and why certain conclusions are reached.

Applications of NLP and ML

Virtual Assistants and Chatbots

The widespread adoption of virtual assistants like Siri, Alexa, and Google Assistant exemplifies the successful integration of NLP and ML. These systems can understand user queries, retrieve relevant information, and execute commands, showcasing the practical applications of language processing technologies.

Sentiment Analysis for Business Insights

Businesses leverage sentiment analysis, a subfield of NLP, to gauge public opinion and sentiment towards their products or services. Analyzing social media posts, customer reviews, and news articles provides valuable insights that can inform strategic decisions and enhance customer satisfaction.

Language Translation and Cross-cultural Communication

NLP plays a pivotal role in breaking down language barriers. Machine translation models, such as Google Translate, utilize sophisticated algorithms to translate text between languages, facilitating cross-cultural communication and collaboration on a global scale.

AI Certification and Expertise

AI Certification for NLP Engineers

In the rapidly evolving landscape of AI and NLP, acquiring certifications is crucial for professionals aiming to demonstrate their expertise. The Blockchain Council certification offers a comprehensive program for NLP engineers, covering advanced concepts, practical applications, and ethical considerations.

AI Developer Certification: A Gateway to NLP Mastery

For aspiring developers venturing into the world of NLP, obtaining an AI developer certification is a strategic move. Such certifications, like those offered by the Blockchain Council, validate proficiency in implementing NLP algorithms, designing language models, and addressing real-world challenges.

Certified Chatbot Expert: Mastering Conversational AI

A certified chatbot expert is someone who has developed conversational agents using NLP and ML and is skilled in this field. With the help of the Blockchain Council's certification program, experts may create, implement, and enhance chatbots for a variety of uses, such as virtual assistants and customer service.

Conclusion

The fields of machine learning and natural language processing come together to enable computers to comprehend, interpret, and produce language that is similar to that of humans. Innovation has been a defining feature of the combination of NLP and ML, from the fundamentals of languages to the revolutionary power of transformer models. 

It becomes increasingly important to address issues like data bias and ethical problems as we manage the complexities of language. In the future, developments in explainable AI, multimodal NLP, and transfer learning should progress the area and get us closer to the goal of creating really intelligent and compassionate computers. Taking part in accredited certification programs, like those run by the Blockchain Council, offers people an organized opportunity to improve their abilities and add to the ever-evolving field of NLP and AI.

[omrimedal-blog]

Langdetect Can the Microsoft Language Detection API recognize nonsense gibberish words?

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Can the microsoft language detection api recognize nonsense gibberish words? langdetect

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      Churn prediction python language. Google language detection apical. Unsupervised language identification based on Latent Dirichlet Allocation.