#NLP Projects

[techieyan]

Revolutionizing Text Analysis with NLP Projects in Artificial Intelligence

The field of artificial intelligence (AI) has seen tremendous growth and development in recent years, with advancements in machine learning, deep learning, and natural language processing (NLP). NLP, in particular, has revolutionized the way we analyze text data, providing powerful tools and techniques for extracting insights and meaning from large volumes of text.

NLP is a branch of AI that focuses on the interaction between computers and human language. It enables computers to understand, interpret, and generate human language, allowing them to process and analyze text data in a similar way to how humans do. With the increasing amount of unstructured data in the form of text, such as social media posts, customer reviews, and news articles, NLP has become an essential tool for businesses and organizations looking to gain valuable insights from this data.

One of the most significant applications of NLP in AI projects is sentiment analysis. Sentiment analysis is the process of identifying and extracting emotions, opinions, and attitudes from text data. With the help of NLP techniques, sentiment analysis can accurately identify the sentiment expressed in a piece of text, whether it is positive, negative, or neutral. This is particularly useful for businesses as it allows them to understand how their customers feel about their products, services, and brand, and make data-driven decisions to improve their offerings.

Another NLP project that has revolutionized text analysis is named entity recognition (NER). NER is a technique that identifies and classifies named entities in text, such as people, places, organizations, and dates. It enables computers to understand the context of a text and extract relevant information, making it an essential tool for tasks such as information extraction, question-answering, and document summarization.

NLP also offers powerful tools for text classification, which involves categorizing text into predefined categories. This is useful for tasks such as spam detection, topic classification, and sentiment analysis. With the help of NLP techniques, computers can learn to classify text accurately, saving businesses and organizations time and resources in manual classification.

One of the most exciting NLP projects in AI is natural language generation (NLG). NLG is the process of generating human-like text from data, making it possible for computers to write articles, reports, and summaries automatically. This has significant implications for various industries, such as journalism, content creation, and customer service. With NLG, businesses can generate personalized content for their customers and automate routine tasks, freeing up human resources for more complex tasks.

NLP has also made significant contributions to the field of machine translation, allowing computers to translate text from one language to another accurately. With the help of NLP techniques, machines can understand the context and nuances of different languages and produce accurate translations. This has opened up new opportunities for global businesses to expand their reach and communicate with customers in their preferred language.

In addition to these applications, NLP has also been used in AI projects for text summarization, question-answering, and text-to-speech conversion. These applications have not only improved the efficiency and accuracy of text analysis but also opened up new possibilities for businesses and organizations to leverage the power of NLP in their operations.

In conclusion, NLP has played a significant role in revolutionizing text analysis in AI projects. Its ability to understand and analyze human language has enabled computers to extract valuable insights, information, and meaning from large volumes of text data. With the continuous advancements in NLP, we can expect to see even more impressive applications that will further enhance the capabilities of AI in text analysis. As businesses and organizations continue to generate and collect vast amounts of text data, NLP will become an increasingly crucial component of AI projects, paving the way for a more efficient, accurate, and intelligent future.

[anonymousdormhacks]

Google says alexander "cheated on his wife" hamilton rights ig

[detroitography]

Detroit by the Numbers: State of the City 2014 - 2025

by: Ted Tansley, Data Analyst Mike Duggan’s tenure as Mayor of Detroit has been focused on data. The number of residents in the city, number of demolitions, number of jobs brought to the city, number of affordable housing built or preserved. All data points get brought up in his yearly State of the City address where he makes his case to the public that he and his team have been doing a good job…

[manmishra]

AI Integration in Software Development Explained

Dive into the future of software development with our latest article on AI Integration in Software Development! Discover how AI is transforming code generation, testing, and project management, enhancing productivity and collaboration. Don't miss out—read

Artificial Intelligence (AI) is transforming the software development landscape. It enhances productivity, accuracy, and innovation across various stages of the development lifecycle. This article explores how AI integrates into software development. It examines its benefits, challenges, and practical applications. The article includes code snippets to illustrate AI’s capabilities. Key Areas of…

[loveinthetimeofanarchy]

not to be a complete hater but using machine learning to translate video of sign language into english text is something that college students have been making successful projects of for at least 10 years. the fact that none of these tools has entered any sort of zeitgeist is a problem of economic prioritization

[datascienceassignmenthelp]

New Era of Natural Language Processing

[datascienceassignment]

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[coachskillsacademy]

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[fandomtrumpshate]

FTH 2025 Supported Org: News Literacy Project

In the age of the internet, one of the greatest threats to public well-being is misinformation. The sheer volume of available information is enough to cause us mental and emotional damage that can make it more difficult to react thoughtfully. Furthermore, our information environment is saturated in misinformation and disinformation, often explicitly engineered to drive our political choices. Furthermore, in the past few weeks, Meta has followed X's lead and decided to discontinue fact-checking on Facebook and Instagram.

A few days after the election, the head of Media Matters described the United States as "pickled in misinformation," and all signs suggest that the problem is getting worse, not better.

The News Literacy Project, a nonpartisan education nonprofit founded in 2008, aims to improve news literacy -- the ability to determine the credibility of news and other information and to recognize the standards of fact-based journalism to know what to trust, share and act on -- in this challenging, high-speed information environment. The NLP is building a national movement to create systemic change in American education to ensure all students are skilled in news literacy before they graduate high school, giving them the knowledge and ability to participate in civic society as well-informed, critical thinkers. A more news-literate populace will be less likely to fall for online hoaxes, and also better equipped to make well-informed decisions about our common lives and governance.

You can support News Literacy Project as a creator in the 2025 FTH auction (or as a bidder, when the time comes to donate for the auctions you’ve won.)

[techieyan]

From Concept to Completion: How to Choose and Execute an AI Project for Your Final Year

Artificial Intelligence (AI) has become a buzzword in the tech industry, with its potential to transform and revolutionize various sectors. As a final-year student, you may be considering an AI project for your final year. However, with the vastness and complexity of the subject, it can be challenging to know where to begin. In this article, we will guide you on how to choose and execute an AI project for your final year.

1. Identify Your Interest and Goal

The first step in choosing an AI project is to identify your interests and goals. AI is a vast field with numerous subfields such as machine learning, natural language processing, computer vision, and robotics. Each subfield has its own set of techniques, algorithms, and applications. Therefore, it is essential to have a clear understanding of what interests you the most and what you want to achieve through your project.

2. Research Existing Projects

Once you have identified your interest and goal, the next step is to research existing AI projects. This will help you understand the current trends, techniques, and applications in your chosen field. It will also give you a better idea of what has been done before and what gaps you can fill with your project. You can look for research papers, articles, and projects on online platforms such as arXiv, Google Scholar, and GitHub.

3. Consult with Your Supervisor and Peers

Your supervisor and peers can provide valuable insights and guidance in choosing an AI project. They can also help you refine your ideas and provide feedback on the feasibility and scope of your project. Consult with them regularly throughout the process to ensure that you are on the right track and make necessary adjustments if needed.

4. Define Your Project Scope

Once you have chosen a topic for your AI project, it is crucial to define its scope. AI projects can be complex and time-consuming, so it is essential to set realistic goals and expectations. Define the specific problem you want to solve, the data you will need, and the techniques you will use. It is also crucial to consider the resources and time available for your project.

5. Collect and Prepare Data

Data is the foundation of any AI project. Depending on your project, you may need to collect your data or use existing datasets. The quality and quantity of your data can significantly impact the performance of your project. Therefore, ensuring that your data is clean, relevant, and sufficient for your project is vital.

6. Choose the Right Tools and Techniques

There are various tools and techniques available for AI projects, and choosing the right ones can make a significant difference in the success of your project. Consider the type of data you have, the problem you are trying to solve, and your programming skills in selecting the tools and techniques. It is also beneficial to experiment with different tools and techniques to find the ones that work best for your project.

7. Implement and Test Your Project

With your data, tools, and techniques in place, it is time to implement and test your project. This step involves coding, training your model, and evaluating its performance. It may require multiple iterations and adjustments to achieve the desired results. It is crucial to document your progress and results throughout this process.

8. Evaluate and Refine Your Project

Once your project is implemented, it is essential to evaluate its performance and refine it if necessary. This step involves analyzing the results, identifying shortcomings, and making necessary improvements. It is also crucial to compare your project's performance with existing solutions to determine its effectiveness.

9. Write Your Final Report

The final step in executing an AI project is to write your final report. This report should document your project's background, goals, methodology, results, and conclusions. It is also essential to include any challenges faced and how they were overcome. Your report should be well-structured, concise, and supported by evidence.

In conclusion, choosing and executing an AI project for your final year can be a daunting task, but with the right approach and guidance, it can be a rewarding experience. Remember to choose a topic that interests you, define the scope of your project, and use the right tools and techniques. Finally, don't be afraid to seek help and collaborate with others along the way. Good luck with your AI project!

[bayesic-bitch]

for their final project, one of my students built an NLP model to read your reddit thread and decide if you're the asshole or not

[aorish]

i have to do an NLP project with Recurrent Neural networks for this class and it's kind of insulting because they're like. noticeably worse than transformers even though you also have to clean the data ahead of time? why are we doing this stone age bullshit from a decade ago

[oxford-garments]

Ifá - Wikipedia

MYR. ALEV΂I

Quasi-religions are non-religious movements which have unintended similarities to religions, such as political movements. According to Ifá teaching, the divinatory system is overseen by an orisha spirit, Orunmila, who is believed to have given it to humanity. Ifá is organised as an initiatory tradition, with an initiate called a babaláwo or bokɔnɔ. Quasi Protomartyr Anglican Theology for Mirror for/of Princes Chieftain Church; each book is a humanoid book turned Deity with Pendant, Mars as a Air Sign and Jupiter-sol Mars-Saturn as Beginner Planets; Quasi Invocation: Chief of Ethnic Group, Stars Exaltation Realignment through Mars, Military Expedition, Sabaoth Summing through Sun Monotheism in exchange for Ancestry; Kgosi Quasi Goetia: Kgosi Solaí Planet Monotheism, Mars Jupiter Sol all Humanoid Shadows, Mirror for Princes Spirits, Psychiatric Disorder Crowns, Anthropology and Philosopher Crista, Political Behavior of Status, Galaxy with Jupiter Sol-Saturn Equivalent Planet. Jehovah Sabaoth is one of God’s names in the Bible. It occurs more than 270 times in the Old Testament. It combines God’s personal name, Jehovah (Yahweh), with the Hebrew word, sabaoth, meaning “host” or “multitude.” So Jehovah Sabaoth means “The Lord of Hosts.” The important thing about this name for God is that whether it is armies, angels, or stars, Jehovah Sabaoth, the Lord of Hosts, rules over all things both on earth and in heaven.

CONGO TESTAMENT

First book Myr. Senghor and Ethnic Group Fon Tswana Congo

Birth of Obasian Virgos: Mars-Saturn Jupiter-Solaí

Croix du Zavié (Most High’s Cross) 4 Fleur-de-lis with Double Bar

a covenant, agreement, pact

The term "testament," as applied to the two parts of the Bible, means: a covenant, agreement, pact. In the language of the Bible it denotes the agreement or pact between God and man: Man agreed to do certain things and God, in return, promised certain blessings.

This "ravenous bird" is a symbol of those nations whom God employs and sends forth to do a work of destruction, sweeping away whatever is decaying and putrescent ( Matthew 24:28 ; Isaiah 46:11 ; Ezekiel 39:4 ; Deuteronomy 28:49 ; Jeremiah 4:13 ; 48:40 ). It is said that the eagle sheds his feathers in the beginning of spring, and with fresh plumage assumes the appearance of youth. To this, allusion is made in Psalms 103:5 and Isaiah 40:31 . God's care over his people is likened to that of the eagle in training its young to fly ( Exodus 19:4 ; Deuteronomy 32:11 Deuteronomy 32:12 ). Throughout the Bible, the eagle is a sign of vengeance in the scriptures. In Revelations, however, the eagle represents the forerunner of the judgment that is coming and that they still have time to repent their sins.

Fon was a highly militaristic language constantly organised for warfare; it captured captives during wars and raids against neighboring societies. Tactics such as covering fire, frontal attacks and flanking movements were used in the warfare of Fon. The military of Fon was divided into two units: the right and the left. The right was controlled by the migan and the left was controlled by the mehu.

There is an effort to create a machine translator for Fon (to and from French), by Bonaventure Dossou (from Benin) and Chris Emezue (from Nigeria).[14] Their project is called FFR.[15] It uses phrases from Jehovah's Witnesses sermons as well as other biblical phrases as the research corpus to train a Natural Language Processing (NLP) neural net model.[16] Suppressive Forts Defense and Partisan Raid for Sabotage Offense.

Harmony and Contrast Guerilla Warfare (Partisan Raids for Sabotage): Raiding, also known as depredation, is a military tactic or operational warfare "smash and grab" mission which has a specific purpose. Raiders do not capture and hold a location, but quickly retreat to a previous defended position before enemy forces can respond in a coordinated manner or formulate a counter-attack. Raiders must travel swiftly and are generally too lightly equipped and supported to be able to hold ground. A raiding group may consist of combatants specially trained in this tactic, such as commandos, or as a special mission assigned to any regular troops.[1] Raids are often a standard tactic in irregular warfare, employed by warriors, guerrilla fighters or other irregular military forces. A partisan is a member of a domestic irregular military force formed to oppose control of an area by a foreign power or by an army of occupation by some kind of insurgent activity. Sabotage is a deliberate action aimed at weakening a polity, government, effort, or organization through subversion, obstruction, demoralization, destabilization, division, disruption, or destruction. One who engages in sabotage is a saboteur. Saboteurs typically try to conceal their identities because of the consequences of their actions and to avoid invoking legal and organizational requirements for addressing sabotage.

Harmony and Contrast Siege Warfare (Suppressive Forts): A siege (Latin: sedere, lit. 'to sit')[1] is a military blockade of a city, or fortress, with the intent of conquering by attrition, or by well-prepared assault. Siege warfare (also called siegecrafts or poliorcetics) is a form of constant, low-intensity conflict characterized by one party holding a strong, static, defensive position. Consequently, an opportunity for negotiation between combatants is common, as proximity and fluctuating advantage can encourage diplomacy. A fortification (also called a fort, fortress, fastness, or stronghold) is a military construction designed for the defense of territories in warfare, and is used to establish rule in a region during peacetime. The term is derived from Latin fortis ("strong") and facere ("to make").[1] In military science, suppressive fire is "fire that degrades the performance of an enemy force below the level needed to fulfill its mission"[clarification needed]. When used to protect exposed friendly troops advancing on the battlefield, it is commonly called covering fire. Suppression is usually only effective for the duration of the fire.[1] It is one of three types of fire support, which is defined by NATO as "the application of fire, coordinated with the maneuver of forces, to destroy, neutralise or suppress the enemy".

In the Hebrew Bible, the destroying angel (Hebrew: מַלְאָך ה��מַשְׁחִית, malʾāḵ hamašḥīṯ), also known as mashḥit (מַשְׁחִית mašḥīṯ, 'destroyer'; plural: מַשְׁחִיתִים, mašḥīṯīm, 'spoilers, ravagers'), is an entity sent out by God on several occasions to deal with numerous peoples.

These angels (mal’āḵīm) are also variously referred to as memitim (מְמִיתִים, 'executioners, slayers'), or, when used singularly, as the Angel of the Lord. The latter is found in Job 33:22, as well as in Proverbs 16:14 in the plural "messengers of death". Mashchith was also used as an alternate name for one of the seven compartments of Gehenna.[2][3]

In 2 Samuel 24:15-16, the destroying angel almost destroyed Jerusalem but was recalled by God. In 1 Chronicles 21:15, the same "Angel of the Lord" is seen by David to stand "between the earth and the heaven, with a drawn sword in his hand stretched out against the Hebrews' enemies". Later, in 2 Kings 19:35, the angel kills 185,000 Assyrian soldiers.

In the Book of Enoch, angels of punishment and destruction belong to a group of angels called satans with Satan as their leader. First, they tempt, then accuse, and finally punish and torment both wicked humans and fallen angels.[4]

In Judaism, such angels might be seen as created by one's sins. As long as a person lives, God allows them to repent. However, the angels of destruction can execute the sentence proclaimed in the heavenly court after death.[5] Also called Malachei Habala ("Sabotage Angels"), they punish sinners in the underworld and are equated with Shedim (demons) (Berakhot 51a; Ketubot 104a; Sanhedrin 106b).

The angels of punishment as satans are recounted in Islam in the form of a hadith. According to which, a murderer is instructed to repent from their sins by leaving their evil environment and moving to a better one. However, they die on their way, thereupon a disagreement between the angels of mercy and the angels of punishment under the leadership of Iblīs (Satan) occurs, who may take the soul of the repenting murderer.[6]

However, Satan did not have control over those angels as he had lost authority during the rebellion, instead tempting and manipulating others to do his dirty work.[citation needed] As he was not the one committing the sin, punishment goes to the wrong doer, and Satan instead will become a victim along with other sinners from humankind to be tortured by those angels.[7][8]

V΂I DIVINATION

[d0nutzgg]

This is part of a new project I am doing for a Facebook app that can alert someone when there is suspicious activity on their account, and block people who post rude comments and hate speech using a BERT model I am training on a dataset of hate speech. It automatically blocks people who are really rude / mean and keeps your feed clean of spam. I am developing it right now for work and for @emoryvalentine14 to test out and maybe in the future I will make it public.

I love NLP :D Also I plan to host this server probably on Heroku or something after it is done.

[aktechworld]

Role of AI and Automation in Modern CRM Software

Modern CRM systems are no longer just about storing contact information. Today, businesses expect their CRM to predict behavior, streamline communication, and drive efficiency — and that’s exactly what AI and automation bring to the table.

Here’s how AI and automation are transforming the CRM landscape:

1. Predictive Lead Scoring

Uses historical customer data to rank leads by conversion probability

Prioritizes outreach efforts based on buying signals

Reduces time spent on low-potential leads

Improves sales team performance and ROI

2. Smart Sales Forecasting

Analyzes trends, seasonality, and deal history to forecast revenue

Updates projections in real-time based on new data

Helps sales managers set realistic targets and resource plans

Supports dynamic pipeline adjustments

3. Automated Customer Support

AI-powered chatbots handle FAQs and common issues 24/7

Sentiment analysis flags negative interactions for human follow-up

Automated ticket routing ensures faster resolution

Reduces support workload and boosts satisfaction

4. Personalized Customer Journeys

Machine learning tailors emails, offers, and messages per user behavior

Automation triggers based on milestones or inactivity

Custom workflows guide users through onboarding, upgrades, or renewals

Improves customer engagement and retention

5. Data Cleanup and Enrichment

AI tools detect duplicate records and outdated info

Automatically update fields from verified external sources

Maintains a clean, high-quality CRM database

Supports better segmentation and targeting

6. Workflow Automation Across Departments

Automates repetitive tasks like task assignments, follow-ups, and alerts

Links CRM actions with ERP, HR, or ticketing systems

Keeps all teams aligned without manual intervention

Custom CRM solutions can integrate automation tailored to your exact process

7. Voice and Natural Language Processing (NLP)

Transcribes sales calls and highlights key insights

Enables voice-driven commands within CRM platforms

Extracts data from emails or chat for automatic entry

Enhances productivity for on-the-go users