LangChain & Prompt Engineering tutorials on Large Language Models (LLMs) such as ChatGPT with custom data. Jupyter notebooks on loading and indexing data, creating prompt templates, CSV agents, and using retrieval QA chains to query the custom data. Projects for using a private LLM (Llama 2) for chat with PDF files, tweets sentiment analysis.

reddacted lets you analyze & sanitize your online footprint using LLMs, PII detection & sentiment analysis to identify anything that might reveal personal info you may not want correlated with your anonymous profile

This project serves as a comprehensive guide to building an end-to-end data engineering pipeline using TCP/IP Socket, Apache Spark, OpenAI LLM, Kafka and Elasticsearch. It covers each stage from data acquisition, processing, sentiment analysis with ChatGPT, production to kafka topic and connection to elasticsearch.

Voice of the Customer (VoC) to enhance customer experience with serverless architecture and sentiment analysis, using Amazon Kinesis, Amazon Athena, Amazon QuickSight, Amazon Comprehend, and ChatGPT-LLMs for sentiment analysis.

Simulated real time trading app for stocks and crypto with RAG LLM that can execute orders (GPT4o, Claude 3.5, AI web search w/ Perplexity Sonar) Supports futures, options, automated risk management (stop-loss, DRIP), tax/P&L realism, current news and equity sentiment, portfolio risk and diversification analysis. Modern, simple and educational.

The project demonstrates an example of how to use a supervised learning task using GPT-3.5 with JSON export, evaluating reviews in different languages

LLM Agent that performs sentiment analysis of drawings and natural language using a combination of Google Gemini Vision model and GPT-4 Turbo with LlamaIndex.

Real-time sentiment analysis of Slack messages with Kafka and Flink, using the Stanford NLP algorithm and the GPT-4 LLM.

Developed an end-to-end stock price prediction model by integrating LLM-based sentiment analysis of financial news with time series forecasting, leveraging Python, TensorFlow, and Hugging Face Transformers; achieved enhanced prediction accuracy by incorporating sentiment data.

ASP.NET Core API for document processing using local LLMs. Features include summarization, analysis, sentiment detection, and document comparison. Compatible with OpenAI API-standard LLM servers.

Forecasting Bitcoin returns through time series analysis, emphasizing sentiment analysis on news (using BERT LLM), social media, and Google search trends, with the final model based on Random Forest, augmented with engineered memory features.

Mai is an emotionally intelligent, voice-enabled AI assistant built with FastAPI, Together.ai LLMs, memory persistence via ChromaDB, and real-time sentiment analysis. Designed to feel alive, empathetic, and human-like, Mai blends the charm of a flirty cyberpunk companion with the power of modern multimodal AI.

The GenCode library is a first-of-its-kind powerful ORM for LLM like GPT4 for helping LLM understand your application model. Perform NLP based model manipulaton and decision making fast for your .NET apps. With fast integration, package harness power of language tasks, including text generation, sentiment analysis, & more. See docs & examples.

A production-style, agentic AI trading bot that combines LLM reasoning, sentiment analysis, market indicators, and broker APIs to make and (optionally) execute trading decisions.

Identify and Analyze Negative Reviews with Sentiment Analysis, Topic Modeling and LLM fine-tuning

Using BigQuery and Dataform integration with LLMs in Vertex AI to perform sentiment analysis at Scale

A research-oriented multi-agent trading simulator using open LLMs. Specialized agents perform news sentiment analysis (DistilBERT), technical analysis via moving-average crossovers, and trade decision generation with GPT-2, coordinated by an execution simulator and orchestrator.

Flask-based interactive storytelling application that implements a LLM character simulation engine with persistent emotional state management, featuring a "Personality Orrery" system that tracks dynamic character traits, sentiment analysis, and environmental interactions to create evolving AI personas in explorable game worlds.

Unlock real-time insights with this comprehensive guide to building an end-to-end data pipeline. Leverage TCP/IP sockets for data ingestion, Apache Spark for powerful processing, OpenAI's LLM (ChatGPT) for sentiment analysis, and seamlessly integrate Kafka & Elasticsearch for scalable storage and querying.

LLM chat bot with real time sentiment analysis powered by OpenVINO

Analyzes Gaza-related tweets using LLaMA-3 via GroqCloud. Combines Selenium scraping with LLM-powered sentiment analysis to surface public emotion trends.

Code from the workshop I gave at La Salle University on AI, LLMs and a hands-on sentiment analysis project with Python, Groq and Langchain

Programming with torch is becoming fun day by day. And with that finetuning a LLM is becoming interesting too! I have fine tuned FinBERT LLM an opensource finance transformer model to give stock market prediction based on the news headlines or financial articles with the help of sentiment analysis on the text input.

This MCP Server is a lightweight, production-ready backend built with FastAPI that dynamically routes tasks—such as chat, summarization, sentiment analysis, and recommendations—to the appropriate AI model using both rule-based and LLM-powered intelligent routing.

In this project, we want to test how LLMs can be used for identifying sentiments in products reviews in an ecommerce platform

Developed a Python-based API that processes customer reviews, performs sentiment analysis using a Large Language Model, and returns structured results.

A complete guide to NLP and ML for text processing, covering rule-based models, RNNs, CNNs, Transformers, entity detection, sentiment analysis, LLM fine-tuning, RAG, and prompt engineering with tools like Langchain and Ollama.

I created a new technique to do sentiment analysis with 98% probability using multiple techniques combined to from a new method. I made a video on this whole project and show you, how “Next Gen Sentiment” is much better then NLTK, TEXTBLOB or LLMs.

Stock Scanner with Local LLM for Sentiment Analysis - McMaster Engineering Capstone 2025. Real-time stock market scanner with AI-driven sentiment analysis using a local LLM. Analyze news, social media for trading insights. Custom technical indicators, privacy-focused, user-friendly. Ideal for investors, traders, developers in AI finance.

Scalable Django DRF backend for advanced NLP: Sentiment Analysis & Text Summarization via API. Features secure JWT auth, user management, history, Redis caching, and Docker. Integrates LLMs (Gemini) with future local AI support