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.

🔍 LangKit: An open-source toolkit for monitoring Large Language Models (LLMs). 📚 Extracts signals from prompts & responses, ensuring safety & security. 🛡️ Features include text quality, relevance metrics, & sentiment analysis. 📊 A comprehensive tool for LLM observability. 👀

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

[NAACL 2024] Data and code for our paper "Sentiment Analysis in the Era of Large Language Models: A Reality Check"

🚀 A New DAPO Algorithm for Stock Trading (arXiv:2505.06408) Implementation of our IEEE IDS 2025 accepted algorithm combining Dynamic Sampling Policy Optimization (DAPO), Group Relative Policy Optimization (GRPO), and LLM-driven risk/sentiment signals for efficient and profitable stock trading on the NASDAQ-100 index.

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.

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Java 23, SpringBoot 3.4.1 Examples using Deep Learning 4 Java & LangChain4J for Generative AI using ChatGPT LLM, RAG and other open source LLMs. Sentiment Analysis, Application Context based ChatBots. Custom Data Handling. LLMs - GPT 3.5 / 4o, Gemini Pro 1.5, Claude 3, Llama 3.1, Phi-3, Gemma 2, Falcon 3, Qwen 2.5, Mistral Nemo, Wizard Math

H&M Seasonal Campaign Sentiment Analysis – Case Study & Replication Package for BUS2503 / AI for Business using KNIME, Python, and LLM ChatGPT as a co‑tutor.

A curated list of Affective Computing & Emotion AI: Papers, datasets, and toolkits for Multimodal Emotion Recognition, Emotional Reasoning, Multimodal Sentiment Analysis, and Empathetic LLMs/MLLMs.

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.

Multiple LLM Based AI Agent work together to gather information about stocks, to process financial details and market sentiments and to advice to perform trade.

Code Implementation about "Self-adaptive LLM instructions optimization for aspect-based sentiment analysis by incorporating emotion-oriented in-contexts"

📡 Extract crypto market signal from news using Python and LLMs 📡

LLM and NLP models in Cryptocurrency Sentiment Analysis: A Comparative Classification Study

Lightweight research library for benchmarking sentiment and behavioral metrics in LLM trading experiments

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.

Sentiment example using TypeChat with a self hosted LLM

Fine-Tuning Microsoft Phi-2 for financial news sentiment analysis improved accuracy from 0.349 to 0.872 and reduced training loss by 38%. Dataset: 4846 entries with "Neutral," "Positive," and "Negative" labels.

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.

This repository contains the resources, code, and documentation for LlamaLens, a specialized multilingual large language model (LLM) designed to analyze news and social media content effectively. LlamaLens supports multiple languages, including Arabic, English, and Hindi, and is tailored for diverse tasks such as sentiment analysis, misinformation.

How to use Large Language Model for Sentiment Analysis

Python script for analyzing financial news sentiment using GPT-based Large Language Models (LLMs). It demonstrates how to leverage LSEG's Data Library to fetch news headlines and apply LLMs for sentiment classification, helping in the development of advanced investment strategies

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.

Efficient fine-tuned large language model (LLM) for the task of sentiment analysis using the IMDB dataset.

Multi-agent trading backtest framework (Rigid): Sentiment, Fundamental, and Valuation agents feed a Reasoning Agent that makes trading decisions; a Portfolio Manager handles allocation. Uses manually curated news data and historical prices to evaluate LLM performance in financial analysis.

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.