A hybrid NLP engine utilizing rule-based linguistic patterns and neural network classification for precise sentiment quantification.

NLP pipeline for classifying sentiment in financial news and generating weekly summaries of market-moving events. Supports sentiment-informed investment analysis.

This is a comprehensive data solution designed to streamline the collection, transformation, and analysis of financial data related to gold as a commodity for investment portfolio management.

Utilizing AWS EMR clusters with attached JupyterEnterprise Packages, and Google Colabs. This Twitter Sentiment Analysis was done in a Hadoop Environment using Pyspark and EDA was done using matplotlib, seaborn and other related libraries.

A flexible sentiment analysis classifier package supporting multiple pre-trained models, customizable preprocessing, visualization tools, fine-tuning capabilities, and seamless integration with pandas DataFrames.

This project focuses on extracting and analyzing social media data from Reddit to uncover meaningful insights . The goal is to help marketing analysts understand trending topics, audience sentiment, and engagement patterns. By examining these insights, marketers can make data-driven decisions to enhance campaign strategies and improve engagement.

Song-lyric mood prediction with explanations: FastAPI + ONNX serving a fine-tuned DistilBERT and a SHAP-explainable baseline, Qdrant vector search over 76k songs, CI, live demo.

sentiment analysis

Enterprise-grade retail intelligence platform with competitor scraping, ETL workflows, and analytics dashboard.

BERT-Sentiment-Analysis: A project using BERT-based models to perform sentiment analysis on multilingual text data, with examples in Arabic and English. This repository demonstrates loading, preprocessing, and analyzing text data with pre-trained models for accurate sentiment classification.

This is a web application that uses an NLP model to perform sentiment analysis.

This repository features a BERT-based model fine-tuned for sentiment analysis, classifying text as Positive, Negative, or Neutral. The project includes scripts for data preprocessing, model training, and evaluation, making it easy to adapt for custom datasets. Ideal for applications like social media analysis, product reviews, or customer feedback.

Multi-label sentiment analysis on product reviews using LLAMA, GPT, BERT, and transformer-based NLP techniques in Python.

FinBERT × SARIMAX framework for renewable energy stock forecasting. Master thesis, Columbia QMSS 2024.

Large-scale bibliometric analysis of US-affiliated PubMed abstracts (1994–2025): disease trends, topic modelling, co-authorship networks.