Sentiment-analysis Libraries

12 Components & Libraries

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Sentiment-analysis Libraries

Web mining module for Python, with tools for scraping, natural language processing, machine learning, network analysis and visualization.

Pattern is a web mining module for Python. It has tools for: It is well documented, thoroughly tested with 350+ unit tests and comes bundled with 50+ examples. The source code is licensed under BSD. …

Learn how to process, classify, cluster, summarize, understand syntax, semantics and sentiment of text data with the power of Python! This repository contains code and datasets used in my book, "Text Analytics with Python" published by Apress/Springer.

Text analytics can be a bit overwhelming and frustrating at times with the unstructured and noisy nature of textual data and the vast amount of information available. "Text Analytics with Python" is …

My first Python repo with codes in Machine Learning, NLP and Deep Learning with Keras and Theano

Codes in NLP, Deep Learning, Reinforcement Learning and Artificial Intelligence I am a Data Scientist and I code in R, Python and Wolfram Mathematica. Here you will find some Machine Learning, Deep L…

Obsei is a low code AI powered automation tool. It can be used in various business flows like social listening, AI based alerting, brand image analysis, comparative study and more .

All the Observers can store their state in databases (Sqlite, Postgres, MySQL, etc.), making Obsei suitable for scheduled jobs or serverless applications. Install the following (if not present alread…

Machine Learning tutorials with TensorFlow 2 and Keras in Python (Jupyter notebooks included) - (LSTMs, Hyperameter tuning, Data preprocessing, Bias-variance tradeoff, Anomaly Detection, Autoencoders, Time Series Forecasting, Object Detection, Sentiment Analysis, Intent Recognition with BERT)

This book brings the fundamentals of Machine Learning to you, using tools and techniques used to solve real-world problems in Computer Vision, Natural Language Processing, and Time Series analysis. T…

AI-related tutorials. Access any of them for free → https://towardsai.net/editorial

Thank you for reading and for being a supporter of Towards AI! Big thank you to C4H3I LLC for sponsoring us in June, 2022!

Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications

This repository is part of the book: "Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications" This accessible and classroom-tested textbook/reference presents an int…

Deploy-BERT-for-Sentiment-Analysis-with-FastAPI

Deploy BERT for Sentiment Analysis as REST API using FastAPI, Transformers by Hugging Face and PyTorch

Deploy a pre-trained BERT model for Sentiment Analysis as a REST API using FastAPI The model is trained to classify sentiment (negative, neutral, and positive) on a custom dataset from app reviews on…

Succinct Machine Learning algorithm implementations from scratch in Python, solving real-world problems (Notebooks and Book). Examples of Logistic Regression, Linear Regression, Decision Trees, K-means clustering, Sentiment Analysis, Recommender Systems, Neural Networks and Reinforcement Learning.

Interested in deeper understanding of Machine Learning algorithms? Implement them in Python from scratch:

This is my github repository where I post trading strategies, tutorials and research on quantitative finance with R, C++ and Python. Some of the topics explored include: machine learning, high frequency trading, NLP, technical analysis and more. Hope you enjoy it!

Hi everyone! This is my respository where I store all my code and research within Quantitative Finance with the programming languages: C++, R and Python. Some of the things you can find here are trad…

Simple Stock Investment Recommendation System based on Machine-Learning algorithms for prediction and Twitter Sentiment Analysis.

Fist I set up the model for the forecast: Finally I apply the LinearRegression algorithm to the data set and then I plot it to see the representation of the forecast made.