An affect generator based on TextBlob and the NRC affect lexicon. Note that lexicon license is for research purposes only.

A TextBlob sentiment analysis pipeline component for spaCy.

Emotion AI (Sentiment Analysis) of Tweets using TextBlob and Django (Python)

This project performed sentimental analysis based on opinion words (like good, bad, beautiful, wrong, best, awesome, etc) of selected opinion target ( like product name for amazon product reviews).

Twitter Sentiment Analysis using Textblob and Tweepy, wrapped with Flask as a web app.

API server for TextBlob: Sentiment analysis, POS tagging, noun phrase extraction.

Simple example of using TextBlob to perform sentiment analysis

Sentiment Analysis using tweepy,NLTK and Textblob

Sentiment Analysis Twitter Bahasa Indonesia dengan TextBlob. Diaplikasikan untuk menganalisis tentang topik debat capres-cawapres 2019

A Hack of the Hour presents to you a product that is meant to strengthen the relationship between a Therapist & their Client. A product that is meant to channel the Client's emotions and feelings anywhere, anytime. Providing a detailed analysis to the therapist, tailored to the Client itself.

Small side project building a basic sentiment analysis for Twitter timelines with Python's TextBlob and SQLite

The code uses the tweepy library to access the Twitter API and the TextBlob library to perform Sentiment Analysis on each Tweet.

Perform sentiment analysis on tweets using NLTK and TextBlob!

Ingesting real-time Twitter API using tweepy into Kafka and process using Apache Spark Structured Streaming with Sentiment Analysis TextBlob before loading into time-series database, InfluxDB and monitoring dashboard, Grafana

Vecna is a Python chatbot which recommends songs and movies depending upon your feelings

A streamlit python web app to analyze sentiment in a CSV file and add the sentiment values to the file.

This Python script creates a user-friendly GUI using tkinter for summarizing news articles. It employs natural language processing tools like nltk and TextBlob, allowing users to input a news article URL, click the "Summarize" button, and receive a quick overview of the article's title, author, publication date, summary, and sentiment analysis.

In this project, I explore various machine learning techniques including Principal Component Analysis (PCA), Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Sentiment Analysis in an effort to predict the directional changes in exchange rates for a list of developed and developing countries.

Developed a machine learning model to detect media bias in news articles. Employed natural language processing techniques to analyze text content and classify sources into unbiased, left-leaning, or right-leaning categories. This project enhanced my expertise in text analysis and understanding of media landscape.

In this repo i created a twitter sentiment analysis on flask app (web base).

Student Feedback Analyzer is a Python-based desktop application which is developed with a motive to change the existing feedback system used in colleges that uses multiple options in the form of radio button in a form to find out the feedback of the student on a particular subject or a faculty instead use sentiment analysis to find out the feedback score of a subject or faculty from the students feedback. The application uses textblob to find the polarity of a feedback given by the subject and stores it in a database. Later the Administrator can find out the feedback score of each faculty and / or subject using the admin module.

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

Designed an end-to-end analysis pipeline using MySQL, Python, and Power BI. Analyzed purchase patterns and customer sentiment using SQL and Python (TextBlob, pandas). Built an interactive Power BI dashboard for sales, trends, and sentiment insights.

Sentiment Analysis of Youtube Video Comments using Youtube Data Api

Pipescore-api, takes a keyword and searches through, facebook, google news and instagram for posts, news and groups and performs a sentimental analysis on the post (text) using textBlob to give the sentiment(polarity, subjectivity) thus to show how positive and negative the post is

Twitter Sentiment analysis with polarity and subjectivity using Python

I’ll break down how hyperpersonalization is reshaping industries, from finance to banking, and how you can build your very own hyperpersonalization web app using Python, Machine Learning, Generative AI (GenAI), and open-source LLMs from Hugging Face.

simple script for downloading Youtube comments without using the Youtube API

Natural language processing (NLP) is an exciting branch of artificial intelligence (AI) that allows machines to break down and understand human language. I plan to walk through text pre-processing techniques, machine learning techniques and Python libraries for NLP. Text pre-processing techniques include tokenization, text normalization and data cleaning. Once in a standard format, various machine learning techniques can be applied to better understand the data. This includes using popular modeling techniques to classify emails as spam or not, or to score the sentiment of a tweet on Twitter. Newer, more complex techniques can also be used such as topic modeling, word embeddings or text generation with deep learning. We will walk through an example in Jupyter Notebook that goes through all of the steps of a text analysis project, using several NLP libraries in Python including NLTK, TextBlob, spaCy and gensim along with the standard machine learning libraries including pandas and scikit-learn.

Sentiment Analysis using TextBlob