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This repo belongs to Twitter Sentiment Analysis

:chart: A web app to search twitter based on #Hashtags and calculate the sentiment of tweets.

Twitter Sentiment Analysis

Sentiment analysis for Twitter's tweet (in Indonesia language) was built with 3 models to get a comparison in determining which model gives the best results for predicting a tweet to have a positive or negative meaning.

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

A Spark Streaming implementation for Online Twitter Sentiment Analysis.

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To Scrape IMDB for Celebrity Data and Analyze Sentiment on Twitter | Edureka Course Work

Sentiment Analysis of tweets using python

Twitter Sentiment Analysis using Spark, MongoDB, and Google Cloud

Sentiment Analysis applied to the latest Tweets

This is the repository for my bachelor project, an ASP.NET app allowing user to understand sentiment and key phrases of interesting tweets.

Sentiment Analysis

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Using the twitter api to pull historical tweets from a user and then runing a sentiment analysis over them, with a dashboard powered by streamlit

Scrapped tweets using twitter API (for keyword ‘Netflix’) on an AWS EC2 instance, ingested data into S3 via kinesis firehose. Used Spark ML on databricks to build a pipeline for sentiment classification model and Athena & QuickSight to build a dashboard

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Tool to describe sentiment present on tweets by analysing polarity of each word.

The purpose of this project is to classify the given twitter tweets in to positive, negative and neutral classes i.e. the project basically analyses the emotion of a particular tweet. To train and classify the given data, We have used different classification techniques such as Multinomial Naive Bayes, Linear SVM(Support Vector Machine), LSTM(Long Short Term Memory), CNN(Convolutional Neural Network) and ensembles of some of the models mentioned above. Before feeding the data into the model, pre-processing of data was done, features like unigram, bigram, trigram, padding, Word2vec etc. were used and finally the data was converted into a format which a machine learning model can understand. Out of all the classification techniques used, prediction taken by majority voting between CNN, LSTM, SVM performed better.

Predicting sentiment of a tweet using hadoop-mapreduce

Simple sentiment analysis with Vader and Tweepy

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An iOS app that uses CoreML and Natural Language Processing Framework to understand the sentiments and emotions being expressed in the tweets

A small app to get people's opinion about some trends with location tags!

Sentiment Analysis of tweets using the sentiment 140 dataset. Compares & contrasts the efficacy of various modeling techniques. Detects depression in tweets.

Sentiment Analysis applied to the latest Tweets

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