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Twitter Sentiment Analysis using #tag, words and username

Sentimental Analysis using LingPipe, Mogodb and MapReduce

Sentimental Analysis of Twitter Posts and Recommending Psychological Reports Based on them

An AI project where we do web scrapping from Twitter API, find the sentimental analysis using bag-of-word model, & store it in a csv file.

This project is on twitter sentimental analysis by combining lexicon based and machine learning approaches. A supervised lexicon-based approach for extracting sentiments from tweets was implemented. Various supervised machine learning approaches were tested using scikit-learn libraries in python and implemented Decision Trees and Naive Bayes techniques.

Finds the Happiest US and Indian State based on Sentimental Analysis of Twitter Data

I have used Multinomial Naive Bayes, Random Trees Embedding, Random Forest Regressor, Random Forest Classifier, Multinomial Logistic Regression, Linear Support Vector Classifier, Linear Regression, Extra Tree Regressor, Extra Tree Classifier, Decision Tree Classifier, Binary Logistic Regression and calculated accuracy score, confusion matrix and ROC(Receiver Operating Characteristic) and AUC(Area Under Curve) and finally shown how they are classifying the tweet in positive and negative.

Suicide Ideation in Society -A Multivariate Analysis: Twitter sentimental Analysis using python to predict the suicidal thoughts of individuals by analyzing their tweets whether the sentiment of their tweets are positive or negative.

Taking a look at data of 1.6 million twitter users and drawing useful insights while exploring interesting patterns visualized with concise plots. The techniques used include text mining, sentimental analysis, probability, time series analysis and Hierarchical clustering on text/words using R.

In this project we did sentimental analysis on data collected from the social media, Twitter and predicted the current trend. The data can be tweets, quoted tweets and the favorites for a tweet (the number of times a tweet has been liked). Data was collected for a pair of keywords using the Twitter Search API. The collected tweets are then classified as positive, negative, neutral or junk based on the sentimental analysis of the text in the tweet/quoted tweet (favorites are considered as positive). Based on this classification it is possible to predict which among the pair of keywords is more popular. The prediction is under the assumption that more positive and neutral responses are there for a keyword, more trending it is with the public. An Android app was created to display data analysis results for a pair of keywords The accuracy of prediction was examined by predicting the outcome of November 5th Governor Elections in New Jersey using keywords Barbara Buono and Chris Christie.

Twitter Sentimental Analysis Of Assassination Of Imran Khan

A twitter bot that allows you to view tweets, save them and get a sentimental analysis about a particular topic

✔️ Twitter Sentimental Analysis Covid-19 (using Textblob - Naive bayes) + Python Backend Flask + Docker + Docker Compose + MongoDB

Build machine learning models that can classify whether the tweets are positive or negative by analyze the tweets from twitter application api dataset with NLP

In this project we are analysing a direct correlation between “public sentiment” and “market sentiment” using twitter sentiment analysis and Convolutional Neural Networks to predict the movement of Stock Market.

• Designed a web system and an android application which integrated collaborative filtering and clustering technique to recommend products, music and movies • Natural Language Processing(NLP) library of Stanford was used for sentimental analysis of twitter data

Assignment-11-Text-Mining-01-Elon-Musk, Perform sentimental analysis on the Elon-musk tweets (Exlon-musk.csv), Text Preprocessing: remove both the leading and the trailing characters, removes empty strings, because they are considered in Python as False, Joining the list into one string/text, Remove Twitter username handles from a given twitter text. (Removes @usernames), Again Joining the list into one string/text, Remove Punctuation, Remove https or url within text, Converting into Text Tokens, Tokenization, Remove Stopwords, Normalize the data, Stemming (Optional), Lemmatization, Feature Extraction, Using BoW CountVectorizer, CountVectorizer with N-grams (Bigrams & Trigrams), TF-IDF Vectorizer, Generate Word Cloud, Named Entity Recognition (NER), Emotion Mining - Sentiment Analysis.

Predicting the sentiments of the tweets such as Positive, Negative and Neutral. Classified using the SVM model after pre-processing and cleaning the data.

The Natural Language Processing model displays the sentiments of people tweeting about a particular topic in a graphical attractive looking manner

Analysis of sentiments of twitter dataset using NLP and ML

This Project is based on the IEEE Transaction Paper under the Topic Sentiment Analysis of Lockdown in India During COVID-19:A Case Study on Twitter

This gives the present mood of your situation by reading the tweets of your own and predict by my algo in python and give weather positive or negetive

Here i tried my hands on the twitter dataset , cleaned the dataset using ScikitLearn NLP tools like NLTK, proprocessed the data and used Bag of Words feature,build up models using different algorithms like DecisionTreeClassifier,Support Vector Machines,Logistic Regression,compared the f1score of different models.This is my like first Mini-project on Machine Learning

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Sentimental analysis

-- Bill Gates Twitter Sentimental Analysis, -- Scrape And Summarize News Articles, -- Movie Recommendation Engine, -- Classify Images (cifar10) using CNN, -- Classify Clothes using ANN and CNN, -- Spam Text Message Classification, -- Building Artificial Neural Networks

Scrapped out twitter and Youtube comments and have done sentimental analysis to get positive negative and neutral reactionss

A Simple Scraper leveraging tons of API's to scrape over 1.7k stocks & sentimental content from twitter, reddit & All major finance news Websites. This tool can be integrated to provide for an easy way to start AI sentimental analysis for stock trading.

Millions of people are using Twitter and expressing their emotions like happiness, sadness, angry, etc. The Sentiment analysis is also about detecting the emotions, opinion, assessment, attitudes, and took this into consideration as a way humans think. Sentiment analysis classifies the emotions into classes such as positive or negative. Nowadays, industries are interested to use textual data for semantic analysis to extract the view of people about their products and services. Sentiment analysis is very important for them to know the customer satisfaction level and they can improve their services accordingly. To work on the text data, they try to extract the data from social media platforms. There are a lot of social media sites like Google Plus, Facebook, and Twitter that allow expressing opinions, views, and emotions about certain topics and events. Microblogging site Twitter is expanding rapidly among all other online social media networking sites with about 200 million users. Twitter was founded in 2006 and currently, it is the most famous microblogging platform. In 2017 2 million users shared 8.3 million tweets in one hour. Twitter users use to post their thoughts, emotions, and messages on their profiles, called tweets. Words limit of a single tweet has 140 characters. Twitter sentiment analysis based on the NLP (natural language processing) field. For tweets text, we use NLP techniques like tokenizing the words, removing the stop words like I, me, my, our, your, is, was, etc. Natural language processing also plays a part to preprocess the data like cleaning the text and removing the special characters and punctuation marks. Sentimental analysis is very important because we can know the trends of people’s emotions on specific topics with their tweets.