A visualization dashboard made for sentiment analysis of tweets of indians public in covid19 pandemic.

This repository contains the relevant data and code to replicate the analysis and results reported in the paper "Using Twitter to Track Immigration Sentiment During Early Stages of the COVID-19 Pandemic"

Challenging times such as the current COVID-19 pandemic can have a major impact on mental health1. One of the possible consequences of this downward trend in mental health is an increase in the prevalence of both mental illnesses such as depression and suicidal ideation. In the Netherlands, 113 suicide prevention runs the national suicide prevention helpline. Help seekers can reach out to the helpline by calling in, but they can also start to chat with one of the 113 volunteers. In the help-line at 113, the volunteers apply motivational interviewing2 during their conversations with the help seekers. At this time, 113 uses previous conversations as an educative tool to improve their expertise in the application of motivational interviewing. However, with this approach many chat sessions are never processed. To address this gap, lexical analysis of all chat sessions could be used analysis as an instrument to detect the cognitive and lexical markers of suicidal ideation and intent, raising the prospect of improving the effectiveness of suicide hotlines with interventions targeted at cognitive distortions associated with suicidal ideation. Examples of such analysis include sentiment analysis, e.g. ANEW3 or LIWC4, the annotation of cognitive distortions5 (as defined in cognitive behavioural therapy6), and the annotation of the effects of motivational interviewing2. The goal of this project is to track emotional and therapeutic/psychological features in the chat sessions that are recorded at the 113 helpline and use this information to provide the operators at the helpline with real-time feedback. With this feedback, you will help the operators assist the help seekers in their efforts to help each individual that reaches out to the suicide prevention hotline.

Sentiment Analysis of Chinese Netizen during the COVID-19 Pandemic

This is the official repository for the project: Joint Sentimental Analysis Based on Tree topology as a commitment to Chen Liao's final dissertation in University of Nottingham Ningbo China (UNNC).

Covid-19 Pandemic Sentiment Analysis of Twitter data

Classification Modeling and Sentiment Analysis of Pandemic Reddit Posts: Natural Language Processing

This repo contains code for a Natural Language Processing project to explore and understand differences in official communications between Governor Cuomo and Mayor de Blasio during the Covid-19 pandemic. Methods include web scraping, parts of speech analysis, topic modeling, and sentiment analysis.

This project is based on Sentiment analysis and it leverages NLP to analyze Textual Data consisting of Covid19 Tweets. The project aims to analyze datasets from a social point of view to help understand people's perceptions during the pandemic. It incorporates various machine learning models to classify the tweets and opinions into a segregated class by assigning polarity scores.

Based on a spring 2020 lesson from the HarvardX Data Science Professional Certificate Program, the R script executes a sentiment analysis of Donald J. Trump's tweets through all of 2020 and early 2021. Included are three select visualizations of log-odds ratios for different sentiments before and after three major historic events -- the WHO's COVID-19 pandemic declaration, the death of George Floyd, and Election Day 2020. Included is the CSV containing the analyzed tweets, which is downloadable through https://www.thetrumparchive.com/

Sentiment Analysis on the tweets by the Indian Twitter users and their views on the Coronavirus pandemic.

No description available

Simple and effective aspect based sentiment analysis on Covid 19 tweets using Python that gives a brief analysis on the pandemic situation over a timeline. This analysis is visualised using a wordcloud.

No description available

Contains code that scrapes tweets from the Twitter hashtag #COVID19. Then tweets were cleaned and used for the analysis. This repository will contain both the code and RMarkdown file that shows my observation.

No description available

Sentiment analysis of financial news published during Covid-19 pandemic

A sentiment analysis on tweets regarding the COVID-19 pandemic.

we do sentiment analysis for tweets to check the fealing about pandemic

Text Mining and Sentiment Analysis on Twitter for Information about students dropping out during the pandemic.

Sentiment Analysis of the COVID-19 Pandemic's Impact on Academics through Naive Bayes- Support Vector Machine

Sentiment Analysis of Tweets About Online Classes During Covid-19 Pandemic in the Philippines Using Naïve Bayes Algorithm

Perform sentiment analysis and topic modeling on collected Reddit user data to identify shifts in food habits during the COVID-19 pandemic

Mental Health Analysis during pandemic using Tweets With AI. One of the projects in Spotle AI-thon (Hackathon) Round-III, which analyzed emotions/sentiments of Tweets during the COVID-19 pandemic using an AI model.

We examined a dataset of tweets based on hashtag phrases, and COVID-19 was among them. Sentiment analysis was used to determine the influence of the pandemic on people's mental health by analyzing Twitter data and, in particular, sentiment analysis.

A comprehensive analysis of public sentiment and emotional intensity towards mask-wearing during the COVID-19 pandemic, using advanced NLP techniques on YouTube comments from Thailand and the USA.

This repo contains the source code and data of the research paper entitled with: Attitudes Evaluation Toward COVID-19 Pandemic: An Application of Twitter Sentiment Analysis and Latent Dirichlet Allocation

This a twitter sentiment analysis that is predict change in human behaviour over the coronavirus i.e when the virus started how people reacted versus after being 6 months into pandemic.

To predict stock close price and return with ARIMA, SVM, LSTM these three models, it is shown that the pandemic negatively influenced the predictions, while involving sentiment analysis improved the predictive outcomes.

The main purpose of this project is to measure the public response to the COVID-19 vaccine with sentiment analysis. Though the vaccine has provided a new hope it has also resulted in several anti-vaccine movements In order to analyze the public opinions and emotions related to the vaccine during the pandemic, I will be utilizing recent Twitter streaming data.