Sentiment Analysis Project using Natural Language Processing (NLP) Techniques

A sentiment analysis project performed on data collected from Twitter mentioning the two primary contestants in the 2020 US Elections.

A bert-fusing architecture for twitter sentiment analysis. accepted in AACL-IJCNLP 2020 Student Research Workshop.

Predicting US Presidential Election 2020 Result Using Twitter Sentiment Analysis with Python.

We use the BERT language model for Twitter sentiment analysis leading to the US 2020 presidential elections. We investigate if sentiment analysis can provide an indication of the outcome of the results using canonical LSTM and BERT language model.

Extraction of tweets and Perform Sentiment analysis on the presidential candidature of Donald Trump, Joe Biden in US Presidential Election in November, 2020.

Predicting the 2020 U.S. presidential election using Twitter sentiment analysis.

Sentiment Analysis on a Twitter dataset for the course Computational Intelligence Lab 2020 at ETH

Indonesia has 19.5 million Twitter users from a total of 500 million global users and continues to grow over time. Twitter users utilize it as a forum for open campaigning by Medan mayoral candidates and their volunteers prompted Netizens to respond. Netizen's response to any tweet is Positive and Negative. Therefore, this research tries to analyze tweets about netizen sentiment for the Medan City Elections in 2020. Opinions or sentiments from Twitter users can certainly be used as criticism and suggestions that can be accommodated by candidates for mayor and deputy mayor of Medan. Netizens of Twitter often opinion about the Regional Head Candidate through his Uploads. The opinions of the Twitter Netizens are still random or unclassified. To facilitate the process of classifying opinion data netizens needed a Sentiment Analysis. Sentiment Analysis is carried out by classification of tweets containing Netizen sentiments towards the Implementation of Medan City Elections 2020. The classification method used in this research is the Multilayer Perceptron method with the relu activation function and adam optimization function combined with TF-IDF feature extraction. The validity test applied to this research used a confusion matrix. With tf-idf feature extraction and the multilayer, perceptron method will be able to automatically classify sentiment analysis with an accuracy of 92,733%

Performing comparative sentiment analysis to determine public reaction on newly introduced Farm Laws of 2020, India by collecting data using Twitter Tweepy API

Twitter Sentiment Analysis

Twitter brand sentiment analysis on 2020 presidential election

This is a sentiment analysis on the Twitter US 2020 election environment using Python. It takes tweets as inputs and evaluates who the tweet relates to and whether the sentiment is positive or negative.

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2020 - Twitter Scrapper + Sentiment analysis of Covid related Tweets in Quebec in French and English

An sentiment analysis exploration of twitter data regarding candidates in the US 2020 democratic presidential primary

Lexicon-based sentiment analysis on Malay tweets that pulled from Twitter. My final year project in 2020.

Twitter Sentiment Analysis Project This project uses sentiment analysis techniques to analyze public sentiment during the 2020 election by analyzing Twitter data. The goal is to uncover trends, patterns, and key insights into how people felt about political events, candidates, and issues based on Twitter posts.

Analysis of the social interactions between Twitter users interested in the Tokyo 2020 Olympics and analysis of the sentiment and the content of their tweets.

This project is regarding the analysis of Tweets from Donald Trump, U.S president (2016-2020) from Jan 2015 to 17/09/2020. The analysis includes how tweet counts, frequently tweeted words, Twitter handles, and tweet sentiment changed from year to year.

The analysis of Twitter data during the fall 2020 U.S. election focuses on understanding public sentiment, key topics of discussion, and engagement trends. This type of analysis can provide insights into how different events (debates, news) impacted the online conversation

In the era of technology and internet, people use online social media services like Twitter, Instagram, Facebook, Reddit, etc. to express their emotions. The idea behind this paper is to understand people’s emotion on Twitter and their opinion towards Presidential Election 2020. We collected 1.2 million tweets in total with keyword like “RealDonaldTrump”, “JoeBiden”, “Election2020” and other election related keywords using Twitter API and then processed them with natural language processing toolkit. A Bidirectional Long Short-Term Memory (BiLSTM) model has been trained and we have achieved 93.45% accuracy on our test dataset. We then used our trained model to perform sentiment analysis on the rest of our dataset. With the sentiment analysis results and comparison with 2016 Presidential Election, we have made predictions on who could win the US Presidential Election in 2020 with pre-election twitter data. We have also analyzed the impact of COVID-19 on people’s sentiment about the election.

This project aims to do sentiment analysis of political views of Twitter users during the 2020 American Presidential Election campaign. This is an important study as public opinion for a particular candidate will impact the potential leader of the country. We are relying on Twitter as it acquires a large diverse data set representing the current public opinion about President Trump. The business objective of this project is to analyze the following questions: • What is the public sentiment distribution across the various locations/ states in the U.S.? • Which states require more strategic campaigning/ awareness by the Trump Government? • Does the positivity of a tweet influence the quantity of retweets/favorites? We performed the analysis to study the current state of public opinions.

After a contentious election season, we wanted to analyze emotions and ideas in the news and social media from the past few years. We used sentiment analysis to map the subjectivity and polarity of over 140,000 news headlines and thousands of Congressional tweets. Then, we used Latent Dirichlet Allocation to perform topic modelling on the top tweets of 2019 and 2020 to find correlations between influential ideas and people on Twitter.

The findings of the author on Sentiment Analysis of 12447 Twitter feeds in India from 10th to 21st March carrying hashtags related to Corona (#coronavirus, #COVID19, #coronaviruspandemic, #coronavirusoutbreak etc.) shows that the sentiment is polarized on the issue with no middle ground. Fortunately, majority show positive sentiments on the issue and are not panicked. We may guess that the citizens are amenable to suggestions of restrains and shall refrain from hoarding of essential food supplies and medical equipment. Such optimism may also allow us to face the economic crisis with fortitude, provided the government is able to sustain the consumer sentiment through announcements on financial aid, wage security, tax and mortgage relaxations and cut in interest rates. The scale is from negative to positive starting from 1 from 10 to 20th March 2020. It’s the time around Prime Minister’s address to the nation on Corona Virus. The mood of the people seems positive and upbeat giving hope that India as a community shall put up a disciplined and sustained fight to counter the pandemic as well as economic challenges.

Twitter sentiment anaylsis - LSTM recurrent neural network that assigns a sentiment value to tweets

A Twitter sentiment analysis for the two most powerful US presidential election candidates Donald Trump and Joe Biden just a few months before the election. Data was taken from replies of general public on a tweet from each of the candidates.

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Analysis of Tweets about the 2020 presidential debate from October 15th until election day.