Project developed in Python 3.5 making use of Bokeh library to display the opinion of users of the debate of June 13, 2016 among the candidates for the presidency of the government of Spain.

résumé automatique de texte oriente vers la prévention, l’éradication et détection des maladies ravageuses déclenchées sur les réseaux sociaux (twitter) cas de l’ebola , meningite et malaria

We'll build a sentiment analysis platform using the IMDB dataset.

Supplementary data for the 2021 JNLE article "Automatic Generation of Lexica for Sentiment Polarity Shifters" by Schulder, Wiegand and Ruppenhofer.

Analyze Tweets using NLTK and Tweepy

Given a users review, predict the stars given by the reviewer

This research wants to build a time serie of the polarity of tweets related to a cluster of firms, and compare it to the time serie of the same firms in the stock market.

In this project a streamlit app is created in which user can upload any english language audio file to get its transcripts, sentiment analysis report, list of statements according to its nature like positive, negative or neutral and at last extractive summary of the whole audio.

Custom Sentiment API

Sentiment mining on Yelp reviews

Web Scrapping British Airways review to gain company insights. Build a random forest model to predict customer buying behavior.

sentiment analysis

Supplementary data for the doctoral thesis "Sentiment Polarity Shifters: Creating Lexical Resources through Manual Annotation and Bootstrapped Machine Learning" by Marc Schulder.

Logistic Regression and Feature Engineering.

What is the relationship between airline sentiments and airlines? What is the reason for the negativity mentioned in the dataset? What is the relation of time with sentiments? Which model is best for sentiment analysis when we do ensemble learning?

Opinion Mining

US 16 Elections, text and sentiment analysis from tweets on May 25th until May 27th 2016.

Sentiment classification of live tweets into positive, negative and neutral polarity.

Supplementary data for the LREC 2020 paper "Enhancing a Lexicon of Polarity Shifters through the Automatic Addition of Shifting Directions" by Schulder, Wiegand and Ruppenhofer.