Repository with all what is necessary for sentiment analysis and related areas

😃 iOS11 demo application for sentiment polarity analysis.

Dictionary based Sentiment Analysis for Japanese

Codes for our paper "SentiLARE: Sentiment-Aware Language Representation Learning with Linguistic Knowledge" (EMNLP 2020)

Japanese Realistic Textual Entailment Corpus (NLP 2020, LREC 2020)

Hashformers is a framework for hashtag segmentation with Transformers and Large Language Models (LLMs).

An emotion-polarity classifier specifically trained on developers' communication channels

Sentiment Strength Detection in Bahasa Indonesia

Sentiment analysis for Twitter and social media

A Web Scraping project to analyze product reviews in Amazon.com. A sentiment analysis is done and the reviews are sorted and highlighted in green or red based on the sentiment score.

SentiSE is a sentiment analysis tool for Software Engineering interactions

Encyclopedic Hub for Sentiment Dictionaries

This project focuses on sentiment analysis. Social Sentiment analysis is the use of natural language processing (NLP) to analyze social conversations online and determine deeper context as they apply to a topic, brand or theme.

How Will Your Tweet Be Received? Predicting theSentiment Polarity of Tweet Replies

A rating-based sentiment dataset of IMDB movie reviews (WASSA 2014)

Python module to get Sentiment Rankings for Unicode Emojis

Annotated sentences to do sentiment analysis with supervised learning in the danish language. The dataset was created specifically to classify sentences to the root comments of political articles on social media.

Java port of Python NLTK sentiment VADER module. VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment analysis tool that is specifically attuned to sentiments expressed in social media, and works well on texts from other domains.

More than the default behavior of a GA device, the robot, with this project can run custom Choregraphe projects, by saying something like "execute object recognition".

Twitter Feeds were analysed during the Lok Sabha Elections 2019 to guage the overall popularities of each party and predict the winner based solely on the tweets made by the population. This was made as a part of our Data Science course (UE18CS203) at PES University.