Analyzing subjectivity in social networks
-
Updated
Jan 7, 2019 - Jupyter Notebook
Analyzing subjectivity in social networks
An Empirical Evaluation of Word Embedding Models for Subjectivity Analysis Tasks
This Twitter Sentiment Analyzer helps detect the Positive and the Negative Tweets by classifying the data, analysing the sentiments of the words that are commonly used and labelling them as positive and negative words. The Bag of Words (BoW) was used to detect Racist/Hate Speech from a training dataset extracted from Twitter API
Code and data for EMNLP2021 paper: WIKIBIAS: Detecting Multi-Span Subjective Biases in Language
Multi-View Sentiment Corpus (EACL 2017): tweets labelled by three annotators with sentiment, emotion, irony, subjectivity and implicitness
Subjectivity removal and Polarity classification of movie reviews employing a shallow model (Multinomial Naive Bayes) and a deep model (Bidirectional LSTM with self-attention)
Reading list for Awesome Sentiment Analysis papers
Topic modelling and analysis of different UK newspapers, primarily using BERTopic
This is the repository for the newly created Czech Subjectivity Dataset (Subj-CS) and our paper:
Exploring the 2021 AAPI movement using Twitter and news data.
Testbench for sentiment and factuality in texts.
Natural Language Toolkit for Malaysian language, https://malaya.readthedocs.io/
Add a description, image, and links to the subjectivity-analysis topic page so that developers can more easily learn about it.
To associate your repository with the subjectivity-analysis topic, visit your repo's landing page and select "manage topics."