An Empirical Evaluation of Word Embedding Models for Subjectivity Analysis Tasks
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Updated
Jul 4, 2021 - Python
An Empirical Evaluation of Word Embedding Models for Subjectivity Analysis Tasks
This is the repository for the newly created Czech Subjectivity Dataset (Subj-CS) and our paper:
Subjectivity removal and Polarity classification of movie reviews employing a shallow model (Multinomial Naive Bayes) and a deep model (Bidirectional LSTM with self-attention)
Analyzing subjectivity in social networks
Exploring the 2021 AAPI movement using Twitter and news data.
Testbench for sentiment and factuality in texts.
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
Multi-View Sentiment Corpus (EACL 2017): tweets labelled by three annotators with sentiment, emotion, irony, subjectivity and implicitness
Topic modelling and analysis of different UK newspapers, primarily using BERTopic
Code and data for EMNLP2021 paper: WIKIBIAS: Detecting Multi-Span Subjective Biases in Language
Reading list for Awesome Sentiment Analysis papers
Natural Language Toolkit for Malaysian language, https://malaya.readthedocs.io/
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