Persian Sentiment Analysis
6 papers with code • 0 benchmarks • 0 datasets
Persian Sentiment analysis is the task of classifying the polarity of a given text.
Benchmarks
These leaderboards are used to track progress in Persian Sentiment Analysis
Most implemented papers
A Deep Convolutional Neural Networks Based Multi-Task Ensemble Model for Aspect and Polarity Classification in Persian Reviews
The results indicate that this new approach increases the efficiency of the sentiment analysis model in the Persian language.
DeepSentiPers: Novel Deep Learning Models Trained Over Proposed Augmented Persian Sentiment Corpus
To best of our knowledge, we do not merely suffer from lack of well-annotated Persian sentiment corpus, but also a novel model to classify the Persian opinions in terms of both multiple and binary classification.
ParsiNLU: A Suite of Language Understanding Challenges for Persian
Despite the progress made in recent years in addressing natural language understanding (NLU) challenges, the majority of this progress remains to be concentrated on resource-rich languages like English.
Jointly Modeling Aspect and Polarity for Aspect-based Sentiment Analysis in Persian Reviews
The developed models were evaluated using the collected dataset in terms of example-based and label-based metrics.
A Novel Approach for Enhancing Sentiment Classification of Persian Reviews Using Convolutional Neural Network and Majority Voting Classifier
To reduce these errors and improve the efficiency of model predictions, combining several models known as ensemble learning may provide better results.
Constructing Colloquial Dataset for Persian Sentiment Analysis of Social Microblogs
Second, this study proposes a new architecture based on the convolutional neural network (CNN) model for more effective sentiment analysis of colloquial text in social microblog posts.