Constructing Colloquial Dataset for Persian Sentiment Analysis of Social Microblogs

Core Concepts

The author constructs a colloquial dataset for sentiment analysis in Persian microblogs, utilizing a CNN model to enhance sentiment classification performance.

Abstract

The study focuses on sentiment analysis in Persian microblogs, addressing the lack of suitable datasets and the challenges posed by informal language. The authors propose a new architecture based on CNN for effective sentiment analysis, achieving a 72% accuracy rate. Various deep learning models are evaluated using different word embeddings to enhance performance.
The study emphasizes the importance of annotated datasets for supervised learning methods and highlights the significance of hybrid approaches in improving accuracy. The construction process of the Persian opinion dataset is detailed, including data crawling from Instagram and Twitter. Annotators manually label opinions, ensuring diversity and informality in the dataset.
The proposed model's hyperparameters are optimized through experiments, with the CNN-based architecture outperforming other models in F1 score and accuracy metrics. The model successfully handles colloquial language, abbreviations, emojis, and emerging vocabulary in sentiment analysis tasks. Validation results demonstrate substantial inter-annotator agreement and high self-agreement among annotators.

Stats

The results demonstrate the benefit of our dataset and the proposed model (72% accuracy).
Various experiments were performed on deep neural network models such as GRU, CNN-RNN, CNN, LSTM, and Bidirectional GRU.
The best accuracy is 72% in the CNN-based model.
Hyperparameter settings used in different models are provided.

Quotes

"The presented architecture applies a CNN network model to predict text polarity."
"Our model predicts correct sentiment values even with incomplete colloquial sentences."
"The proposed model achieves significant improvement compared to other models."

Key Insights Distilled From

Constructing Colloquial Dataset for Persian Sentiment Analysis of Social Microblogs

by Mojtaba Mazo... at arxiv.org 03-08-2024

https://arxiv.org/pdf/2306.12679.pdf

Deeper Inquiries

How does the emergence of COVID-19 impact language trends reflected in social media sentiments?

The emergence of COVID-19 has had a significant impact on language trends reflected in social media sentiments. During the pandemic, new words, phrases, and colloquial literature became popular in people's conversations. This shift in language usage is evident in social media platforms like Instagram and Twitter, where users express their thoughts and emotions regarding the pandemic. The use of emojis, abbreviations, interrupted words, colloquial phrases, and new vocabulary related to COVID-19 has become prevalent in online discussions. These changes reflect how current events influence language trends and sentiment expressions on social media.

What are potential limitations or biases introduced by relying on pretrained word embeddings for sentiment analysis?

While pretrained word embeddings offer a convenient way to represent words as numerical vectors for sentiment analysis tasks, they come with certain limitations and biases. One potential limitation is that pretrained embeddings may not capture domain-specific nuances or context-specific meanings present in the dataset being analyzed. This can lead to inaccuracies in sentiment classification when dealing with specialized topics or industry jargon.
Additionally, biases inherent in the training data used to create pretrained embeddings can carry over into sentiment analysis models. If the training data contains biased language patterns or stereotypes, these biases can be perpetuated when using pretrained word embeddings for sentiment analysis tasks. It is essential to be aware of these limitations and biases when utilizing pretrained embeddings and consider fine-tuning them on specific datasets to improve performance.

How can this research be extended to incorporate real-time social media data for dynamic sentiment analysis?

To incorporate real-time social media data for dynamic sentiment analysis based on this research framework:

Implement a streaming data pipeline: Develop a system that continuously collects live social media posts from platforms like Twitter and Instagram.
Real-time preprocessing: Apply efficient text preprocessing techniques to handle incoming data streams quickly.
Update model inference: Modify the existing deep learning model architecture to support real-time predictions based on newly arriving data.
Incorporate feedback loops: Integrate mechanisms for updating model parameters based on user feedback or changing sentiments observed over time.
5.Implement scalable infrastructure: Deploy the system on cloud-based services capable of handling high volumes of real-time data processing efficiently.

By following these steps, researchers can extend this study's findings into a practical application that performs dynamic sentiment analysis using real-time social media inputs effectively.