Deep Learning-based Sentiment Analysis in Persian Language: A Hybrid Approach

Konsep Inti

Hybrid deep learning model for sentiment analysis in Persian language achieves impressive performance.

Abstrak

In the realm of natural language processing, sentiment analysis has gained significant traction, especially in the Persian language. The study introduces a hybrid deep learning model tailored for sentiment analysis using customer review data from Digikala Online Retailer. Various challenges are highlighted, including the scarcity of extensive Persian training datasets and the necessity for high-performing GPUs. The research delves into different network architectures and techniques to enhance accuracy, showcasing models with varying hidden layers and activation functions. The dataset utilized comprises 100,000 customer reviews across different product categories, segmented into positive, negative, and neutral classes. Techniques such as normalization, tokenization, sentence length unification, vectorization, and splitting of train and test data are employed to process the dataset efficiently. Results from different models are presented along with their respective accuracies and performance metrics. The study concludes by emphasizing the importance of leveraging deep learning methods specifically designed for the nuances of the Persian language in sentiment analysis.

Statistik

Achieving an F1 score of 78.3 across three sentiment categories: positive, negative, and neutral.
Dataset comprises 100,000 customer reviews across different product categories.
Word2Vec+5Layers+ReLU+lrDecay model achieved an accuracy rate of 72.1%.

Kutipan

"The study introduces a novel hybrid deep learning model for sentiment analysis."
"Our investigation faced two primary obstacles that challenge accuracy enhancement."
"Results from different models are presented along with their respective accuracies."

Wawasan Utama Disaring Dari

Deep Learning-based Sentiment Analysis in Persian Language

by Mohammad Hey... pada arxiv.org 03-19-2024

https://arxiv.org/pdf/2403.11069.pdf

Pertanyaan yang Lebih Dalam

How can the challenges related to scarce training datasets in Persian language NLP be effectively addressed?

Addressing the challenges related to scarce training datasets in Persian language NLP requires innovative approaches. One effective strategy is data augmentation, where existing datasets are manipulated to create new samples for training. Techniques like back translation, synonym replacement, and word embedding transformations can help generate additional data points. Another approach is transfer learning, leveraging pre-trained models on similar languages or domains and fine-tuning them on the limited Persian dataset. Collaborative efforts within the research community to share annotated datasets can also alleviate scarcity issues by pooling resources and expertise.

What implications does the requirement for high-performing GPUs have on the scalability of deep learning models?

The requirement for high-performing GPUs has significant implications on the scalability of deep learning models. Firstly, it impacts model complexity and size since larger networks with more parameters demand increased computational power for training and inference. This leads to longer processing times and higher hardware costs as sophisticated architectures are developed. Additionally, GPU limitations may restrict batch sizes during training, affecting convergence speed and overall performance optimization. Scalability becomes a concern when deploying these resource-intensive models in production environments or scaling up operations due to infrastructure constraints.

How might integrating character embeddings with word embeddings impact the accuracy of sentiment analysis beyond this study?

Integrating character embeddings with word embeddings can enhance sentiment analysis accuracy by capturing finer linguistic nuances present in text data beyond traditional word-level representations alone. Character-level information helps address out-of-vocabulary words by encoding subword structures that contribute meaningfully to sentiment classification tasks. By combining both types of embeddings, models gain robustness against misspellings, slang terms, or rare words common in informal online content like social media posts or reviews. This fusion enables deeper semantic understanding at a granular level while improving generalization capabilities across diverse textual inputs beyond what individual embedding types could achieve independently.