Sentiment Analysis Challenges in Persian Language

TL;DR

This paper discusses the unique challenges faced in performing sentiment analysis on Persian language data, emphasizing the need for specialized frameworks and techniques due to linguistic and data-related complexities.

Contribution

It identifies and articulates the main challenges in Persian sentiment analysis, highlighting the necessity for tailored approaches in NLP research.

Findings

Persian language presents specific NLP challenges.

Deep learning techniques face hurdles with Persian sentiment data.

A comprehensive framework is needed for effective sentiment analysis.

Abstract

The rapid growth in data on the internet requires a data mining process to reach a decision to support insight. The Persian language has strong potential for deep research in any aspect of natural language processing, especially sentimental analysis approach. Thousands of websites and blogs updates and modifies by Persian users around the world that contains millions of Persian context. This range of application requires a comprehensive structured framework to extract beneficial information for helping enterprises to enhance their business and initiate a customer-centric management process by producing effective recommender systems. Sentimental analysis is an intelligent approach for extracting useful information from huge amounts of data to help an enterprise for smart management process. In this road, machine learning and deep learning techniques will become very helpful but there is the number of challenges which are face to them. This paper tried to present and assert the most important challenges of sentimental analysis in the Persian language. This language is an Indo-European language which spoken by over 110 million people around the world and is an official language in Iran, Tajikistan, and Afghanistan. Its also widely used in Uzbekistan, Pakistan and Turkish by order.