# Types of Approaches, Applications and Challenges in the Development of Sentiment Analysis Systems

arXiv: https://arxiv.org/abs/2303.11176 · HTML: https://mlsanity.com/paper/2303.11176/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 40 references that are in the map.
- First posted on arXiv 9 Mar 2023.

## Builds on (most cited first, 5 of 5)

- [Block Hunter: Federated Learning for Cyber Threat Hunting in Blockchain-based IIoT Networks](https://mlsanity.com/paper/2204.09829/) (2022)
- [Security Considerations for Virtual Reality Systems](https://mlsanity.com/paper/2201.02563/) (2022)
- [Detecting Spam Reviews on Vietnamese E-commerce Websites](https://mlsanity.com/paper/2207.14636/) (2022)
- [Web3 Challenges and Opportunities for the Market](https://mlsanity.com/paper/2209.02446/) (2022)
- [Blockchain-enabled Authentication Handover with Efficient Privacy Protection in SDN-based 5G Networks](https://mlsanity.com/paper/1905.03193/) (2019)

## Abstract (arXiv)

Today, the web has become a mandatory platform to express users' opinions, emotions and feelings about various events. Every person using his smartphone can give his opinion about the purchase of a product, the occurrence of an accident, the occurrence of a new disease, etc. in blogs and social networks such as (Twitter, WhatsApp, Telegram and Instagram) register. Therefore, millions of comments are recorded daily and it creates a huge volume of unstructured text data that can extract useful knowledge from this type of data by using natural language processing methods. Sentiment analysis is one of the important applications of natural language processing and machine learning, which allows us to analyze the sentiments of comments and other textual information recorded by web users. Therefore, the analysis of sentiments, approaches and challenges in this field will be explained in the following.