Types of Approaches, Applications and Challenges in the Development of Sentiment Analysis Systems
This is an incremental review paper that surveys existing methods and challenges in sentiment analysis for applications like product reviews and social media monitoring.
The paper reviews sentiment analysis systems, addressing the challenge of extracting useful knowledge from vast unstructured text data generated by user opinions on social media, but does not report specific results or numbers.
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.