Sentiment analysis is the systematic identification, extraction, quantification, and study of affective states and subjective data. It makes use of natural language processing, text analysis, computational linguistics, and biometrics. Sentiment analysis is frequently used in marketing, customer service, and clinical medical applications. It is used to voice of the customer materials including reviews and survey replies, internet and social media, and healthcare materials. Section 2 describes related work based on a literature review. Section 3 discusses performance analysis and visualization, and Section 4 concludes the paper on the importance of analysis.
A. Problem Statement
Inspite of the fact that there is software available to extract information about a person's opinions of a certain good or service, companies and other data workers still have problems with the data extraction.
• Web-Based Application Sentiment Analysis Concentrate on One Tweet Only - People are using social media sites like Twitter, which produce large volumes of opinion writings in the form of tweets and are available for sentiment analysis, while the World Wide Web expands quickly . From a human perspective, this equates to a vast volume of information, making it challenging to quickly extract sentences, read them, analyse tweet by tweet, summarise them, and organise them into an understandable style .
• Sentiment Analysis Challenges with Inappropriate English - The term "informal language" describes the usage of slang and colloquialisms in conversation, including expressions like "Could not" and "Couldn't." The analysis and decision-making process may be hampered by the inability of some algorithms to recognise sentiment from the usage of informal language.