sentiment analysis

sentiment analysis

Sentiment analysis, also known as opinion mining, is a natural language processing technique that aims to identify and extract subjective information from text data. It involves analyzing the sentiment of a given text, such as positive, negative, or neutral. Sentiment analysis can be used to analyze social media mentions, feedback from surveys and product reviews, incoming support tickets, and more. It allows companies to analyze data at scale, detect insights, and automate processes 

Sentiment Analysis

identify and extract

Text data is preprocessed to remove any irrelevant information such as stop words and punctuation marks. Next, the text is tokenized into individual words or phrases. After tokenization, the sentiment of each word or phrase is determined using a pre-trained machine learning model. Finally, the overall sentiment of the text is calculated by aggregating the sentiment scores of individual words or phrases

Sentiment analysis has a wide range of applications across various industries. For instance, it can be used to monitor customer feedback on social media platforms, gauge public opinion on political issues, analyze patient feedback in healthcare services, predict stock prices and market trends in finance, and analyze incoming support tickets in customer service