Sentiment Analysis
Sentiment Analysis, also known as opinion mining, refers to the use of natural language processing, text mining, and computational linguistics to identify and extract subjective information from original materials.
Sentiment AnalysisImportance
- Providing objective insights:By using sentiment analysis tools powered by artificial intelligence (AI), businesses can avoid the personal biases of human reviewers. As a result, companies can achieve consistent and objective results when analyzing customer opinions.
- Create better products and services:Sentiment analysis systems help companies improve their products and services based on real and specific customer feedback. AI technology identifies real-world objects or situations (called entities) that are associated with negative customer sentiment.
- Large-scale analysis:Businesses continue to mine information from massive amounts of unstructured data, such as emails, chatbot transcriptions, surveys, CRM records, and product feedback. Cloud-based sentiment analysis tools enable businesses to scale the process of discovering customer sentiment in text data affordably.
- Real-time results:In today's fast-changing environment, businesses must respond quickly to potential crises or market trends. Marketers rely on sentiment analysis software to understand how customers feel about a company's brand, products, and services in real time and take immediate action based on their findings. They can configure the software to send alerts when negative sentiment is detected for specific keywords.
How Sentiment Analysis Works
Sentiment analysis is an application of natural language processing (NLP) techniques that train computer software to understand text in a similar way to humans. This analysis typically goes through several stages before providing a final result.
- Preprocessing
In the preprocessing stage, sentiment analysis identifies keywords to highlight the core information of the text.
- Tokenization can break a sentence into several elements or words.
- Stemming converts a word into its root form. For example, the root form of am is be.
- Removing function words can filter out words that do not add meaning to the sentence. For example, with, for, at, and of are all function words.
2. Keyword analysis
NLP techniques can further analyze the extracted keywords and assign them a sentiment score. A sentiment score is a measurement that indicates the emotional element in a sentiment analysis system. The sentiment score provides a relative sense of the sentiment expressed in the text for analysis purposes. For example, a researcher might use 10 for satisfaction and 0 for disappointment when analyzing customer reviews.