Glossary keyword - Sentiment Analysis

Sentiment Analysis

Sentiment analysis is an automated process that analyzes and determines the emotional tone of opinions. This tool contributes to data analysts to classify texts as negative, positive, or neutral. Sentiment analysis depends on using machine learning and natural language processing techniques to identify the social sentiment of documents.

Reasons for using Sentiment analysis

Sentiment analysis is a combination of several various scientific techniques such as biometrics, natural language processing, computational linguistics, and text analysis. Additionally, it is an automated system based on artificial intelligence, meaning that there is no need to spend a lot of effort and time to investigate social sentiments. Another benefit is to express the enormous size of attitudes and emotions as valuable information for research purposes. Finally, regardless of the type and size of companies or organizations, sentiment analysis is becoming smarter, more affordable, and accessible.

Advantages of Sentiment analysis

Sentiment analysis is a magical tool that shows the present and future behavior of people according to held opinions. Companies get to easily manage their brand reputation by analyzing discussions, posts, and responses of consumers about products or services on social media platforms. Secondly, this tool boosts understanding customers’ feedback better without asking them directly. The most exciting thing is that companies are capable of knowing and preventing possible crisis beforehand. Not only commercial organizations but also governments use sentiment analysis to identify people’s reactions toward the country policy.

How does it work?

Aside from determining opinions, this system extracts emotions of words or phrases as polarity, subject, and opinion holder. Sentiment analysis uses hybrid, statistical, and knowledge-based approaches. This tool is capable of classifying texts by influencing groups based on direct effect phrases like bored, or happy. Besides written documents, like Google Voice Search, it is possible to analyze the voices of customers by detecting tones, syllables, and emotions in their speeches. 

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