The Top 3 APIs For Sentiment Analysis Compared (2023)
Are you interested in creating tools that intelligently track how interviewees feel about specific topics? Or how about systems that track how buyers feel about a new product across all social media mentions? Or that investigates how callers feel about contacts with a specific agent? Sentiment Analysis, backed by powerful AI models, can be beneficial.
In this post, we’ll dig deeper into what Sentiment Analysis is, how it works, current models, use cases, the best APIs to utilize when performing Sentiment Analysis, and some of its current limitations.
What is Sentiment Analysis?
Sentiment Analysis in Natural Language Processing (NLP) refers to the use of Artificial Intelligence (AI) and Machine Learning (ML) techniques to detect and categorize sentiments in a body of text for textual analysis. Sentiment Analysis is sometimes known as Sentiment “Mining” since it involves finding and extracting—or mining—subjective information from source data.
Sentiment Analysis is used to determine a writer’s or speaker’s overall attitude toward an object or subject. Often, this means that product teams create tools that employ Sentiment Analysis to examine comments on a news article or online reviews of a brand, product, or service, or that may be applied to social media posts, phone conversations, interviews, and other forms of communication.
These ascribed sentiments can then be utilized to examine client feelings and feedback, serving as market research to inform marketing, products, training, hiring decisions, and key performance indicators (KPIs).
Intention Analysis and Emotion Detection work in the same way as Sentiment Analysis to round out the fundamental building blocks of NLP text. Intention Study reveals where in a text intents such as opinion, feedback, and complaint, among others, are discovered for analysis. Emotion Detection finds locations in a text where emotions such as pleased, furious, satisfied, and excited are identified for analysis.
Depending on your goals, you can study text in varying depths. You could, for example, calculate the average emotional tone of a group of reviews to see what percentage of purchasers liked your new clothing line. If you want to identify what visitors like or dislike about a given product and why they compare it to comparable things by other companies, you must evaluate each review phrase with a focus on specific components and the use of specific keywords.
There are several ready-to-use sentiment analysis APIs available, each of which use a different method to determine the polarity of a given text. This can range from handwritten rules to machine learning techniques. Any developer can quickly and easily build and use NLP technology with just a few lines of code.
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Plaraphy also offers three possibilities for rewritten texts. This is essential when writing a cover letter because there are occasions when we want to say something but don’t know how. Plaraphy’s Standard Mode, Fluency Mode, and Creative Mode can help you communicate in a variety of situations. This tool can help you figure out the ideal method to say whatever you want to say.
Quillbot is a collaborative writing tool that employs artificial intelligence to help you improve your writing by paraphrasing or rewriting passages. Millions of individuals use the powerful artificial intelligence-powered paraphrasing tool, which comprises a paraphraser and a summarizer, to rewrite and improve sentences, paragraphs, and articles.
This API began as a full-sentence thesaurus, assisting authors of all levels in writing clear, concise language. Over time, the technology has been used to translate articles, PhD theses, and legal emails, among other things.
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For the best match, the article spinner or rewriter analyzes its library of over 500,000 synonyms. After reading your post, the computer will suggest rewrite possibilities based on your preferences. While you may always use your own words to write content quickly, it also provides appropriate synonyms.