Know If A Phrase Is Positive Or Negative Using This Sentiment Analysis API
If you wish to start using this sentiment analisis API, you can do so by reading the quickstart guide.
Positive and negative sentiment can be detected via sentiment analysis and opinion mining. Sentiment labels (such as “negative,” “neutral,” and “positive”) and confidence scores at the sentence and document levels can be obtained using sentiment analysis. Opinion Mining gives granular information about the opinions associated with terms (such as product or service qualities) in the text.
Sentiment Analysis
Sentiment Analysis tags text with sentiment labels and produces confidence scores at the sentence and document levels. Labels such as positive, negative, and neutral are utilized. At the document level, the mixed sentiment label can also be returned.
Determine The Sentiment Analysis Model.
By default, sentiment analysis will apply the most recent AI model to your text. You can also specify which model version to use in your API calls.
Languages Used For Input
When you submit papers for sentiment analysis, you have the option of specifying which of the available languages they are written in. If you do not indicate a language, sentiment analysis will use English as the default. To allow different language and emoji encodings, the API may return offsets in the response.
Data Submission
When given smaller amounts of text to deal with, sentiment analysis and opinion mining provide higher-quality results. This is in contrast to some features, such as key word extraction, which works better on longer blocks of text.
The analysis begins as soon as the request is received. It is stateless to use the sentiment analysis and opinion mining features concurrently. There is no data stored in your account, and results are returned in the answer immediately.
When this functionality is used asynchronously, the API results are available for 24 hours from the time the request was consumed, and this is stated in the response. After this time period, the results are deleted and no longer retrievable.
For example, you may perform sentiment analysis on a sales call when the customer utilizes sarcasm (such as “Well thanks for being so helpful”) or a tone of voice that indicates positive feelings but is actually quite negative. An API that lacks contextual awareness would be unable to differentiate between the two scenarios.
Instead, using an API is your best option for language compatibility. Despite the fact that Javascript, Go, or Ruby are likely the best languages to use to access them, the vast majority of machine learning frameworks are developed in Python. In this case, an API is an excellent solution.
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The first thing you should do is create an account on this website. After that, you will be given a personal API access key, which is a string of letters and digits that allows you to use this API. The final step is to use Plaraphy’s API for authentication; to finish, simply add your user token to the authorization header.
If you have any questions, you can also check the Plaraphy website’s frequently asked questions section by clicking here. You can get all of the information you need right here, so don’t put off getting this API and improving the functionality of your website.