Sentiment Analysis in SmartReader
Take a look at how to make use of this simple and Excel-based AI sentiment analysis tool.
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sentiment analysis, in the simplest words, is determining whether a piece of text is positive, negative, or neutral. the system combines natural language processing and machine learning techniques to identify and extract opinions within the text. presently, sentiment analysis has many practical applications.
companies are constantly trying to analyze their brand presence in the market and how consumers react to their products or service through the textual feedback available online. but this information is unstructured and highly diverse. with the help of sentiment analysis apis, data analysts can easily transform this unstructured information into structured data containing useful insights. these insights can be extremely helpful for marketing analysis, product reviews, net promoter score, and implementing important business strategies.
paralleldots sentiment analysis api uses state-of-the-art technology to provide an accurate analysis of the overall sentiment of the textual data incorporated from various sources like feedback, surveys, reviews, etc. it uses long short-term memory (lstm) algorithms to classify a text's sentiment into positive, neutral, or negative.
read this blog to gain more knowledge about the concept, analysis, and applications of sentiment analysis.
salient features of our sentiment analysis api
- the sentiment analysis api works on a excel
- the api is well capable of analyzing open-ended texts and has an accuracy rate like no other.
- it is robust against tricky sentences and is fully capable of recognizing phrases containing double negatives and word order ("crushed my hopes" vs, "crush on him")
- it detects the three primary sentiments, positive, neutral, and negative, in the open-ended texts
- the api ensures data privacy as it supports private cloud deployments via docker containers or on-premise deployment.
feel free to use our sentiment analysis api demo here.
a step-by-step guide to using the api in smartreader
smartreader is a simplified excel-based saas solution at paralleldots that automatically analyzes the open-ended customer responses. using artificial intelligence, it understands the data and generates a list of the most predominant themes in the data to bring out useful insights accordingly.
to use the sentiment analysis api in smartreader, you need to follow the following steps:
step 2 : after login, you'll see your custom dashboard. to start a new project, click on the "create new project" button on the top left corner of the dashboard.
step 3 : write your project name and choose "sentiment" from the insights section.
select the language of your data.
then, select the file format of your data. (.xlx/ .xlsx/ .csv)
finally, from the drop-down menu, select the data that you want to analyze, upload your file. and start the analysis.
step 4 : once you finish uploading your spreadsheet, the ai behind smartreader starts learning from your data. this process takes several minutes because smartreader is going through all your data word by word and learning different concepts from it. feel free to minimize or close the tab and continue your work.
step 5 : once this is done, you'll be redirected to your dashboard where you will see the results of the analysis.
once this is done, you will be redirected to your dashboard where the results of the analysis will be displayed, as shown below.
smartreader is a flexible, diy tool that businesses can use for analyzing open-ended textual data. the tool learns from your data and suggests relevant themes emerging from it, and is fully customizable. it allows you to download and analyze the data from the comfort of your spreadsheet.
click here to read more about smartreader.
we hope you liked the article. please sign up for a free paralleldots account to start your ai journey now. create your own classification model with smartreader and discover interesting insights from your customers' voice. you can also check out free demos of paralleldots ai apis here.
Published at DZone with permission of Shashank Gupta, DZone MVB. See the original article here.
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