Identify The Sentiment Behind Any   Text Review Suggestion Opinion Post Query

Our sentiment analysis tool helps you gather text data from multiple sources and analyze the opinions of users.

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What is Sentiment Analysis?

Sentiment analysis is the process of examining and categorizing pieces of text in positive, negative, or neutral categories.

It can also help you analyze and interpret the mindsets, opinions, emotions, etc and weigh the sentiments expressed in the text.

It can help data analysts analyze public sentiments,conduct market research, gauge brand reputation,and evaluate user experiences.

BytesView sentiment analyzer is an effective tool that can help you evaluate the sentiments of users by analyzing complex structured and unstructured text data.

With our sentiment analysis tool you can gather text data from multiple sources (reviews, suggestions, opinions, social posts, opinions, support queries) and transform it into actionable insights to help make data-driven decisions.

Sentiment Analysis Demo





BytesView Sentiment Analysis Technical Specifications

Technical Specification of sentiment Analysis
Deployment Availability
BytesView Server
BytesView Cloud
Amazon Web Server
Google Spreadsheet
Zendesk(Coming Soon)
Supported Languages
(Add Support for more than 30 language)
Specific Sentiment Analysis Solution Developed For
Customized solution as per your need

How can your business benefit from a sentiment analysis tool?

Manage your brand reputation

Analyze the sentiment around your brand with our analysis tool and understand the motivations behind your customers purchasing choices and the intent behind their queries.

Brand Reputation
Social media conversations

Analyze social media conversations

Nowadays a single tweet can transform how the world views your brand. Use sentiment analysis to analyze conversations related to your brand and resolve any problem before it grows.

Increase employee productivity

Analyze large volumes of employee feedback data to examine employee satisfaction levels. Our sentiment analysis tool leverages the insights to reduce employee churn rate.

Increase employee productivity
Increase customer satisfaction

Increase customer satisfaction

Your brand is being talked about by your customers right now and now with our Sentiment analyzer, you can extract value and insights from customer feedback data and plan effective strategies to enhance customer satisfaction.

How to access BytesView Sentiment Analysis tool ?

Given below are the steps to access the BytesView sentiment analytics API. You can either gather the data by yourself in an excel/CSV format or ask us to do so.

  • Gather text data from multiple sources such as social media, news, online review websites, customer support tools, etc.
  • Compile the gathered text data in an Excel/CSV file or Google spreadsheet.
  • Install the BytesView plugins on Google spreadsheet and open the add-ons tab to locate it.
  • Create a demo account or Purchase a subscription plan that suits your requirements and get an API key.
  • Paste the given API key on the BytesView plugin to activate it
  • Select the cells you want to analyze and the machine learning model you want to use. Click run to start analyzing your data.
  • For more information on sentiment analysis models, go through the video.
access sentiment analysis

Now build and train custom sentiment analysis models as per your need

build custom sentiment analysis

Now train custom sentiment analysis models with data related to your organization to further increase accuracy of the output.

  • Collect the data you want to analyze and export them as a CSV or Excel file. Use a web scraping tool or let us do it for you.
  • Go on the BytesView dashboard and click on “create a model” and chose between a classifier or an extraction model.
  • Click on classifier and then select sentiment analysis model.
  • Import your data and select which column you want to analyze if there is more than one.
  • Tag a few of the data units as Positive, Negative, or Neutral to train your model. The model will begin making its own conclusions after a few tags.
  • Name and Test your model to see how it is working.
  • Once your model is trained, you can upload the whole data set to get results.

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