Use Sprinklr’s Sentiment Analysis model and identify the sentiment present in messages accurately, extracting key information that drives critical business decisions.

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Sprinklr Sentiments Analysis model reads messages in context and extracts opinions/sentiments from the text it reads and categorizes them as positive, neutral, or negative. The model is trained using huge volumes of pre-labeled datasets which enables the model to predict accurately almost any kind of text. Our Sentiment Analysis model is built using "state-of-the-art technology" and a robust system of feature extraction which is continually updated by our data experts. Sprinklr makes 10 billion predictions per day with an accuracy level of > 80 %. We also provide customized models for industries to incorporate industry-specific perspectives catering to the specific requirements of every supported industry. The model is designed to read messages in context and extract opinions/sentiments from the text it reads and categorizes them as positive, neutral, or negative.

Use cases of Sentiment Analysis

• Gives you real-time insight into the brand's health by showing current sentiment towards the brand/topic.

• Helps you measure product feature performance.

• Helps you track the sentiments of major influencers of your brand.

• Helps you create and monitor your campaigns by analyzing the sentiments attached to them.

• Helps you create customizable dashboards to show current sentiment toward the brand.

• Helps you keep track of major influencer accounts.

• Helps you manage potential crises conversation before they get viral and consequential. Sprinklr’s Sentiment Analysis enables you to make use of Smart Alerts to send you alerts if a topic is getting a lot of messages with negative sentiments.

• Helps you prioritize messages in order to engage better with your customers.

• Helps you compute satisfaction scores for your customers.

Sprinklr's Sentiments Analysis algorithm

Sprinklr's Sentiment Analysis makes use of Machine Learning (techniques to analyze and categorize opinions expressed by people across blogs, reviews, social media, forums, news, or through any other sort of text as positive, negative, or neutral according to the sentiments expressed by them. As a message can have different implications for different industries, scoring is vetted both on Language and Industry-specific content. Sprinklr uses advanced, deep learning techniques to calculate the sentiment of a statement.

Following is a step-by-step description of how our Sentiment Analysis model works:

1. Data Collection: Messages and unstructured data from more than 25 social networking websites (Facebook, Twitter, Instagram, etc), 350 million web sources, internal data, surveys, and Call Transcripts are collected.

2. Annotation: More than a million messages across 20 industry verticals were manually annotated, analyzed, and categorized according to the sentiments associated with them into positive, negative, and neutral to create a pre-labeled training dataset by data experts to help build an industry-specific model. While annotating the messages, we follow certain guidelines to ensure uniformity. While annotating the data, we use as many examples as possible so that we include more and more instances and contexts in which a particular word may be used. Our datasets also take slang, jargon, and idioms into consideration.

3. Training the Model: The model is fed with this pre-labeled training data (dataset which is already labeled as either positive, negative, or neutral) and is tested on a new dataset to check the performance and accuracy of the model. The model is fed on new inputs and parameters are adjusted again and again until the model reaches the desired accuracy level.

4. Feedback Analysis: You can give feedback on the sentiment of a message on the dashboard and make corrections. This feedback is captured in the platform for further model tuning.

Languages covered

Sprinklr Sentiment Analysis works across 100+ languages. We deploy different algorithms for different languages. Our Sentiment Analysis has an accuracy level of more than 80% in the languages we support.

Following is the list of supported languages –

Industries covered

Here are the industries that the sentiment algorithm covers:

Best practices

It is recommended to use Machine Learning based algorithms like Sprinklr Sentiments for Sentiments Analysis because it takes the specific context of the text/messages into account while analyzing them for the sentiments attached. You must refrain from creating rules using the Rule Engine for all your sentiment tunings because rule engine conditions are set for specific keywords in a sentence, which may not take the context of the message into account and might lead to incorrect classification of sentiment.

Examples of some corner cases one might encounter

1. Miscalibration in Emotion metric

2. Sentiment miscalibration in foreign languages like French messages

3. Messages not categorized with sentiments

In all such cases, kindly register a Support ticket to resolve it at the earliest.