Number of "Anger" reactions on a Facebook message or comment 

Sum of likes on all the comments of a message 

Number of all comments on a message 

Sum of all engagements on a message like replies, retweets, favorites, etc. 

Number of messages from accounts that are not authenticated in Sprinklr 

Number of "Haha" reactions on a Facebook message or comment 

Count of likes on a message 

Number of love reactions on a facebook message 

Count of messages matching your criteria. One mention corresponds to one message. 

Count of messages excluding message type of retweets 

Number of mentions having a AI identified negative sentiment 

A measure of net vote on a message. It is calculated as Net Vote = Up Votes – Down votes 

Count of AI identified insights with neutral sentiment in Location Insights 

Number of mentions having an AI identified neutral sentiment 

Number of messages from accounts that are authenticated in Sprinklr 

Number of mentions having a AI identified positive sentiment 

Number of followers of a post 

Number of answers given on a Quora question 

Total reactions on a Facebook message like love, haha, etc. 

Count of replies on a message 

Number of reward reactions on a Facebook message 

Number of sad reactions on a Facebook message 

Count of shares of a message 

Count of retweets of a tweet 

Percentage of number of upvotes versus total votes (Upvotes+Downvotes) for a Reddit message 

Number of views on a message 

Number of Wow reactions on a Facebook message 

A count of deduplicated author's display name posting the messages 

Score assigned to measure the level of influence of an author profile based on metrics such as followers, following and posts. The score ranges from 0 to 100 

Total votes a reddit author has earned on all their posts and comments. Higher upvotes results in higher Karma 

Average of experience score for each insights in a message on the basis of its sentiment 

A measure of overall sentiment of a message. It is calculated as: 

Net Sentiment Score = (Positive Mentions - Negative Mentions) / (Positive Mentions + Negative Mentions) 

A relative measure of global popularity of the web domain based on unique visitors and pageviews metrics from the past 3 months 

A search engine ranking score provided by Moz that predicts how likely a website is to rank on Search Engine Result Pages (SERP). The metric ranges from 0 to 100, with 100 being a popular site 

The sentiment score for a message based on all its individual insights. 

Experience Score = 100* (Number of positive insights) + 50* (Number of neutral insights) + 0*(Number of negative insights) / Total Insights 

Ranking of the web domain from Similarweb based on monthly unique visitors and pageviews metrics. Lower the rank, more popular the site 

Number of external links to the domain of the source of the message 

A measure of the authority of the web domain by Moz. The metric ranges ranges from 0 to 10, with 10 being a popular site. Domain Authority is recommended 

A measure of how likely a web domain will be penalized. The metric ranges from 0 to 18, with 18 being a spammy site 

Number of stars on a review at the time of reviewing. It ranges from 0 to 5 

Number of page views of a web message as provided by Alexa. It is calculated as pageviews of the message against a sample of million pageviews made by the Alexa traffic 

Potential number of audience who viewed the web message as provided by Alexa. It is calculated as number of users visiting the site publishing the message out of a million global internet users 

Potential number of audience who viewed the social message. It is measured by including the follower count of the author at the time of posting. 

Estimation of the earned monetary value of a message equivalent to gaining value via marketing and PR efforts. It is calculated based on Media Reach, Ad Rate and Editorial ranks of the publisher and Word Count. 

Potential number of audience who viewed the message for sources like Web sources (Unique Visitors per Month), Print (Circulation) and Broadcast (Viewership) 

Potential number of audience who viewed the web article based on the website’s Unique Visitors per Month (UVPM) on desktop – Powered by Similarweb 

Potential number of audience who viewed the web article based on the website’s Unique Visitors per Month (UVPM) on mobile- Powered by Similarweb 

Potential number of audience who viewed a message.

Overall Reach = Reach of social channels + Media Reach of traditional & web channels. 

Potential number of audience who viewed the social message. It is measured by including the follower count of the messages from accounts that are authenticated in Sprinklr. 

Potential number of unique audience who viewed the social message(s). It is measured by including the follower count of the author at the time of posting and deduplicating users across all messages. 

Potential number of audience who viewed the social message. It is measured by including the follower count of the author at the time of posting. 

Potential number of audience who viewed the web article based on the website’s Unique Visitors per Month (UVPM) on desktop and mobile - Powered by Similarweb 

Potential number of unique audience who viewed the web message based on Unique Visitors per Month (UVPM) but deduped at a domain/publisher level. For eg: If cnn.com has five messages, Web Unique Reach will only be considered for one message. 

Number of unique web domains kike forbes.com who published a message 

Number of comments received on Facebook messages with an article shared in the message

Number of unique news publications like Forbes who published a message(s)

Number of reactions received on Facebook messages with an article shared in the message

Number of AI-generated stories i.e. groups of similar messages

A measure of the impact created by a web message based on two metrics: Moz Rank and Web Shares Overall. The score ranges from 0-100, with 100 being a highly impactful article. 

A measure of the popularity of the publisher’s domain based on MOZ Rank. The score ranges from 0-100, with 100 being a highly influential article 

Number of shares of web messages on Facebook 

Number of shares of web messages on Reddit 

Number of shares of web messages on Twitter 

Total number of shares of web messages on Twitter, Facebook and Reddit 

Count of AI identified attributes with negative sentiment on the message in Product & Location Insights. 

Count of AI identified attributes with positive sentiment on the message in Product & Location Insights. 

Count of AI identified attributes on the message in Product & Location Insights. 

Count of AI identified subject insights with negative sentiment for Location Insights & Product insights. 

Count of AI identified subject insights with positive sentiment for Location Insights & Product insights. 

Count of AI identified subject insights for Location Insights & Product insights. 

Number of mentions containing Photos in the message body

Number of cases where a case in Sprinklr is an entity that can be used to track a message(s) or a thread 

Number of Insights detected in a message with Product & Location Insights module 

Count of AI identified insights with negative sentiment in Location Insights 

The name assigned to the Data Ingestion File 

The tag assigned to the Data Ingestion File 

Tags associated with different domain lists. You can visualize tags to measure the performance of different usecases on the widget 

Widget-specific themes to categorize data based on keywords. To configure them, find "Dynamic Theme Options" within Widget builder for certain widget types like Bar, Column, Line etc 

A profile list of selected author profiles created within Sprinklr (available globally) 

Tags associated with different keyword lists. You can visualize tags to measure the performance of different usecases on the widget 

Set of keywords and filters that can be used to sort Listening Insights data across a pre-built area of focus. 

Tags associated with different theme queries. You can visualize tags to measure the performance of different usecases on the widget 

Topic queries to visualize on the widget with a set of filters inorder to measure the performance 

Topics which are grouped together in a topic group. You can visualize tags to measure the performance of different usecases on the widget 

Tags associated with different topic queries. You can visualize tags to measure the performance of different usecases on the widget 

A profile list of selected author profiles created within Sprinklr (available at workspace level) 

AI identified age category of the author based on profile image and name. By default, this includes 4 age buckets like 0-18, 19-25, etc. 

Classification of authors based on their followers count using defined ranges like 0-500, 500-1000, etc. 

Author's display name as shown in the native channel 

AI identified gender of the author based on profile image and name. By default, this includes two buckets - Male and Female 

AI identified general interests of the author based on user bio like entertainment, politics, technology etc 

Score assigned to measure the level of influence of an author profile based on metrics such as followers, following and posts. The score ranges from 0 to 100 and bucketed with multiple of 10 

AI based classification if an author profile is business/ brand (True) or not (False) 

AI identified marital status of the author based on user bio. By default, this includes two buckets - Married and Unmarried 

AI identified niche interests of the author based on user bio like Actress, Singers, Musician etc 

AI recognised parental status from user bio 

AI identified profession of message author like Artist, Author, Engineer etc. 

Country identified from the profile of the author 

Language identified from the profile of the author 

Classification of the author profile based on the type of profile like Business, Individual, Sports Celebrity etc 

Display name of authors with the highest volume of positive sentiment messages 

Display name of authors with the highest volume of negative sentiment messages 

Display name of authors with the highest volume of messages 

Display name of authors with the highest influencer score 

Display name of authors with the highest reach 

Total number of followers of the author of the message 

Classification if the Twitter author profile is verified i.e. has blue tick (True) or not (False) 

The date/time when the message was created, as captured by Sprinklr or provided by the source partner 

The respective day number in the selected time range where start date will be indicated as Day 1. 

The name of the day of the week based on the created time of the message such as Monday, Tuesday and so on. 

The name of the calendar month based on the created time of the message such as January, February and so on. 

The hour of the day based on the created time of the message such as 01:00, 03:00 etc (based on 24h clock format and time zone) 

The respective week number in the selected time range where starting week will be indicated as Week 1. 

The content of a message as captured by Sprinklr or provided by the source partner 

The root domain name of a message's website of origin. 

Classification of owned messages (messages from the accounts authenticated in Sprinklr) versus earned messages (all other messages). 

Emoticons detected within message text. 

Forum link associated with the message 

AI classification if a brand has responded (True) or not (False). 

Hashtags (#) used within message text like #sprinklr, #cxm etc 

AI identified language of a message like English, Hindi, Chinese etc 

Identifies the URLs mentioned within the message text 

Classification of message based on the digital media format used. Eg: Post, Photo, Video, Link, Status, etc 

Classification based on the type of message and source like Twitter Reply, Reddit Comment, Instagram Post etc 

Flags if the tweet is possibly NSFW/Sensitive (True) or not (False), as provided by Twitter 

Classification of subject of Quora questions in to categories like Food, Nutrition, Banking etc as provided by Quora 

Classifies the facebook owned review between recommended or not recommended. 

The name of the source from which the review message was captured. For eg: Amazon, Lowe's 

The title of the review captured by Sprinklr like Awesome Features, Great Product etc 

AI identified themes at phrase level within underlying messages based on clustering similar phrases 

AI identified top words within underlying messages based on clustering of similar keywords 

The channel from which the message was sourced like Twitter, Facebook, News etc 

AI based classification whether a message is Spam or Not Spam 

AI based classification of Spam into different categories like Advertisement, Spam, Not Spam or Inappropriate/NSFW Content etc 

Permalink to the the associated message from the web source 

Phrases weighted by frequency of occurence within the body of the message(s) 

Keywords weighted by frequency of occurence within the body of the message(s) 

Keywords weighted by frequency of occurence within the title and body of the message(s) 

Keywords weighted by frequency of occurence within the title of the message(s) 

Shows top attributes weighted by frequency of use within the reviews in a word-cloud format. 

Classifies the top subject within insights weighted by frequency of use in a word cloud format 

AI identified emotion associated with a message like Anger, Appreciation, Love etc 

Category based on the AI identified emotion associated with a message like Happiness, Sadness etc 

AI detected sentiment of a message, It is classified as Positive, Neutral & Negative 

AI detected sentiment of the message containing Smart Clustering phrase. 

Potential number of audience who viewed the social message. It is bucketed together in ranges with interval of 500 & measured by including the follower count of the author at the time of posting. 

No. of followers of the user at the time of fetching of a message. 

AI powered grouping of messages with similar content 

Ranking of the media source of the message based on its presence such as International, National, etc. The scale of ranking is from 1 to 5, 1 being a better rank 

Classification on the basis of Media Type, if the message is a news comment (True) or not (False) 

Source-level categorization based on the type of publication that an article is from like Consumer, Trade, Local etc. 

Name of the Media Source for News websites, Blog sites, Print publications, TV channels Eg: Dailymail UK, Forbes, etc 

The headline of the article as available on the online website or the print publication. 

Name of the News wesbite from which the message was sourced like Dailymail UK, Forbes, etc. 

Name of the TV channel from which the broadcast was sourced 

AI identified activity within a photo mention for Visual Insights module. For eg, Smile, Event, Writing, etc. 

AI identified brand within a photo mention for Visual Insights module. For eg, Sprinklr, Apple, etc. 

Classifies enrichment status of a photo mention, i.e. if a photo has been enriched by visual insights model or not 

AI identified Group or an Individual within a photo mention 

AI identified gender within a photo mention. By default, this includes 2 gender buckets – Male and Female 

AI identified general objects within a photo mention. For eg: watch, Vehicle, Building, etc. 

AI recognized scenes within a photo mention like Movie, Room, Sky, etc. 

AI identified sentiment within a photo mention. The sentiment can be Positive, Negative, or Neutral. 

Display name of authors with the highest influencer score posting the photo brand. 

Classification of the business location as 'My Brand' or 'Competitors' Brand' as per the configuration of locations within the Location Insights module 

The name of the brand as per the configuration of locations within the Location Insights module 

The location configured as per the configuration of locations within the Location Insights module 

The city of origin for a message which is derived either based on author profile or source location or derived by Sprinklr AI based on content, etc. 

The city mentioned within the userbio of the author of a message 

The name of the city associated with business location as per the configuration of locations within the Location Insights module 

AI recognised city from a message body like Columbus, Miami, etc. 

The partner brand locations i.e. competitor location that are mapped to the Business Location in the Location Insights module 

The continent of origin for a message which is derived either based on author profile or source location or derived by Sprinklr AI based on content, etc. 

The country of origin for a message which is derived either based on author profile or source location or derived by Sprinklr AI based on content, etc. 

The country mentioned within the userbio of the author of a message 

The name of the country associated with business location as per the configuration of locations within the Location Insights module 

AI recognised country from a message body like Belgium, China, etc. 

The geographical region of origin for a message which is derived either based on author profile or source location or derived by Sprinklr AI based on content, etc. 

The state of origin for a message which is derived either based on author profile or source location or derived by Sprinklr AI based on content, etc. 

Indicates the state mentioned within the userbio of the author of a message 

The state mentioned in the body of a message 

The name of the state/ province associated with business location as per the configuration of locations within the Location Insights module 

The zip codes associated with business location as per the configuration of locations within the Location Insights module 

Average star rating across all mentions pulled into Sprinklr. It ranges on a scale of 0 to 5 stars 

Average star rating across all mentions pulled into Sprinklr. It ranges on a scale of 0 to 5 

Categories associated with a mention powered by AI for Location & Product Insights. For eg: Quality, Pricing & Value, Availability etc. 

AI identified top keywords/phrases associated with the message. Applicable for Location & Product Insights modules 

AI identified names of organization present in the review. 

AI identified subject categories associated with an insight. For eg: Personel, Product, Fixtures etc 

Indicates if a message or a case is archived or not archived 

The name of the user(s) who assigned the message(s) or case(s) 

The name of the user(s) to whom the message(s) or case(s) is assigned 

AI based classification of message into 'Care' and 'Non-Care' message type. 

Details of case created within sprinklr platform, including Case ID, Case subject & description. 

A unique ID associated with each case. 

AI classification if a message is engageable (True) or not (False). 

The link of the web application used to generate a tweet like mobile.twitter.com, twitter.com, etc 

The name of the web application used to generate a tweet like Twitter for Android, Twitter for iPhone, etc 

Indicates the name of Global Queues (created within Sprinklr to store various types of assets) associated with a message. It is accessible only at Global level 

AI recognised customer feedback categorization on the basis of message content like Complaint, Issue, Enquiry, Compliment, Leads, etc. 

Tags assigned to a message, either manually by users or automated via rules 

Indicates the priority (created within Sprinklr to classify priority) associated with a message like Highest, High, medium, Lowest, Low 

Classification if a message/ case is private (True) or not (False). 

The value assigned in the Status field of each message/ case. This field can be set in Sprinklr under All Settings. 

Indicates the name of Workspace Queues (created within Sprinklr to store various types of assets) associated with a message. It is accessible only at workspace level. 

AI identified potential consumer equity issues within a message such as socio-political issues, employee/ workplace issues, financial issues and business/ operational issues 

AI identified potential consumer equity issues within a message such as Company Financial, Layoffs, Discrimination etc. at a granular level below respective Consumer Equity - L1s. 

AI identified categories of a message based on its inclination towards a broad cultural style like Individualism, Survival, Authority, etc. 

AI recognised industry like Retail, Technology, etc. based on the message content such as brand name 

AI recognised event from a message like Superbowl, Earth Day etc. 

AI recognised language from a message like English, Spanish etc. 

AI recognized brands mentioned in a message like Sprinklr, Apple etc. 

AI recognized brand variation from a message like Sprinklr, Sprinklr Inc etc. This is based on a set of known brands which are identified in a message. 

AI recognized person/individuals mentioned in a message like Christiano Ronaldo, Lionel Messi etc 

AI recognized product names mentioned in a message like Glass, Earring etc 

AI identified Subject verbatims captured from phrase insights in reviews 

Phrase or insight level sentiment predicted by the Sprinklr Location Insights Model. The sentiment can be Positive, Negative or Neutral. 

AI identified brand reputation pillars within a message such as Workspace Wellbeing, Sustainability etc