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Sentiment Analysis (Beta)

In Beta

This feature is in Beta. If you have questions or comments, emailย

Sentiment Analysis is the interpretation of the general thought, feeling, or sense of an object or a situation.

Symbl's Sentiment API works over Speech-to-Text sentences and Topics (or aspect).

Sentiment APIโ€‹

To see the Sentiment API in action, you need to process a conversation using Symbl. After you process a conversation, you'll receive a conversation Id which can be passed to the following Conversation APIs. All you need to do is pass the conversationId and query parameters sentiment=true.


Each continuous sentence spoken by a speaker in conversation is referred to as a Message. Hence ,we named our Speech to Text API as Messages API. Messages API returns you a list of messages in a conversation.

๐Ÿ‘‰Messages APIโ€‹


For topic level, the sentiment is calculated over the topic messages scope i.e. it factors in the sentiment of messages where the topic was talked about.

๐Ÿ‘‰Topics APIโ€‹

API Responseโ€‹

"messages": [
"id": "6412283618000896",
"text": "Best package for you is $69.99 per month.",
"from": {
"name": "Roger",
"email": ""
"startTime": "2020-07-10T11:16:21.024Z",
"endTime": "2020-07-10T11:16:26.724Z",
"conversationId": "6749556955938816",
"phrases": [
"type": "action_phrase",
"text": "$69.99 per month"
"sentiment": {
"polarity": {
"score": 0.6
} ]


polarityShows the intensity of the sentiment. It ranges from -1.0 to 1.0, where -1.0 is the most negative sentiment and 1.0 is the most positive sentiment.
suggesteddisplay suggested sentiment type (negative, neutral and positive).

suggested objectโ€‹


We have chosen the below polarity ranges wrt sentiment type which covers a wide range of conversations. Polarity Sentiment may vary for your use case. We recommend that you define a threshold that works for you, and then adjust the threshold after testing and verifying the results.

PolaritySuggested Sentiment
-1.0 => x < -0.3negative
-0.3 => x <= 0.3neutral
0.3 > x <= 1.0positive


  • View tutorial on Sentiment Analysis on Messages here
  • View tutorial on Sentiment Analysis on Topics here