Sentiment Analysis from Text

This bot takes a text and extracts the global Sentiment Analysis in it, as well as the global polarity for the entities and keywords detected in the text.

Top Benefits

  • Detect if what is said in a comment or document is positive, negative, neutral, or has no polarity.
  • Do opinion mining in news, social comments and contact center interactions.
  • Perform a granular analysis, assessing polarity both at a whole document level and at a sentence/segment/entity/concept
  • Identify subjectivity, irony and contradiction.
  • Easy to integrate and fully customizable.

Tasks

  • Analyze the global sentiment expressed in a text
  • Extract the entities in the text with the polarity associated to them
  • Extract the concepts or keywords in the text with the polarity associated to them
  • Obtain the full sentiment analysis of a text

This bot carries out a request to MeaningCloud's Sentiment Analysis API. MeaningCloud's Sentiment Analysis does a complete morphosyntactic analysis and returns a complete sentiment analysis at a global, sentence and segment level. It also detects the entities (organizations, locations, people, etc.) and concepts (keywords) in the text, and the polarity associate to them to enable you to make aspect-based sentiment analysis.

The bot will output the global polarity for the complete text, a list of the entities detected with their polarity in parentheses (and their type if configured), a similar list for concepts/keywords detected, and a JSON with the complete analysis returned by the API. Having the JSON response gives you access to all the information provided by the API, irony, subjectivity, as well as the sentences identified with the entities and concepts analysis at a sentence level.

Thanks to MeaningCloud, you can customize the analysis using user dictionaries (https://www.meaningcloud.com/developer/resources/dictionaries) to ensure the detection of the entities and concepts you want to analyze (and with the ontology type you want) and user sentiment models (https://www.meaningcloud.com/developer/resources/sentiment-models), to customize the sentiment analysis if the general scenario does not apply.
For instance, for the sentence The restaurant was great even though it’s not near Madrid., we will obtain a global sentiment analysis of P+ (Strong positive), the entity Madrid without any polarity (Madrid (NONE)) and the concept restaurant with strong positive polarity (restaurant (P+)).

Typical uses cases for Sentiment Analysis include social media analysis to analyze trends or brand reputation, Voice of the Customer in surveys or social media, etc.

Get Bot

Free

Bot Security Program
Level 1
Applications
Business Process
Category
Downloads
64
Vendor
Automation Type
Bot
Last Updated
July 24, 2020
First Published
December 5, 2019
Enterprise Version
11.x
Community Version
11.3.1
ReadMe
ReadMe
Support

See the Bot in Action

Sentiment Analysis TaskBot
Example response of analysis available
JSON response with complete analysis
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Sentiment Analysis TaskBot
Example response of analysis available
JSON response with complete analysis

Setup Process

Install

Download the Bot and follow the instructions to install it in your AAE Control Room.

Configure

Open the Bot to configure your username and other settings the Bot will need (see the Installation Guide or ReadMe for details.)

Run

That's it - now the Bot is ready to get going!

Requirements and Inputs

  • Access to MeaningCloud Sentiment Analysis 2.1 (MeaningCloud account)
  • Input a UTF-8 text