Fully Customizable

Fully Customizable AI tool
Fully Customizable

Projects in AISpotters can be customized to the unique customer requirements by posing various question types such as short answer, paragraph answer, multiple choice, multiple select, likert scale, drop down, multiple choice/select grid. With 8 different options to gain insights in the data, we are certain that this covers majority of your project needs.

multiple question type

Question Agglomeration

A project created in AISpotters can have any combination of below question types. Combining several or even one of the below question type can help you gather in depth human intelligence from the data. Without any limit to the type and number of questions that can be set in a project, we believe we can majority of your annotation needs.


Single Answer

This question type is chosen when the answer to the question can be provided in a single line with few words. Suited best for those insights, which summarizes the data qualitatively such as translation in NLP (applies for Indic languages), short transcription in audio, captions for image and video.


Paragraph Answer

Paragraph question types are intended for more in-depth analysis of the input data, where the responses span for multiple sentences. Example projects with these question type can be “Story completion” in NLP, multiple captions/descriptions in image/videos and multi sentence speech to text transcription in audio. There will be no pre defined word limit for either of the question type, unless specifically indicated by the user.



In certain cases, the user would like the human annotator to select one option from the multi choices. When multiple such insights are needed for a single data, the user will simply repeat these question types. Some of the examples of such annotations are emotion analysis in NLP, presence/absence of object in images/videos and question answering in audio.


Multiple Choice

The question type is similar to checkbox, where the user expects the human annotator to pick from the pre defined list of options except here the annotator is allowed to select more than one option for a single question. Selection of available objects from list in image/video, picking all of the emotional states for a given sentence or paragraph are some of the examples for this question type.


Likert Scale

Likert scale allows the annotators rate the intensity of a particular label for the given data. This question type provides exactly that facility for the user, and above that the scale can be customized by the user to show the right qualification of the scale. Some of such examples could be scale between 1-7 the quality of a image or video, scale between most-intense to most-subtle emotion expressed in an audio or sentence.



A dropdown provides the same functionality as that of Multiple choice question type, except that in the case of drop down question type the user has moment to think about the response for the question prior to looking to the choices; there by removing the inherent bias. This question type can also be used to save space, when multiple questions are needed to be shown in a single screen.


Checkbox Grid

A question type that serves as a consolidation of multiple checkbox questions, where the annotators can see all the choices available for each of individual categories for a given data. In this question type, the rows represents the qualitative parameter and the columns represents potential values these parameter can take. An example in video could be “quality”, “relevance” etc as rows, “bad”, “average”, “good” as column. Annotators will be able to select one value for each of the parameter.


Multiple Choice Grid

This question type is very similar in its application to the checkbox grid, but allows the annotators to select more than one value for each row. An example in video annotation could have “Person talking”, “Argumentative” and “speaker 1”, “speaker 2” as column.

Case Study 1 - Sentence Annotation

An application where the researcher intends to qualify a sentence beyond sentiment by annotating the emotions expressed in the sentence, while doing that the researcher also attempts to gather the sentiment expressed, also tries to get the key words used by the annotators to determine the emotions and sentiment and also attempts to identify the parts of the speech. As explained in this case study, this project comprises of following question type,

  • Emotions represented by checkbox as more than one emotion can be expressed in a single sentence
  • Sentiment expressed by Multiple choice providing the option of select either “positive”, “negative” or “neutral”
  • Parts of speech is represented by 4 single answer corresponding to “Verb”, “Noun”, “Adjective” and “Adverb”
  • Key words can be answered by either single line answer or paragraph