8,000+ text messages labeled, 10,000+ images annotated

Overview

The client needed to improve the accuracy and consistency of both text and visual data. The project focused on identifying and classifying entities in text, analyzing sentiment, and adding detailed labels to image content.

We labeled text messages for entities such as names, organizations, and locations while also classifying sentiment as positive, negative, or neutral. In parallel, images were annotated with detailed descriptions and labels, creating a more structured dataset for analysis and AI applications.

The challenge

Inconsistent text classification — the client needed accurate identification and categorization of entities such as names, organizations, and locations within text data.
Unstructured sentiment data — text needed to be classified by emotional tone to make sentiment information more useful for analysis.
Limited visual data structure — images required detailed descriptions and labels to improve indexing, searching, and the overall organization of visual information.

What we did

1 Annotated text entities — identified and classified names, organizations, locations, and other relevant entities within text data.
2 Applied sentiment labels — classified text according to sentiment as positive, negative, or neutral.
3 Annotated visual data — added detailed descriptions and labels to relevant elements within images.
4 Applied quality-focused annotation — reviewed annotations to improve accuracy and consistency across both text and image datasets.

Results

8,000+ text messages labeled — creating structured text data with entity and sentiment annotations.
10,000+ images annotated — adding detailed labels and descriptions to visual data.
98% accuracy and efficiency achieved — maintaining a high level of quality across the annotation process.
More structured training data — improving the organization and usability of both textual and visual datasets.

Our methodology

How we structure text and image data

Effective annotation requires consistent labeling rules across different data types. A structured process helps turn raw text and visual content into datasets that are easier to analyze and use for AI applications.

Entity annotation

We identify and classify relevant entities such as names, organizations, and locations within text.

Sentiment classification

Text is labeled according to its sentiment to distinguish positive, negative, and neutral content.

Visual data annotation

Images are reviewed and labeled with detailed descriptions of relevant elements.

Annotation quality control

Completed annotations are reviewed for accuracy and consistency across both text and image datasets.

The team consistently shows professionalism in how they interact and operate, creating a smooth and efficient working relationship. Their excellent communication skills not only make things clear but also create a collaborative environment. Their careful attention to detail is noticeable, showing their commitment to delivering high-quality work.

— Spokesperson, Software Company