Sentiment Analysis is about determining the emotional tone of a text. Such datasets allow you to train the model to automatically assess the mood in comments, reviews, and tweets.

Sentiment Analysis

Sentiment Analysis for Machine Learning

What goals can you achieve by using Sentiment Analysis

Using neural networks trained on sentiment definition, a wide range of scientific and business processes can be improved. For example, to detect negative feedback and automatically respond to it by offering promo codes or discounts. Or you can use the model to study your audience: automatically calculate the statistics of reactions to events, products and innovations. The areas of application are very diverse, but to implement them you need high-quality labeled training data.

Types of Sentiment Analysis

Вinary sentiment definition system
In this case, two grades are used to determine the polarity of the document: positive or negative. This is one of the easiest types of tone labeling, since performers have fewer doubts when determining classes.
Multi-class sentiment definition system
Unlike the binary sentiment definition system, the tonality includes other types of emotional tonality. This makes the assessment more accurate and universal.

For example, in addition to positive or negative reactions, neutral, sad, joyful, and others can also be defined. Our software allows you to enter any tone classes needed for your tasks.
Sentiment definition taking into account subjectivity and objectivity
There is a huge difference between "I didn't like the food delivered" and "the delivery was too long. In addition to the emotional sentiment classes, we need to mark the subjectivity and objectivity of the evaluation, so that the neural network learns to understand this difference. To avoid errors, we adjust the accuracy of the check and introduce an additional class for disputed values. The data is then double-checked by people with a linguistic education.

Application example

The LabelMe team is working on data labeling for a wide variety of businesses:
Retail and E-commerce
We labeled tens of thousands of client feedbacks and comments for emotional coloring. Using this data, clients developed algorithms for analyzing and processing feedback. Because of our markup, their systems learned to automatically understand the client's mindset and give them unified responses. This allowed us to reduce the cost of staffing without sacrificing quality.
Computational linguistics
Use of neural networks greatly simplifies the processing and content analysis of large amounts of textual information. For example, a person doesn't need to manually reread and extract the right classes of emotional sentiment. We'll do the analysis and markup so you can create your own automated solution. We work in accordance with all major semantic thesauruses: WordNet-Affect, SentiWordNet, SenticNet, as well as Russian-language analogues RuSentiLex and LINIS Crowd.
Media and social networks
Sentiment analysis is needed to improve moderation algorithms, learning users' attitudes towards different topics, social mood index, and study the portrait of the target audience. LabelMe has extensive experience in parsing and marking the sentiment of texts from a variety of platforms: VKontakte, YouTube, Instagram, Twitter, IQBuzz, Facebook.
Translation algorithms from different languages
Sentiment analysis is actively used in the improvement of automatic translation methods from different languages. This is due to the fact that a professional translator can correctly understand different semantic and emotional constructions. For the algorithm to learn to do the same - it is necessary to include datasets with marked entities and sentiments in the learning process. The accuracy of translations in the future depends on the variety of texts and labeling quality.
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