Services

Social Media Services We Offer

SentiSquare

SentiSquare is an online service for digital marketing managers who deal with high traffic and noise in social media and can’t comprehensively monitor what their consumers are saying about their brands around the globe.

SentiSquare uses deep semantics to discover and summarize opinions hidden in multilingual content, giving a clear understanding of the main issues customers are facing.

Our Services

Gain Insights

The main benefit from using SentiSquare is understanding the customer desires expressed in social media conversations. Thanks to this knowledge we find the way to approach them.

Summarize Opinions

There is abundant amount of comments all over the internet. Each comment is potentially a customer opinion. SentiSquare helps to understand the mass opinions in a moment by providing opinion summaries.

Prioritize Content

The ability to detect the importance of customer opinions is one of the key features of SentiSquare. We can thus prioritize topics and opinions based on its impact.

Follow Trends

New topics continuously arise while former topics are die out. This topic evolution is captured directly from data by the SentiSquare technology.

Behind SentiSquare

Technology

Our team has 12-year history in text analysis research. We are strong in Semantic Analysis, Sentiment Analysis, Summarization, and Information Extraction.

Semantic Analysis

Semantic analysis is essential part of SentiSquare. We go beyond keywords and understand the text.

Language Independence

SentiSquare algorithms do not depent on the language. We analyze Czech, Hungarian, French, English, Slovak, Polish, German, Italian, Spanish, ... you name it!

Big Data

Our work starts when it is not in human capacity to read and sort data anymore. We analyze all the data available on the internet as well as internal data of our customers (customer care, call centers, e-mails, public internet forums).

SentiSquare Technology

Sentisquare software gathers comments related to a brand from various sources. The comments are clustered into topics using semantic analysis, which is able to link two opinions even if they are expressed in different words. Thanks to this, Sentisquare can compute how many contributors wrote about a topic and prioritize the topics.

For each topic, it automatically produces a summary of key opinions by selecting the most important text spans. The vector representation enables to link topics across different brands. Thus we can see how unique the topics are for a particular brand. It also provides temporal linking of topics. We can see then how topics develop over time.

On the top of the above, we use unique method that deals with complex morphology of a language. Thanks to this, Sentisquare is able to work even with less frequently used languages (such as Czech language).