Multilingual Sentiment and Data Analysis

You’ve launched your product overseas and you want to know what your new customers think. See the big picture with multilingual sentiment analysis.

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Increase sales

Find out what your new customers think, make the correct adjustments, and see your sales soar.

Respond quickly

React faster to customer’s concerns and create lifelong loyalty to your brand.

Latest tech

We work with natural language processing (NLP) to analyze huge segments of data. That gives you a bird’s eye view of customer reaction to your product or brand.

product reviewer

Find out what people all over the globe think about your company

Our specialists at Summa Linguae analyze data collected from internet chat rooms, traditional and social media, and other sources. A customer may want to analyze customer feedback on a particular product or service with the data.

After the data is translated and tagged, it can then be analyzed to see if the overall sentiment is positive, negative, or neutral. If the same words or phrases keep popping up, it can point you toward a specific issue of your product.

More than good and bad

Sentiment analysis moves beyond positive, negative, or neutral to offer more specific feelings.

It covers a wide spectrum, and detects more specific feelings and even intentions. The level of information you receive is dependent on your specific needs, and the output is tailored accordingly.

Here’s a look at some of the main types of sentiment analysis.


Graded Sentiment Analysis

Negative-Neutral-Positive can help you get a sense of how people are feeling about a specific product or service of yours. You can also focus on a specific keyword or topic that’s buzzworthy within your industry or field. Use a 5-star scale to give you more granularity when it comes to your reviews.


Emotion Detection

This type of sentiment analysis helps pinpoint emotions customers are expressing in their feedback, from happy and satisfied to angry and frustrated.


Aspect-Based Sentiment Analysis

Speaking of training the NLP system, they can also be given the ability to determine whether the text data expresses an implicit positive or negative sentiment. If you’re looking at someone’s conversation with a chatbot, their feelings may not be explicit and you may need to infer feelings based on language. This is sentiment analysis that isn’t 100% black and white.

Get in Touch

Contact us now to get started with multilingual data services at Summa Linguae Technologies.

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