Insights from your content

The value of keywords for understanding sentiment and intent

Morphological Analysis and Keywords Extraction

You have textual data. Probably lots of it. And your textual data can be a source of precious insights for both your technical and commercial purposes. In this respect, the correct analysis is critical, and aspects like specific language or context need to be taken into account for results you can trust.

To correctly perform intention detection, extraction, and sentiment analysis from a wide variety of textual data, you need the appropriate morphological analysis tool or extraction tool calibrated on your target language.

In this way, you can improve the analysis accuracy of your textual, voice and image data.

Further, you may need to extract keywords for terminological purposes, especially if your company does not yet have a company glossary. And glossaries are an exceptionally effective tool for quality assurance purposes, a coherent branding and a unified communication strategy.

ASAP Globalizers can extract the sought information from text data or a large amount of translation assets and provide it to you in an easy-to-use format.

Customer case 1

Target data type: social media posts and product reviews on e-commerce sites

Goal: Obtain feedback about your product or service from posts and reviews to be used for product development and marketing.

By labelling and classifying the customer’s reviews and related SNS posts into searchable data, you can aggregate feedback data offered by the customer. Through a morphological analysis of that data, you can extract verbs or adjectives, classify them into positive and negative reactions, and understand where to take action to improve the product or the relationship with your customer.

Customer case 2

Target data type: Text data from enquiries emails and recorded voice data of phone calls

Goal: Create FAQ pages on the website and AI-based- query prediction

You can generate an incremental FAQ website based on real concerns and queries by labelling and classifying the data extracted from the inquiry emails and the voice-to-text data converted by voice recognition. A richer FAQ site will reduce the amount of time spent responding to email or phone enquiries while improving overall customer satisfaction.

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