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Text Analytics

Text analytics platforms rely on Natural Language Processing (NLP) to make sense of unstructured data captured from a range of sources: verbatim comments in surveys, reviews posted in forums, posts in social media, comments transcribed in call centres etc. Key features include sentiment; categorisation or topic analysis; entity extraction (eg to identify brands); and visualisation – usually in an online dashboard. Text analytics capabilities are also built into some platforms in other categories (Social Listening, Surveys).

    • Adoreboard

      Adoreboard provides a consulting service and technology platform for emotion analytics. The ‘Emotics’ software enables analysis of proprietary or competitor data and comparison with industry benchmarks using 88 emotion indexes, topic analysis and explanations. Data sources include social media, NPS verbatim, surveys, news and other data.

    • Ascribe

      Ascribe provides services and software to code and analyse verbatim comments from resaerch surveys. ‘Inspector’ is a software product for fully automated coding of open-ended survey responses and verbatim comments from surveys, emails, call centers and social media. Based on NLP, it can integrate with structured data, features a reporting dashboard of concepts and sentiment and can handle multiple languages. The firm also provides verbatim coding services on a project or long term basis.

    • Canvs

      Canvs analyses and categorizes “Emotional Reactions” in dialogue. The technology is embedded in a range of products with specific focus areas: TV (understanding audiences); Surveys (processing open-ended responses); Watch (focused on Facebook video); Campaigns (for analysing advertising and branded content campaigns); Movies (more audience insight, with predictive analytics tying emotional responses to box office sales forecasts); and Publishers (for analysing reader feedback. An API is also available for custom integrations.

    • Chattermill

      Chattermill uses machine learning to analyse customer feedback from tracking studies, reviews and support tickets. Through integrations with CRM tools, survey platforms and marketing automation systems, algorithms process customer feedback in real time and results are available instantly in online dashboards.

    • Codit.co

      Codit is online text analysis software for coding open-end responses to surveys. The tool enables a researcher to code responses or categorize text using a streamlined interface. The intelligent assistant continually learns how to predict codes for responses based on previous examples, and improves its effectiveness over time.

    • CX Moments

      Cx Moments detects trends and topics in feedback surveys, support activity, bug reports, product mentions and other sources. Integrations are available for VoC tools including Medallia, Confirmit and Qualtrics; as well as Zendesk, Freshdesk, Salesforce, LivePerson, Freshservice and other customer support tools.

    • Decooda

      Decooda is a ‘Cognitive Intelligence Machine’ for analysing text to detect sentiment, emotions and cognitive states in customer experience feedback. It links these analyses topics to identify specifically what impacts each customerĂ•s experience and why. The platform listens and aggregates data using cognitive psychology, deep learning and AI to analyze customer interactions in real-time.

    • Dictanova

      Dictanova has a set of semantic algorithms for analysis of voice-of-customer feedback. It is an API solution for developers to analyse product reviews, satisfaction surveys, CRM notes and other sources.

    • Gavagai

      Gavagai Explorer provides qual-to-quant conversion and analysis of unstructured text data such as answers to open-ended survey questions, customer reviews, online mentions, customer support tickets and other sources. Advanced analysis includes automated thematic clustering and sentiment in more than 40 languages.

    • Heartbeat AI

      Heartbeat AI transforms text input from any source (survey open-ends, call center transcripts, customer feedback, product reviews, employee comments) into ten primary and a hundred secondary emotion categories. These emotion groups, derived from thousands of words and phrases, are accessible through an online dashboard in near-real time.

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