Researchbods has launched Conversation IQ, a new sentiment analysis tool.
Conversation IQ, which sits within the ex-plor insight community platform, helps researchers to capture, quantify and analyse large quantities of qualitative data for entity and sentiment more effectively using an AI driven engine. The tool uses natural language processing to surface and display key themes and entities from areas across an insight community, including discussion rooms, social hubs and forums. These themes can be displayed as word clouds and multiple graphs, or filtered from an intuitive dashboard by task type, date range, language and more.
With the launch of Conversation IQ, Researchbods aim to make the analysis of qualitative research tasks within an insight community much less time and resource intensive. Conversational and qual data can be rapidly analysed at scale – compressing timescales from days into minutes. This means researchers and clients can focus more time on drawing out key insights on the topics and themes that are important to their members and to their brand.
We pride ourselves on combining people experts with next gen technology and innovation. This approach allows our clients to get to the heart of consumer messages, to make better decisions and ultimately grow their business. Conversation IQ is a great example of this vision in action. Taking thousands of unstructured data points from across an insight community and using the power of text analytics and machine learning, we can drive research professionals towards key themes and compelling insights much faster than ever before.”
Dave Naylor, Chief Technology Officer
Key features
- Automatically identifies recurring themes and popular entities within discussion rooms, social hubs and forums.
- Aggregated comments related to these themes that can be easily filtered and collated to benefit the research focus.
- Identifies and visualises members’ comments as word clouds to highlight key entities, which can be mined for further analysis.
- Appling sentiment analysis helps to derive scoring on how community members feel about these entities – positive, negative, or indifferent.
- Provides dynamic filters to control the data views, e.g. by specific task types, individual tasks or specific date ranges – or combined data from across the insight community.
- Enables visualisation and interrogation of data via multiple graphs and filters.
- Promotes overall reliability, validity and qualified generalising of qual data from across an insight community.
