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The proliferation of unstructured data in today’s world of the Internet of Things (IoT) and Web 2.0, is overwhelming due to the exponential increase in the interconnectivity and engagement amongst people / processes / devices, newer technologies, and the changing consumer lifestyles. Unstructured textual data is being constantly generated via call center logs, web logs, emails, documents on the web, blogs, tweets, customer comments, customer reviews, sales notes in CRM applications, and so on. While the amount of textual data is increasing rapidly, businesses’ ability to summarize, understand, and make sense of such data for making better business decisions remain challenging.

At KMS, our Text Mining and Sentiment Analysis (or Opinion Mining) solutions help organizations to analyze unstructured data and extract subjective information out of raw text data. Using advanced text analytics methodologies like parsing, tokenization, n-grams, classification and sentiment analysis we provide insights on opinions - thoughts, feelings, emotions. In other words, you can find out what people think about your organization or brand.

Our Text Mining and Sentiment Analysis solutions can be applied in Market and Brand research towards:

Share of voice: Share of an organization / brand among the consumer conversations relevant to their areas of business.
Source of opinion: Analysis of sources or channels where brand/company related conversations are observed.
Mood: Analysis of the mood of consumers in the conversations (anxious, depressed, angry, happy, etc.).
Sentiment: Analysis of tone of articles / reviews / conversations (positive, negative, neutral).
Brand Modelling: Analysis of consumer sentiments about or engagement with a brand or a brand category.
Trend Modelling: Analysis of trends in conversations in terms of frequency, intervals, volume of mentions, sentiments, etc.
Topic Modelling: Analysis of the type and context in which an organization / brand is mentioned in the text.
Emerging Trends: Analysis of emerging trends and conversations in the areas where a business operates.
Competition: Analysis of customer perceptions towards competitive brands, competitor activities, competitive benchmarking.
Campaign: Analysis of the effects of a campaign on conversations with respect to an organization / brand.
Context: Analysis of conversations to understand the context behind it.
Example: Analyzing call center logs to understand customer complaints or consumer conversation on a purchase experience.

Our Text Mining and Sentiment Analysis flow into our event processing and operational intelligence services, to correlate text data with other data sources and drive actionable business insights on business processes, events and operations.