Sentiment analysis is contextual mining of text, which identifies and extracts subjective information in the source material and helping a business to understand the social sentiment of their brand, product, or service while monitoring online conversations. However, analysis of social media streams is usually restricted to just basic sentiment analysis and count-based metrics. This is akin to just scratching the surface and missing out on those high-value insights waiting to be discovered. So what should a brand do to capture that low hanging fruit?
With the recent advances in deep learning, the ability of algorithms to analyze text has improved considerably. Creative use of advanced artificial intelligence techniques can be an effective tool for doing in-depth research. We believe it is important to classify incoming customer conversation about a brand based on the following lines:
- Key aspects of a brand’s product and service that customers care about.
- Users’ underlying intentions and reactions concerning those aspects.
These basic concepts become an essential tool for analyzing millions of brand conversations with human-level accuracy when used in combination. In the post, we take the example of Uber and demonstrate how this works. Read On!
here.
Intent Analysis
Intent analysis steps up the game by analyzing the user’s intention behind a message and identifying whether it relates to an opinion, news, marketing, complaint, suggestion, appreciation, or query.
Analyzing intent of textual data
Contextual Semantic Search(CSS)
Now, this is where things get really interesting. To derive actionable insights, it is important to understand what aspect of the brand a user is discussing. For example, Amazon wants to segregate messages related to late deliveries, billing issues, promotion-related queries, product reviews, etc. On the other hand, Starbucks wants to classify messages based on whether they relate to staff behavior, new coffee flavors, hygiene feedback, online orders, store name, location, etc. But how can one do that?
We introduce an intelligent smart search algorithm called Contextual Semantic Search (a.k.a. CSS). CSS works because it takes thousands of messages and a concept (like Price) as input and filters all the messages that closely match the given concept. The graphic shown below demonstrates how CSS represents a major improvement over existing methods used by the industry.
Existing approach vs. Contextual Semantic Search
A conventional approach for filtering all Price related messages is to do a keyword search on Price and other closely related words like (pricing, charge, $, paid). However, this method is not very effective as it is almost impossible to think of all the relevant keywords and their variants that represent a particular concept. On the other hand, CSS takes the concept (Price) as input and filters all the contextually similar even where the obvious variants of the concept keyword are not mentioned.
For curious people, we would like to give a glimpse of how this works. An AI technique is used to convert every word into a specific point in the hyperspace. The distance between these points is used to identify messages where the context is similar to the concept we are exploring. A visualization of how this looks under the hood can be seen below:
Visualizing contextually related Tweets
Time to see CSS in action and how it works on comments related to Uber in the examples below:
Similarly, have a look at this tweet:
In both the cases above, the algorithm classifies these messages as contextually related to the concept called Price even though the word Price is not mentioned in these messages.
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Sentiment Analysis. At Karna, you can contact us to license our technology or get a customized dashboard for generating meaningful insights from digital media. You can check the demo here.
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