Sentiment Analysis & Social Data — Picking the Right Tool for the Job

Chris Moody of Gnip

We were recently at the Sentiment Analysis Symposium with many of the leaders in the sentiment space. Conference presenters spanned a spectrum: academics, engineers and executives involved in research and developing sentiment analysis tools. The event was full of great content, and we wanted to share some takeaways.

It was impressive to see the variety of use cases and solutions that were presented — both signs of a rapidly maturing market.  Because of that variety, it was also clear that a one-size-fits-all solution for sentiment analysis of social data is not on the immediate horizon.  An overarching message from the event was that the different use cases for sentiment analysis and the types of social content being analyzed demand tailored sentiment tools purpose-built for the job.

I was also struck by just how central social data was to the conversation and how most of the speakers talked about analyzing social data in some form.  Sentiment analysis has clearly become important to social data analysis, and vice versa, social data is really attractive to companies and researchers seeking ways to understand the moods and opinions of broad populations of consumers and citizens.

Here are some of the highlights of the business cases that were presented at the conference:

  • Mood Analysis Informs Market Decisions at ThomsonReuters — Scanning a broad swath of social content and news allows ThomsonReuters to build their MarketPysch Indices, including their “Gloom Index,” which they’ve demonstrated provides a leading indicator for market drops. ThomsonReuters’ Aleksander Sobczyk made an interesting point for our readers: the social content they focus on is long form only (i.e. blogs), and not short form content like Twitter.  Developing the depth of insight and certainty essential to their current solution requires a bigger text sample than Twitter provides.
  • Guiding Product Development at Dell — Through its Social Media Listening Command Center, Dell picks up on customer sentiment and uses it to drill in and learn more. For instance, they picked up negative sentiment about a new Alienware laptop right after a release: units were overheating. The negative sentiment tipped them off, and their product team took action to learn more from users. Through engaging their customers online, they were able to discover an idiosyncrasy in laptop use among power users. When plugged into big auxiliary monitors, users were keeping the laptop screen nearly closed, blocking the exhaust fans. Dell fixed the fan placement and eliminated the problem for future customers. Knowing very specifically what the negative sentiment was about was critical to catching a problem spot among loyal fans and power users.
  • Understanding Weibo Emotions (了解微博的情感) — Detecting sentiment in other languages with different linguistic structures and use habits can be difficult, which is why Soshio has tried to normalize around universal emotions to help Western brands make sense of what Chinese weibo (microblog) users are saying about their brands.  According to Ken Hu, Soshio’s founder, they’re actually having to translate written language “into sound” to address sentiment challenges like written puns that only make sense when pronounced audibly.

These are just a sample of the stories we heard from the companies at the event, and what we hear from our customers building sentiment tools.  Some serve customers who need to know about general sentiment (i.e. positive or negative), while are others are interested in specific moods or opinions. Some support tailoring to a specific cultural context or industry vertical, while others help evaluate conversation in a broad, general context.  A few are working hard to analyze sentiment in a global context — including native natural language processing for different languages, each with their own norms related to sarcasm, puns, irony, etc. Content format is a major focus as well. Evaluating 140 characters rich with hashtags and URLs on Twitter requires a much different approach than evaluating a WordPress blog post. These are all valid use cases for sentiment analysis, and all come with unique nuances and benefit from different techniques.

Sentiment analysis is a critical category of tool for broader social data analysis, but at the same time, it’s not a monolithic category. If you’re building a product to analyze social data and want to evaluate sentiment, the good news is that you can pick from a wide range of approaches or tools (or maybe even choose more than one?) for sentiment analysis that is suited to the business problem you’re trying to address, and the content you’re trying to analyze.

If you’re interested to learn more about the event and some other interesting developments and business cases in the sentiment analysis world, check out our recent interview with Seth Grimes, the lead organizer for the Sentiment Analysis Symposium.