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Social Media Analytics: Sentiment Analysis

Social Media Analytics: Sentiment Analysis image influence info kdoe8tI talked a little about sentiment analysis earlier. Sentiment analysis mainly tracks the prevailing opinion of your brand and it’s most effective for larger brands, especially consumer brands. For smaller brands, there just aren’t enough mentions of your brand to make sentiment analysis very insightful. For small brands, scouring specific websites, such as Yelp, EOpinions, and other places likely provides more insights regarding prevailing opinions about your brand.

Sentiment analysis

Sentiment analysis combines natural language processing with artificial intelligence capability and text analytics to evaluate statements found across various social platforms to determine whether they are positive or negative with respect to a particular brand. A firm might track sentiment analysis over time to:

  • determine whether their actions improve or damage sentiment
  • track brand reputation
  • test how marketing efforts affect attitudes toward the brand
  • attitudes toward new products

For instance, maybe a user posts:

XYZ brand is bad ass!

Sentiment analysis must accurately categorize this as positive sentiment, despite the use of bad in the post. In other words, the tool needs to understand colloquial usage (natural language) to know that bad means good in this context. Artificial intelligence comes in when the tool is “trained”. So, let’s say the tool initially categorized the post as negative because of the word “bad” in the post. A human, as part of the training process, goes in and tells the system they were wrong – this is a positive statement. The tool “learns” that “bad ass” means good and doesn’t make that mistake again – at least not as often.

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Now, I could spend several posts showing you how to calculate sentiment using advanced statistics and tuples (don’t ask – they give me a headache), but I assume you just want to use them, not create a tool to analyze sentiment. Hence, I’ll talk about tools already available as a SAAS (software as a service).

There are lots of tools out there for measuring and tracking sentiment. SAS and IBM both make excellent tools, but they’re expensive and a little unwieldy for the novice – designed more for business intelligence specialists. Salesforce.com (marketing cloud), Trackur, Chatterbox, ViralHeat, and Radian6 all have sentiment analysis or include sentiment analysis as a piece of their social media automation. These tools are much more intuitive and user-friendly.

No matter how good, two problems exist with sentiment analysis: 1) training the system and 2) hearing. No tool is able to accurately score sentiment right out of the box and some are easier to train than others. That’s because sentiment is unstructured so the system has to be trained to determine if a particular statement is positive or negative. There’s also the issue of slang, misspelling, and weird sentence structure, which further complicates sentiment analysis. Training can be very time-consuming and tools vary in their ability to be trained, some can’t be trained at all. Across major tools, the industry average is about 65% accuracy – not so good in my opinion.

Hearing is the other problem with sentiment analysis. Some tools only listen to conversations in certain social media, although the ones I listed above listen across Facebook and Twitter and maybe a few other social platforms. Listening to blogs is a little more complicated because the posts are longer. Google Alerts can handle that, but I’ve not found them proficient at detected all conversations going on out there. Still, setting up an alert for your brands, key personnel, and some competitors makes sense. Google Alerts must be hand-scored or use a text analysis program, such as Nvivo or HyperRESEARCH.

A colleague interviewed for this book told the unfortunate story of hiring an intern to cull through the alerts and categorize them on a spreadsheet so the client could respond to unfavorable mentions. Unfortunately, this proved too big a task for the intern or Google. Thus, the client, a pharmaceutical company, was unaware of problems with one of their drugs until contacted by the FDA. Not exactly what the company wanted from their listening program.

Most work fairly similarly, so let’s talk about Trackur, since I have most experience with that platform. Above, you’ll see what one of the dashboards looks like.

Trackur lets you select keywords related to your brand. So, if I were using Trackur to monitor my business, I’d use keywords including my name and company name, if I had brand names, I’d use them, as well as names of key competitors, and industry names such as social media analytics. Trackur will track these keywords, calculated sentiment related to you and your brand, and chart changes in sentiment over time. Trackur has some nice features, in that it also gives you information about users mentioning your brand, thus combining some elements of influence analysis with sentiment analysis.

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Comments on this Article: 5

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  1. Arun Singh says:

    Thanks for this great post. It does seem to me very amazing that analysis (math) can pick up word combination to give a good representation a feeling. I guess errors can come with the inability of a program to identify the nuances of the language, ie language style mostly like irony or humor… Still, it is quite impressive!

  2. Hi Angela, great post. Your summary is exactly right. We’ve been working in the Social Media Analytics space for over 2 years now and continue to be amazed at the difficulty of the problem.

    One part of your post is the special challenge; that in including the blog posts themselves in the analytics and merging that information with the social channels is very, very difficult. Our SaaS tool SocialEars is just entering beta now and does exactly that. We scan the major social channels (TW, FB, LI, RSS) and also all of the blogs and articles that those channels point at (usually using short-urls). All of that is merged in real-time to identify ongoing conversations and those that are most influencing those conversations.

    The real difficulty for us was in making this topic-focused which we have succeeded in doing. We needed to employ some difficult mathematical pattern analysis to the results to reduce the noise level. Good thing is we’re launched the the cloud, so computing power is easily available for this challenge.

    In fact your blog article was discovered by SocialEars because we are analyzing the Social Analytics space for our own marketing purposes. We’re finding almost 10,000 articles and blogs a day specifically talking about Social Marketing and Analytics.

    Please get in touch. I think we have a lot to talk about.

  3. Sentiment analysis is a challenge. I am always very interested to hear what others are doing for tracking and analyzing blog content.

  4. It was a subtle but profound moment i realised that a word was not a number, and switched my focus to part of speech tagging. A wren and martin and a simple calculator is all I now need to make sense of the chaos out there. NLP is definitely far more efficient, especially in reading through text.

  5. We’ve thought about adding a simple sentiment analysis feature to our social business analytics app (http://analytics.thesocialbusiness.com) which uses the number of people who like / unlike a page to determine whether there’s a positive or negative trend. It’s not text analytics for sure, but it’s an easy way to get a basic thumb-in-the-air perspective of fan sentiment.

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