How Your Social Listening Strategy Will Benefit From Natural Language Processing
For marketers, social listening and sentiment analysis have become a part of our daily lives, and now they are beginning to integrate Natural Language Processing to their analyses. Brands are now turning to social listening for crisis management, lead generation, customer service, real-time marketing and more. But do you really know how your social listening tool is finding and classifying conversations about your brand? There’s a lot going on behind the scenes to help you with your social media strategy.
From my personal experience, social listening has become the holy grail for marketing and PR – but how does it work? I’m certainly no expert when it comes to the science behind Natural Language Processing and Sentiment Analysis, however, that is exactly why I’m writing this post – chances are, you’re not a computer sciences expert either. It’s important to understand what’s going on behind the scenes with your monitoring solution, to make an educated decision on which tool to go with based on your needs – and to fully utilize and leverage the power of your listening technology.
What is Natural Language Processing?
NLP (Natural Language Processing) refers to computer systems that process language in terms of its meaning and analyzes language patterns to understand social media conversations. NLP offers valuable insights into tracking sentiment — the tone of a social media post, tweet, LinkedIn update, etc — and tags that post as positive, negative or neutral.
Thanks to our talented and tireless NLP team, we have developed a unique Natural Language Processing system, that works for a variety of different languages – combining machine learning information with hand improved data. Here’s a high-level overview of what’s going on behind the scenes with Synthesio’s Natural Language Processing System.
Automated Sentiment Analysis
At Synthesio, our technology automatically builds lists of key terms in native languages, so we have the colloquial terms unique to each language. Our Automated Sentiment Analysis (ASA) weighs each term based on how charged the word is — really, this means the strength of the negativity or positivity. Our system will then find those key terms in each online mention, and weigh the key terms against each other in order to get the average sentiment for the mention.
To make this process scalable, our dictionaries automatically improve using manually coded data samples in each language and industry.
To date, Synthesio can offer auto-sentiment analysis in 12 languages including English, German, Spanish, French, Polish, Portuguese, Dutch, Chinese, Japanese, Russian, Italian and Turkish – making it very simple for global brands to gain valuable actionable insights into their consumer base – that may be implemented into PR, Marketing, Advertising, Sales and Customer Service efforts.
Machine Learning Word Classification
Synthesio’s machine learning algorithms learn how humans classify data – and copy it. Once enough data (social media verbatim) is manually tagged, we can activate the system and start classifying verbatim – and our machine learning algorithm copies our classification process – and begins classifying automatically. The benefit of auto-text analysis is that computers can analyze and classify much more data at a much quicker rate than humans and sometimes with better accuracy! So, basically, the technology is learning how to be smarter and more efficient than humans… this may seem a little scary, but this system is saving you a ton of work – if you’re a large company, you’re probably seeing hundreds of social media conversations happening around your brand every day.
Thanks to our NLP team, we now have word clouds that seek out and display trends in negative, positive and neutral words and phrases associated with a specific brand or term. Advanced word clouds simplify the detection of insights among very high volumes of data.
This NLP word cloud displays the positive, negative and neutral terms associated with a new Microsoft TV ad – highlighting areas of concern, and trends in language around the ad. By simply clicking on each word, you can drill down and gain context by viewing the posts associated with each word.
Among many other things, NLP gives you the opportunity to seek out the negative mentions of your brand, to prioritize your engagement and manage your reputation – but also find negative mentions of your competitors, too, for competitive intel and lead generation. For reporting purposes and market research – you can get a high-level global snapshot of the sentiment around your marketing campaigns, and drill down for more detail and context.
No social media monitoring vendor would dare say NLP and sentiment analysis is 100% accurate – however, the industry is changing and growing at a rapid pace – we may need an additional human touch for 100% accuracy for the time being – but as an industry, we are well on our way to more sophistication and accuracy in the near future.
More more information on Natural Language Processing – check out our White Paper below – The Truth About Sentiment Analysis and Natural Language Processing or sign up for the Text and Social Analytics Summit 2013 in Boston (June 5-6), and learn about the latest in NLP, from text analytics gurus – Seth Grimes and Tom Anderson