An industry spawned from tweets

Your 140-character messages have become fodder for brand marketers

UCAS Vandenberg, chief executive of Los Angelesbased social media agency Fifty & Five, and his staff sift through thousands of tweets each day, eavesdropping on any mention of their clients’ brands or products.

The agency operates Twitter accounts for many of those brands. Some of them may ask Fifty & Five to respond to complaints it comes upon, or to locate negative tweets about competitors and present the client’s brand as a better alternative.

But wading through all that online conversation can be challenging for a small firm like Fifty & Five. So it is turning to an emerging industry of companies that help businesses such as Vandenberg’s churn through thousands of Facebook and Twitter posts to better understand what customers really think. Some rely on linguistic algorithms, while others try to blend in a bit of psychology and more.

Fifty & Five uses natural-language processing software to sort tweets. The presence of words such as “good” and “love”, along with a brand name, might throw a mention into the positive bucket, and “hate” or “bad” into the negative. Vandenberg favours a service from San Francisco- based public relations software firm Meltwater, although other tech firms, including SAP and Salesforce, offer similar technology.

But purely linguistic algorithms can’t always understand context, Vandenberg said. The system he uses is trained to look for the word “virus” because one of his clients sells antivirus software, but the results can include tweets in which people mention infections that have nothing to do with the computer sort.

Natural-language processors can also be confused by slang or sarcasm – if a user calls a product “sick” or “bad” they could actually mean “good”, he said, explaining that he and his staff can manually tweak Meltwater’s algorithm to recognise terms such as “sick” as positives.

To compensate for stumbling blocks in language processing, a psycho-linguistic approach is emerging, pairing linguistic algorithms with psychological principles.

David Sackin, analytics director at ad agency BBDO, uses software called Decooda, which categorises

Ltweets as positive, negative or neutral but backs the classification with its own psychological research – for instance, what kinds of tweets, Facebook posts or blogs do people create when frustrated or disappointed?

When reflecting on a product they like, users commonly use the word “hate” to express frustrations with rival products.

Atlanta- based Decooda’s algorithm gives added weight to the frequency of certain words and their likelihood, according to psychological research, to indicate positive or negative brand sentiment.

Sackin said BBDO considers such sentiment one element of a larger picture of consumer behaviour. The firm uses it in conjunction with many other software platforms, such as measuring Facebook shares, ad views, or clicks.

IBM, meanwhile, has been developing a psycho-linguistic product analysing individual Twitter users’ personalities.

Using “the big five personality traits” – a common psychological paradigm scoring a subject’s openness, conscientiousness, extroversion, agreeableness and neuroticism – the software creates a portrait based on a user’s tweet history, noting characteristics such as positive or negative outlooks, self-consciousness, or if they’re “open to experience”, among other characteristics.

IBM is betting brands will use this information to tailor their customer service to the personality of individual customers.

For instance, if a generally positive Twitter user expresses extreme anger toward a brand, that complaint could be more important to the brand than one from someone who complains about everything, said IBM researcher Michelle Zhou.

IBM is basing its model largely on text submitted to its psychology team by hundreds of thousands of volunteer subjects.

“Knowing a person’s emotional stability or (tendency) toward negative emotion, the business entity has a better handle on how to interact with this person,” Zhou said.

“If this person has a high level of self-consciousness or a high level of vulnerability, you may want to start (a response) with ‘don’t worry about it’, to give a sense of security to this person.” – Washington Post