Where can I find sample code for the API?
We’ve got sample code that’ll help you get started quickly.
Click here to check out the sample code.
What’s the relationship between LIWC and Receptiviti?
The Linguistic Inquiry and Word Count (LIWC) is the gold standard in language-psychology based text analysis. LIWC has been continuously developed through more than 20 years of academic and applied research by Receptiviti’s co-founder and Chief Science Officer, James W. Pennebaker.
In 2015, James released an entirely new, greatly improved and more accurate version called LIWC2015, which accommodates numbers, punctuation, short phrases, netspeak (language common on Twitter and Facebook), as well as SMS, text messaging, emoticons, and SMSlike modes of communication (e.g., Snapchat, instant messaging).
Receptiviti was launched in conjunction with LIWC2015 and established as its commercial side to make LIWC more accessible to the software development and data science communities, and the growing number of businesses that want to incorporate its capabilities into their technologies. LIWC is made available for academic research purposes at LIWC.net. All non-academic users are required to use LIWC through Receptiviti API. This API provides technical users (such as software developers and data scientists) with the means to integrate Receptiviti’s capabilities into their technologies to conduct programmatic analyses of very large data sets.
How many words are needed for valid results?
The more words that you analyze, the more trustworthy are the results. A text of 10,000 words yields far more reliable results than one of 100 words. For the LIWC scores that are generated by Receptiviti API, we recommend a minimum of 50 words. For the personality scores generated by Receptiviti API, we recommend a minimum of 300 words. If your language sample is smaller than the recommended minimum word count, be careful in interpreting results.
Does Receptiviti make mistakes in categorizing personality and language? Just how precise is it?
Like all text analysis tools, Receptiviti can make errors in identifying and counting individual words, especially words in isolation. Consider the word mad, which is counted in the Anger, Negative Emotion, and Overall Affect dictionaries. Usually, mad does reflect anger. Sometimes it expresses joy (he’s mad for her) and mental instability (mad as a hatter). Fortunately, this is seldom a problem because Receptiviti takes advantage of probablistic models of language use. Yes, in a given sentence, the word mad might be used to express positive emotion. However, if the author is expressing a positive state of affairs, they will generally tend to use relatively high rates of other positive emotion words and few anger words. Small classification errors like this rarely impact the conclusions that can be drawn from the results because they are offset by the way that words are most commonly used by people.
Just as individual words may be misclassified, Receptiviti also does not understand irony, sarcasm, or metaphor. Again, it is all probabilistic. If someone is being mean spirited in their use of sarcasm, there is a good chance that Receptiviti will capture hostility in other word choices.
Does word use validly reflect people’s psychological states?
Let’s rephrase that: If a person is using a high rate of anger words, are they really angry? This is a tough question to answer directly. It also points to the importance of hundreds of scientific studies that have been conducted since the early 1990s.
There have indeed been several studies that find that when people report themselves as being angry they use more anger-related words. Analyses of speeches, writings and conversations show that people rate texts that are high in anger words as expressing higher rates of hostility. But is the speaker really angry? Is it possible that she or he is just pretending to be angry? This is a judgment call, and context matters. For example, if you’re analyzing the words of a Wikipedia page on “anger management”, the results likely have little to do with how angry the author was at the time of writing.