There are many situations when the information you have about your customers is post-sentiment. By this I mean that you already have information, such as a Net Promoter Score (NPS) or rating scale score, that gives you a clear idea of how the consumer feels about a product or service. In this case sentiment analysis of your text data isn't needed, you have that information already. Very often you also have a comment or review that can help explain the ratings, the key is now working out what the review or comment tells you. If you have a few hundred text comments you can read them all. Once you get into the thousands of comments it gets hard to do that, and it can get expensive to have them manually coded.
An answer to this problem is Automatic coding (autocoding). As the name implies it is the matching of predefined words or phrases to consumer comments or reviews. Traditional human based coding efforts have their limits in terms of the number of codes used and the length of the lists of words or phrases that can be used to identify the code. Autocoding can search for 100's or 1000's of word or phrases, of any length, within your text data. Long lists of product names, key phrases and idioms can be captured this way, something no human coder can do. Advances in technology make autocoding feasible for text data sets with 100,000's or millions of entries. This is the same technology that allows millions of hashtags to be detected in social media in real time. A lot of text analytics begins with autocoding by extracting key words or hashtags from text. Another capability of the autocoding process is to extract frequently occurring phrases, such as "blueberries were good" from the text data and build a list of possible phrases to be coded. This is not something humans are very good at doing.
It is better to have concrete information about all of your data by using autocoding than sample a small part of your text data for human coding and hoping you get a representative sample. Autocoding results can be the basis for selecting text data for human coding. If you have no information on your text data, sampling that text data is reliant on random sampling. Knowing more about your data allows for more structured sampling and accuracy if you want to use human coding.
Using autocoding means you can analyze all the text data you collect. Not only that, autocoding is less expensive than traditional human coding.
What is the point of collecting text data if you don't analyze all of it ? Go Auto.
For details on how Mass Cognition can help you make the most of your text data visit www.masscognition.com