Opinion mining is a type of natural language processing for tracking the mood of the public
about a particular product. Opinion mining, which is also called sentiment analysis, involves
building a system to collect and examine opinions about the product made in blog posts,
comments, reviews or tweets.
Automated opinion mining often uses machine learning, a component of artificial intelligence
(AI).
Opinion mining can be useful in several ways. If you are in marketing, for example, it can
help you judge the success of an ad campaign or new product launch, determine which versions of a
product or service are popular and even identify which demographics like or dislike particular
features. For example, a review might be broadly positive about a digital camera,
but be specifically negative about how heavy it is. Being able to identify this kind of information
in a systematic way gives the vendor a much clearer picture of public opinion than surveys or focus
groups, because the data is created by the customer.
An opinion mining system is often built using software that is capable of extracting knowledge
from examples in a database
and incorporating new data to improve performance over time. The process can be as simple as
learning a list of positive and negative words, or as complicated as conducting deep parsing of the
data in order to understand the grammar and sentence structure used.
There are several challenges in opinion mining. The first is that a word that is considered to
be positive in one situation may be considered negative in another situation. Take the word "long"
for instance. If a customer said a laptop's battery
life was long, that would be a positive opinion. If the customer said that the laptop's
start-up time was long, however, that would be is a negative opinion. These differences mean that
an opinion system trained to gather opinions on one type of product or product feature may not
perform very well on another.
A second challenge is that people don't always express opinions the same way. Most traditional
text processing relies on the fact that small differences between two pieces of text don't change
the meaning very much. In opinion mining, however, "the movie was great" is very different
from "the movie was not great".
Finally, people can be contradictory in their statements. Most reviews will have both positive
and negative comments, which is somewhat manageable by analyzing sentences one at a time. However,
the more informal the medium (twitter or blogs for example), the more likely people are to combine
different opinions in the same sentence. For example: "the movie bombed even though the lead actor
rocked it" is easy for a human to understand, but more difficult for a computer to parse. Sometimes
even other people have difficulty understanding what someone thought based on a short piece of text
because it lacks context. For example, "That movie was as good as his last one" is entirely
dependent on what the person expressing the opinion thought of the previous film.
