Sentiment Analysis based on Appraisal Theory and Functional Local ...

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CHAPTER 1
INTRODUCTION
Many traditional data mining tasks in natural language processing focus on
extracting data from documents and mining it according to topic. In recent years,
the natural language community has recognized the value in analyzing opinions and
emotions expressed in free text. <strong>Sentiment</strong> analysis is the task of having computers
automatically extract and understand the opinions in a text.
<strong>Sentiment</strong> analysis has become a growing field for commercial applications,
with at least a dozen companies offering products and services for sentiment analysis,
with very different sets of goals and capabilities. Some companies (like tweetfeel.com
and socialmention.com) are focused on searching particular social media to find to
find posts about a particular query and categorizing the posts as positive or negative.
Other companies (like Attensity and Lexalytics) have more sophisticated offerings
that recognize opinions and the entities that those opinions are about. The Attensity
Group [10] lays out a number of important dimensions of sentiment analysis that their
offering covers, among them identifying opinions in text, identifying the “voice” of
the opinions, discovering the specific topics that a corporate client will be interested
in singling out related to their brand or product, identifying current trends, and
predicting future trends.
Early applications of sentiment analysis focused on classifying movie reviews or
product reviews as positive or negative or identifying positive and negative sentences,
but many recent applications involve opinion mining in ways that require a more
detailed analysis of the sentiment expressed in texts.
One such application is to
use opinion mining to determine areas of a product that need to be improved by
summarizing product reviews to see what parts of the product are generally considered
good or bad by users.
Another application requiring a more detailed analysis of