The Truth About Sentiment & Natural Language Processing By Synthesio
Summary Introduction .2 Artificial Intelligence’s difficulties with sentiment .3 Human analysis is an obligatory step when analyzing web content .5 Current technological advances .5 The future of semantic technology .8 .7.Conclusion .10 Synthesio – The Truth About Natural Language Processing -‐ March 2011
The web has made it possible for brands to discover what people are saying about their brands online, either in mainstream media like online newspapers and magazines, or on social media. Consumers now search for opinions online before, during, and after a purchase. The next step for brands is finding out whether people are talking positively or negatively about their brand, and why. Some online ratings provide a number but not the reasoning behind it, and may only present half of the story. Numerous companies have been working on text mining for close to 30 years in some cases, thus sentiment analysis is not a new area but it has become a hot topic thanks to social media. Social media monitoring companies, as well as PR practitioners, and digital marketers in general, have waged debate over whether sentiment should be analyzed by man or machine. Synthesio currently uses human analysts for sentiment analysis but can add natural language processing capacities on a case-‐by-‐case basis. Although technology is quickly advancing to catch up on its lag behind human analysis, as we advance toward what is referred to as the singularity, it seems as though the best option is currently combining both machine and man. Synthesio – The Truth About Natural Language Processing -‐ March 2011
Artificial Intelligence’s difficulty with sentiment One way that researchers have attempted to classify sentiment is by creating a “sentiment lexicon” Sentiment is not analyzed via artificial intelligence, as some people may be tempted to think. Rather, it is analyzed via a systematic process that involves the use of a sentiment lexicon. This lexicon assigns a degree of positivity or negativity to a word by itself that is then used to give meaning to the entirety of the article. This is a way of analyzing sentiment, then, by considering a type of inherent positivity or negativity of each word that would be used by someone to talk about your business or products. For example, “happy” would be deemed a positive word, as well as “like” and “love”. At the opposite end of the spectrum we can see words like “hate”, “dislike”, etc. There are two problems with this methodology, however. The first problem is that this assigning of positive and negative sentiment evaluates a word without the context of what is around it. The dictionary is extremely limited in the number of words that will always attach a positive or negative sentiment to an expression. The second problem is that researchers may assign different degrees of positivity to a word. Particularly in the case of ambiguous expressions, a researcher may be more inclined to note a word as more or less positive.
Text categorization classifies articles by topic1 Text categor