What is Sentiment Analysis?
First, we need to know what sentiment analysis means. According to the Oxford dictionary, sentiment analysis refers to the process of computationally identifying and categorising opinions expressed in a piece of text, especially in order to determine whether the writer’s attitude towards a particular topic, product, etc. is positive, negative, or neutral.
In order words, it is the process we determine the emotional tone behind a series of words and gain an understanding of the writer’s view and attitude behind the limited expression.
The use of Sentiment Analysis
Undoubtedly, sentiment analysis is widely used nowadays. It allows us to gain the opinion of the public over certain topics. We can get the answer very quickly. One of the most common examples is Facebook.
As shown in the above figure, we can immediately react to the topic and show our feelings.
Is sentiment analysis always reliable?
Human language is complex. Sometimes we humans interpret the wrong meaning in both spoken and written language, teaching a machine to interpret the tone behind words is even more difficult.
“Anyone who says they’re getting better than 70% [sentiment accuracy] is lying, generally speaking” ( Source:http://www.theguardian.com/news/datablog/2013/jun/10/social-media-analytics-sentiment-analysis
I guess 50-60% of accuracy won’t be convincing when you are making some important business decisions. The result can be disastrous if we are making decision based on inaccurate sentiment analysis.
So, we should be aware and understand the methods the social media ventor is using. There are many methods and here I would like to discuss two of them.
- Keyword Processing
In this method, words are categorized as ‘positive’ or ‘negative’. Then, it determines the overall percentage of positive or negative words in a passage.
This method is fast and cheap to implement. However, it may not be useful when dealing with double positives or doule negatives.
- Natural Language Processing (NLP)
NLP is another method that makes the computer understand natural language input and generate natural language output. In order words, computers can get the meaning of human words, as it understands that several words form a phrase, several phrases form a sentence, and sentences express ideas.
Though NLP seems reliable, it still has limitations. For example, it cannot interpret sarcasm and acronyms. Consider the sentence: “He has no enemies, but is intensely disliked by his friends.” We can understand “friends” do not mean a true friend here but the computer will not inerpret the same meaning.
While sentiment analysis becomes more popular nowadays. As an analyst, we should choose a suitable method carefully so that the result can be more comprehensive and reliable. As a user, we should choose a vendor that treats sentiment analysis seriously and is capable to update their technology regularly.