An introduction to sentiment analysis

17

October

2018

No ratings yet.

This digitalized world is ideal for user generated content, which is basically any content that has been created and put out there by users of online platforms. Examples are images, tweets, blogposts, videos, reviews or comments. Although the content different in length or quality, it would be a waste not to use this free content in order to know your customers better. Conducting sentiment analysis can be very useful when monitoring social media to gain a better understanding about the wider public opinion behind certain topics. This ability to extract insights from social data is very practical and that practice is widely adopted.

 

What is sentiment analysis?

Sentiment analysis, also known as Opinion mining, is an automated process of understanding an opinion about a given subject from a text.

 

How to conduct sentiment analysis?

One possible method is a lexicon-based sentiment analysis. First, you need to determine the dimensions. These are aspects of a domain that are relevant to the consumers. For instance, the domain is ‘movies’, then relevant dimensions could ‘storyline’ or ‘acting’. Second, you need a training text. This is a body of text that is specific to a single dimension of the focal domain. This could be a textbook on acting. By removing stopwords from the training text, you have a list of unique words and the number of occurrences of it, for which you can then compute the likelihood of that particular word being about acting.

This gives the probability of the word occurring, given each hypothesis. Wk stands for each word in the vocabulary. Nk stands for the number word occurrence in the text. N is the number of unique words selected from your training text and vocabulary is the sum of all occurrences from the training text.

 

Then, the posterior probability is computed with Naïve Bayes through the following steps:

 

P(h): prior probability of h; the probability that some hypothesis h is true.  h is in this case the dimension.

P(h l D): Posterior Probability of h;the probability of h, given some Data. (lexicon words)

P(D l h): Which is the probability that some data value holds given the hypothesis.

P(D): Is the probability that some training data will be observed (Zacharski et al., 2015).

(For an example see https://www.youtube.com/watch?v=doznOnG81xY&t=82s)

So, when analysing a user generated text, the P(h l D) is updated each time a word is found that occurs in your lexicon.

 

The same method is applied to measure sentiment. You need a lexicon on positive words and negative words, remove the stopwords, compute the likelihood and compute the posterior probability.

 

Limitations

This method comes with some challenges. Such as sarcasm, which is difficult to detected in user generated texts. Also those text can consist of slang or dialect, which makes it difficult to analyse the text. I am curious to find out more about this. So, what other methods do you know of and which one do you think is most accurate?

 

Sources

Zacharski et al. (2015). Chapter 6 Naïve Bayes. Retrieved from http://guidetodatamining.com/chapter6/

Zacharski et al. (2015). Chapter 7 Classifying unstructured text. Retrieved from http://guidetodatamining.com/chapter7/

 

 

 

Please rate this

The Heart Of A Machine

16

September

2018

No ratings yet.

Lack of emotions

Replicating human intelligence in computers would be the epitome of AI. However, they currently lack one important aspect of human intelligence; Emotional Intelligence. Some argue that this draws a line between humans and AI, which is why they think some human tasks can never be replaced by robots. For instance, in providing bed side manner or customer service. One would prefer a sympathetic person rather than a cold hearted robot. Nonetheless, Artificial Emotional Intelligence is gaining ground, but I see it as a double edged sword.

 

Advantages of Artificial Emotional Intelligence

Sentiment analysis can help machines understand human emotions so that they can adapt their behavior accordingly. However, research has shown that body language conveys 55% of the value of your message and 38% is delivered in the intonation of your voice. Only 7% is captured in the actual choice of words. So, emotions are largely conveyed non-verbally.

 

Affectiva is one of the growing businesses in this field. Their Emotion AI Technology is already used to judge the emotional effect of an advertisement by analyze facial images of the person to classify them according to an emotional state. This technology could also be applied in autonomous cars so that they can assess the condition of the passengers and act accordingly. Such as detecting lack of attention and tiredness. Furthermore, combining AI with Emotional Intelligence is beneficial for improving customer service as AI recognize and respond more effectively to emotional responses, improving customer experience.

 

Risks of Artificial Emotional Intelligence

While I do see the advantages of this technology, I wonder whether or not it will stab us in the back. First of all, there is a matter of privacy. Emotions are private affairs and therefore protective legislation are needed to deal with the collection, storage and use of this information about ones’ emotional state. Secondly, emotional manipulation. By understanding human emotions, it can also manipulate a persons’ state of mind for the wrong intentions or worsening ones’ emotional state. And thirdly, what if they start to experience emotions too? How will we remain in control?

 

What do you think the future of Aritificial Emotional Intelligence holds for us?

 

 

Resources

Joshi, N. (2018). Artificial emotional intelligence: the future of AI. Retrieved from https://www.experfy.com/blog/artificial-emotional-intelligence-the-future-of-ai

Kleber, S. (2018). 3 Ways AI is getting more emotional. Retrieved from https://hbr.org/2018/07/3-ways-ai-is-getting-more-emotional

Beck, M. & Libert, B. (2017). The rise of AI makes emotional intelligence more important. Retrieved from https://hbr.org/2017/02/the-rise-of-ai-makes-emotional-intelligence-more-important

Yonck, R. (2018). The coming era of emotional machines. Retrieved from https://www.psychologytoday.com/intl/blog/the-intelligence-report/201802/the-coming-era-emotional-machines

Zurcher, A. (2014). The danger of emotional machines. Retrieved from https://www.bbc.com/news/blogs-echochambers-28005330

 

Please rate this