What is Sentiment Analysis?

Sentiment analysis is a method of studying customer sentiment by using natural language processing, statistics and text analysis. The most thriving businesses are those who understand the sentiment of their clients, that is, whatever people are saying and what they mean. Customer sentiment can be found pretty much everywhere: for instance, on social media, when people express their views, ideas and feelings on tweets and comments.

What is the purpose of Sentiment Analysis?

Sentiment Analysis purports to understand all these emotions with the help of software, and it’s one of the most important strategies for developers and business leaders as well. As with many other fields, advances in deep learning have brought sentiment analysis into the foreground of cutting-edge algorithms. Today we use natural language processing, statistics, and text analysis to extract, and identify the sentiment of words into positive, negative, or neutral categories.

How does sentiment analysis work?

Sentiment analysis is used to understand whether textual data (or data in general) shares a positive, neutral or negative meaning. It works through Natural Language Processing (NLP) and Machine Learning Algorithms. These algorithms are typically of three types:

Rule-based: a type of algorithm that automatically performs sentiment analysis according to a set of rules, which are manually crafted. These rules include NLP techniques such as tokenizations, stemming, parsing, part-of-speech tagging, lexicons and so on. In general, a rule-based algorithm first defines two lists of polarized words, for instance, negative and positive words. Then, it counts the number of positive and negative words that pop up in a given text. If the number of positive words surpasses the number of the negative ones, the system will return a positive sentiment. And vice versa. A neutral sentiment is returned if the numbers are even. Such a method is quite superficial, as it doesn’t take into account the complexity of word combination.

Automatic: a type of system that relies on machine learning techniques to learn from data.

Hybrid: this system combines both rule-based and automatic algorithms.

The word Sentiment

What does sentiment mean? Can it be split into clear categories such as bad, happy, sad, angry? Or is it dimensional and multi-faceted? It’s really hard to give a proper, all-encompassing definition to sentiment, because of its polyhedric nature.

We also have to take into account that people express opinions in several complex ways. We have rhetorical figures like irony, sarcasm and implied meaning which can be difficult to decipher by algorithms. This is the reason why sentiment analysis uses text analysis and natural language processing to understand in the most accurate way a particular text, considering its context

Challenges of Sentiment Analysis

It goes without saying that sentiment analysis is one of the most difficult techniques in natural language processing. We as humans cannot analyze sentiments as accurately as we would. And while we have made consistent progress thanks to data science, there’s still plenty of miles to go.

Texts can be subjective and objective. Unlike subjective texts, objective texts do not contain any explicit sentiment. Let’s give you an example of a text of which we want to make a sentiment analysis.

The sky is nice.

The sky is blue.

The majority of people would argue that the first sentence has a positive sentiment, while the second is neutral. In the example above, the word “nice” is more subjective than the adjective “blue”.

It’s very hard for an algorithm to make such a distinction.

Sentiment analysis also has to understand the specific context of words, especially when it comes to rhetorical speeches like irony and sarcasm. There are no textual clues to help a machine learn.

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