Named entity recognition is key in sentiment analysis.
Sentiment analysis is the process carried out with the aim of recognizing the subjectivity of a text . The purpose is to distinguish the author's emotions from the content through a study of his textual production.
Sentiment analysis is usually understood as a technique of artificial intelligence . Using algorithms and using natural language processing (NLP), an attempt is made to identify and classify the opinion expressed in the message. The usual thing is to try to differentiate between a positive feeling, a neutral feeling and a negative feeling.
Features of sentiment analysis
Sentiment analysis involves an automatic classification of documents based on the connotation of the language that was used. A linguistic investigation is not carried out, but statistical associations and comparisons are made.
Sentiment analysis is often associated with text mining , which is an area of data mining . Data mining is known as the branch of computer science and statistics that aims to detect patterns in large amounts of data; In the specific case of text mining, the purpose is to obtain new information by examining the texts.
When carrying out a sentiment analysis (or opinion mining , as the concept is also mentioned), the aim is to know the contextual polarity of the text and the attitude of its author . In this way, you can know the emotional state of the creator of the text, the judgment he makes and his communicative intention .
Sentiment analysis aims to determine the basic emotions that are reflected in a text.
The polarity
Polarity refers, in this framework, to the attribution of a positive, neutral or negative characteristic to an entity (from a sentence to the entire text). Basic sentiment analysis consists precisely of determining whether the text in question reflects a favorable, neutral or unfavorable opinion on a certain issue .
A more advanced sentiment analysis can establish a scale of values (from 1 to 5, for example, with 1 being the lowest score and 5 the highest) or attributing certain emotions to the author (happiness, anger, etc.).
Sentiment analysis in e-commerce is very important.
Development of sentiment analysis
Sentiment analysis is developed through a series of procedures. Typically, keywords are located first and then the lexical affinity of the terms is examined.
By appealing to statistics and various sentiment analysis techniques and tools, it is thus possible to classify text into different categories, studying the affinity of words with various emotions and assigning scores.
It is interesting to mention that artificial neural networks can be trained through machine learning to recognize the nuances of opinions. If it is indicated in the training that thousands of opinions are "positive" and as many "negative" , the artificial intelligence is able to detect patterns in the reviews and learns; Thus, faced with new texts with which she has not been trained, she can recognize subjectivity .
It can be noted that sentiment analysis consists of examining the words of a text to, taking into account the lexicon of the content, know its subjectivity. Simplifying: if words like "boring" , "bad" and "detestable" appear in a movie review, the text is associated with a negative rating. On the other hand, if the text includes terms like “great,” “excellent,” and “wonderful,” the sentiment analysis will determine that there is a positive polarity.
Of course, for the analysis to be accurate, multiple variables must be included to help interpret the context and nuances. Detecting irony or sarcasm, for example, is not easy for a machine. The same thing happens with emojis or emoticons.
Your benefits
Nowadays, it is common for a consumer to make a purchasing decision based on the comments and ratings they find on the Internet. That is why sentiment analysis became a very valuable tool for companies.
An analysis of reviews following the parameters and criteria mentioned can be key for a company to know how its proposals are being received. Data is also important when designing virtual assistants or chatbots and establishing care models.
Sentiment analysis is used to obtain conclusions based on information, anticipate consumer behavior, correct strategies and automate tasks, to point out other possibilities.
Of course, sentiment analysis is also useful for evaluating the work done in marketing and communication. Examining the brand image and the strength of its discourse is a possibility offered by this resource.
Much of this work is carried out using social network analysis . Today , users and consumers express their opinions on Facebook , .