The human-machine dialogue has been one of the most active areas of research for decades. In recent years, great progress has been made in the field of NLP (Natural Language Processing), although, due to the complexity of human language, the machine is not yet able to understand perfectly.



What is NLP

Natural Language Processing, known as NLP, is that branch of Artificial Intelligence that makes it possible for the computer to understand a written text or sentences spoken aloud, so that it can return an output.

Understanding human language is a complex action that includes not only the meaning of individual words, but also the logical and semantic meaning of the sentence. In other words, for natural language to be comprehensible to the machine it is necessary to take into account phonetics, syntax, semantics, phonology, morphology, pragmatics and the entire discourse in which the words are found.


The 3 NLP tasks to consider

For the computer to be able to understand a speech as a whole, there are three main tasks to consider:


    • Semantic Role Labeling: labels the words of a sentence according to their semantic role, such as “subject” and “verb”. This type of analysis does not understand the meaning of the sentence or individual words, but focuses on the role that the individual elements have within the speech.
    • Word Sensing Disambiguation: has the task of associating a word with its contextual meaning. Depending on the context, in fact, a word can change its meaning, which is why it is essential to give the machine the analysis tools necessary to understand the meaning relating to a given circumstance.
    • Semantic Parsing: converts a text into a logical representation to trace the meaning of a sentence.


    NLP applications

    The uses of Natural Language Processing are many and varied. Some have now become part of our daily life, such as virtual assistants (Alexa, Siri, Google, Cortana). Others are less known, but of undisputed utility, such as Automatic Summarization which automatically produces a summary of one or more textual documents. It can be used for the extraction of information from governance documents (reports, procedures), administrative documents (invoices, contracts).

    Let’s see other examples:


      • Chatbot: automatic chats that respond with appropriate actions according to the user’s request. They are widely used to automate customer service to answer questions and help people who need information about services, problems, products, etc. in real time.
      • Intent Monitoring: has interesting implications in marketing: includes text in order to predict future behaviors, such as a customer’s willingness to buy.
      • Sentiment Analysis: analyzes the text to understand the sentiment (positive, negative and neutral) about a topic.


      Sentiment Analysis: what it is and what are the advantages

      As mentioned in the previous paragraph, Sentiment Analysis is that branch of Artificial Intelligence in charge of analyzing textual data to detect positive, negative and neutral sentiment or opinion. It is often used in marketing to monitor qualitative feedback towards brands, products and services. It is also useful in the field of customer care, in fact, it gives the possibility to identify negative customer comments faster and respond as soon as possible, preventing discontent from growing and damaging the reputation of the brand.

      In short, the applications are different and in the future we will surely hear more and more about Sentiment Analysis. This is because, regardless of the use we can make of it, it has three major advantages:

      • Order unstructured data at scale
      • Analyze using consistent criteria
      • The analyzes are in real time

      The aforementioned elements meet important business needs. In fact, organizations accumulate a huge amount of unstructured data that would require an excessive cost, both economically and in time, to be analyzed manually. Furthermore, if it were human resources to deal with it, each person would use different criteria to define a “positive” or “negative” text, leading to an analysis based on inconsistent and therefore not very useful criteria. Another need is to have a real-time view of the sentiment of the public concerned, in order to be able to act promptly.


      There is no doubt that Sentiment Analysis in NLP development will bring enormous benefits to public and private organizations.


      The experience of Drive2Data

      Drive2Data, an expert in data refining, has carried out several studies on Sentiment Analysis processes. After a first experience using tokenizations, lemmature and Machine Learning, today we are experimenting with the application of Natural Language Processing to make improvements to the Sentiment Analysis solution.

      Leveraging NLP engines to perform sentiment analysis on text gives more intelligent solutions with more accuracy within less amount of time. NLP methods like Lemmatization and Stemming helps better in analyzing the words and sentences to predict the sentiment of the sentences by measuring their polarity scores. Utilizing deep learning models like Transformers, GPT’s can be more beneficial for sentiment analysis.

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