How Natural Language Processing (NLP) Aids Sentiment Analysis?

With social networks, users today possess all kinds of facilities to show their opinions on any topic they want. Being aware of opinions regarding a brand or a product and measuring its impact is currently of vital importance for all companies since their image is at stake.

Sentiment Analysis is considered a major metric to analyze the online reputation of brands, their products, services, and offerings. Data analysts use this phenomenon to extract information for the following purposes:

  • Brand monitoring
  • Market research
  • Business online reputation
  • Know customer’s intent & opinion
  • Understand customer’s reaction

“4.4 Million Google Searches, 294 Billion Emails, 54 Million Whatsapp Messages are sent in just 60 seconds on the web, which is impressive and surprising at the same moment.”

Source: smartinsights

Natural language processing has been widely adopted by businesses today for sentiment analysis to extract deep insights into the intent of the customers and is nonetheless, considered the best way to understand the language used and uncover the sentiment behind it.

Sentiment Analysis: A Brief

Sentiment analysis refers to the different methods of computational linguistics that help identify and extract subjective information from existing content in the digital world (social networks, forums, websites, etc.).

Thanks to sentiment analysis, we can be able to extract tangible and direct value, such as determining whether a text taken from the Internet has positive or negative connotations.

Sentiment analysis, also known as opinion mining, is a task of massive classification of documents automatically, which focuses on cataloging documents according to the positive or negative connotation of the language used in it.

With opinion mining, companies get immediate availability of the desired information where it not only allows us to answer what internet users think about a product or a brand but also facilitates, through the appropriate means, procuring competitive advantages in different areas.

“By sentiment analysis or opinion mining, we can gather enough information to know what users (or target) think or think on the Internet.”

What is Sentiment Analysis for?

  • Obtaining quality data
  • Avoiding a multitude of data that adds no value to the decision-making
  • Facilitating online reputation management of brands and businesses
  • Developing better business strategies
  • Making effective decisions in real-time to ease online reputation crisis and challenges
  • Knowing what actions to take in online marketing strategic plan

Natural Language Processing (NLP)

In general terms, masses consider sentiments to possess two significant values of positive and negative whereas, in reality, the emotions shown by the end-users prevail in rich data sets that tag consumer choices in different scenarios and influence their decisions.

Targeting user emotions is the pivot to drive a business towards the track of success where NLP has a crucial role to contribute to the goodwill maintenance of brands.

Sentiment Analysis is based on the working of Natural Language Processing combined with speech analysis, social media monitoring, or social media listening and social media analytics which helps enterprises to understand customer reactions and enables them to respond accordingly being strategically ready and rigid.

Say, a customer approaches you with a problem encountered with a product or service availed from you, an NLP system embedded within your systems would recognize the type of emotion expressed and would mark it for a quick automatic reply or will forward the case to the right concerned person.

This is how it makes speech analysis simple and trouble-free.

Basic Understanding of Data Processing

The amount of data currently being generated in companies is growing and expanding at an exponential rate and obtaining useful and valuable information from them is a very important competitive advantage over the competitors.

Generally, the structure used for proper organization of data is Binary Trees through which the three behavior patterns (positive, negative, or neutral) can be established. With this structure, behaviors are scrutinized and when a significant amount of data is collected the algorithm offers a percentage of possibilities to predict one behavior or another.

The following data processing steps are performed:

-Data Structuring
First, keywords are used to discard unwanted content, and then words are established to obtain categories according to their polarity or origin.

-Content Extraction
Once the filter is passed, the undesired content/information is removed and the emphasis is then made to start working on the quality of content

-Content Analysis
This process can be performed by the algorithm or by a natural person where the useful and quality content is framed in the category that corresponds to it.

-Content Cleaning
Perhaps, the content has been mistakenly sneaked in and now is the time to submit it to its correct category or drop it directly

All possible aspects of improvement are overseen in this section. Perhaps if a new word is found to include to discard content or if its realized that a word considered positive is used negatively at certain points, all related scenarios are reviewed and assessed accordingly.

Natural Language Processing Methods used in Sentiment Analysis

Sentiment Analysis predicts appropriate and useful data results from various NLP methods and algorithms which are bifurcated into the following:

-Rule-Based Systems
Algorithms executed which are based on a set of manually crafted rules

-Automatic Systems
Include machine learning algorithms and techniques that learn from data and output assessed data

-Hybrid Systems
Combines the approaches of both rule-based and automatic systems to perform sentiment analysis

These are the systems that are powered by Artificial Intelligence and Machine Learning principles that analyze, monitor, and assess data to draw conclusions and make predictions over the views and opinions expressed by the end customer.

There are many software tools supported by these robust technologies which make complex tasks extremely easy and fast with their ability to monitor in real-time and handle procedures for data supervision.

Now let’s explore these systems one by one and get familiar with their functionality:

# Rule-Based Systems

Such systems are made to work on the set of human-created rules that are proficient in identifying the subject, context, and polarity of an opinion or belief.

Some techniques involved in its working are:

These systems are provided with two listicles containing positive and negative words and they are made to count the number of positive-negative words that appear in a given text after analyzing the words deeply and differentiate them as per the defined list provided.

If the number of words that match from the positive words list exceeds that of the negative words listicle, the system retrieves positive sentiment and returns the same, following the same for the other side scenario.

# Automatic Systems

Automatic systems unlike the rule-based systems work on a technicality and are bolstered by machine learning techniques and principles. Sentiment analysis is processed using a classification model where the classifier is fed with the text which examines and outputs the result as positive, negative, or neutral.

Classification Algorithms are generally based on Statistical Models of:

  • Naive Bayes
  • A group of probabilistic programs use Bayes’s Theorem and predicts the text output
  • Linear Regression
  • The statistical algorithm works on the X-Y feature sets where they predict a value Y for a given data set of X
  • Deep Learning
  • The diverse set of programs that employ artificial neural networks to mimic human intelligence to process the given data.

# Hybrid Systems

Hybrid systems, compared to both of the above-mentioned, are more powerful and accurate when delivering the final results as such systems combine the desirable elements of automatic and rule-based techniques into hybrid systems.

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