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Sentiment Analysis Empowering Businesses

According to Buddhist principles, every person has a “monkey mind” referring to being confused and unsettled in decision-making. This makes people compare brands before selecting their product. Sentimental Analysis assists in understanding the monkey brains which identify the opinions on our brand and provides us with a solid understanding of our business. This blog provides insight into the overview of Sentimental Analysis, how does it process people’s expressions and emotions, the algorithm and software types and how significant its role is in a business and brand marketing.

What is Sentimental Analysis?

Sentiment analysis is a text-based process that identifies the positive or negative opinion within a sentence, paragraph or complete document. By applying Natural Language Processing (NLP) and text analysis techniques we analyse unstructured data and extract significant information from a sentence. It is transformed into effective business intelligence. This helps in analysing and measuring business emotions to convert them into factual data. The converted data allow us to categorise expressions as positive, negative or neutral.

Process of Sentiment Analysis

It uses rule-based, automatic and hybrid methods and algorithms. The automatic approaches use machine learning techniques. Whereas, the hybrid approaches offer more power by combining elements of the rule-based and automatic approaches. The rule-based approach assists in identifying the polarity and the subject of opinion. It employs techniques such as:

  • Stemming, tokenisation, part-of-speech tagging and parsing
  • Lexicons (i.e. list of words and expressions)

The first step in the process is to collect the customers’ public posts across the main social media platforms that reference the business’s products or services. These are then analysed using a feature extractor with the results fed into a machine learning (ML) algorithm. The ML text classifier transforms the extracted text into a “bag of words” and “n-grams” with their associated frequencies. The n-grams are then classified by a statistical model that produces customer insight and predictions.

Types of Classification Algorithms

Naïve Bayes

Naïve Bayes is the simplest and commonly used classification model. It computes the posterior probability based on the distribution of words in a document. These probabilities occur by using Bayes Theorem. This explains the probability of a feature, based on its prior conditions. The basic model of Naïve Bayes is improved by removing stop words, lemmatizing words, and using n-grams to work as an advanced method.

Linear Regression

Linear Regression is one of the strongest tools in machine learning and statistics to predict the value from a continuous range rather than classifying them into various categories. The sample data of linear regression comes from a set that follows a probability distribution based on a fixed set of parameters. The objective of linear regression is to predict the value by consequently reducing the errors. This inversely increases our prediction.

Support Vector Machines

It is a supervised machine learning model that uses a non-probabilistic algorithm to categorise the text based on the similarities within it. Each data item is created as a point on an n-dimensional space. The value of each feature becomes the value of a specific co-ordinate. By using the hyperplane, we classify the text into two categories depending on the similarities.

Deep Learning Services

Deep learning development is a diverse set of algorithms simulating a human brain by applying neural networks to process data. It runs the data through several layers of neural networks to create a simplified representation of data to the next layer.

Text Classification

The accurate classifiers involve identifying subjective and objective pieces of text and analysing their tone. The text without a context is analysed by using pre-process or post-process techniques. Sometimes a negative response can be expressed using positive words, as occurs with sarcasm. Algorithms such as MapReduce can be used to detect sarcasm. The commonly used emojis and Unicode characters can also be pre-processed to improve analysis results. We can define neutral text by classifying it into objective text, irrelevant information or text containing wishes.

Types of Software in Sentiment Analysis

Text Processing – It performs word grouping (“lemmatisation”), word stemming, parts of speech tagging and chunking, phrase extraction, date extraction, location and named entity recognition, and more.

Tweet Sentiments – Twitter is a commonly used platform for customers to express opinions on products. Twitter Sentiment Analysis analyse both new and existing tweets to extract the emotions one tweet at a time.

ML Analyser – This software uses machine learning development to perform text classification, article summarisation, stock symbol extraction, and name, location and language detection.

Sentiment Analysis in Business

Sentiment analysis helps a business by identifying the attitudes, emotions, and opinions of its customers about its products, services and brand. This is achieved by analysing social networking sites and other digital media forums where people are commenting on its products and services. Sentiment analysis identifies the most significant expressions and feelings of customers that could have the greatest impact on the business and its brand. It helps a business by listening to the customers’ emotions from survey responses, social media conversations and more. It can then customise its offerings to meet customers’ expectations in terms of pricing plans, ease of access, customer service, etc.

Role of Sentiment Analysis in Brand Marketing

Sentiment analysis is used to gain valuable insights from customers not easily achieved by other means. It is about enhancing business and its brand in the eyes of its current and future customers. Sentiment analysis reports are directly usable in showing key areas for improvement. It increases the customer retention rate, resolves customers’ pain points, optimises customer service and pricing and measures the social media ROI. In conclusion, sentiment analysis enables a business to gain new insights, understand its customers and empower its teams effectively for more productive work.

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