- January 21, 2021
- Posted by: Suhith Kumar
- Category: Text Analytics
Organizations’ obsession with metrics and data is real. They, after all, empower the stakeholders to find opportunities and focus on the details that matter. However, it is important to remember that data and metrics alone do not reveal much about customers, whose purchasing decisions are based on sentiments and feedback.
Most customers today share their feelings about brands and products through social media comments, surveys, product reviews, to name a few. More significantly, according to a Podium survey, 93% of consumers said that online reviews impact their purchasing decisions.
You might be interested to read on : Why Sentiment Analysis plays a key role in strategy formulation
Sentiment analysis is one way for brands to understand their customers’ feelings about their products and services, with the technology providing context to each feedback. Is your product solving your customers’ main issues? Are they satisfied with the quality of your service? Sentiment analysis can answer these and mine subjective information from online content, including blogs, texts, comments and more.
Customer sentiment can be learned with the help of Natural language processing (NLP) solutions, machine learning and statistics. It provides the basis for optimized brand messages towards the target audience/demographic. It is worth mentioning that sentiment analysis was used by the Obama administration to forecast public response to its policy announcements before the 2012 US presidential election.
Steps involved in Sentiment Analysis:
- Gather feedback data
- Ensure data is qualitative to perform analysis
- Check for readymade software (they are easier to operate) and APIs
Types of sentiment analysis
Based on scale, sentiment analysis can be classified as coarse-grained and fine-grained.
Coarse-grained helps extract sentiment at a document or a sentence level.
Fine-grained helps extract sentiment across each of the sentence parts.
While this sentiment analysis type is performed at sentence and document levels, it is mainly used to evaluate sentences.
It can be further sub-classified into subjectivity classification and sentiment detection and classification.
The aim is to identify subjective (in other words, opinionated) sentences and classify them according to their polarity, whether they are positive, negative or neutral feedback.
The data is pre-processed to understand the public’s opinion about a specific topic. The feedback is then summarized to allow for improving the product or service.
Sentiment detection and classification
Here the objective is to determine if a sentence provides any sentiment. If the answer is yes, the subsequent goal is to identify if the expression is positive, neutral or negative.
It is quite common to come across sentences lacking in emotion.
Take this sentence for example: Everyone should get a second chance. It is subjective and vague. This sentence, therefore, falls under the neutral sentiment category.
Let us look at another sentence: It was among the most enjoyable train journeys we have gone on. The statement is not only clear and subjective but also offers a positive sentiment.
It is worth mentioning that sentiment does not always depend on objectivity or subjectivity. However, sentences must be distinguished based on those that express emotions and other signals, to gain key insights from customer feedback.
If precision in sentiment extraction is important to your organization, this is the type of analysis you want to perform.
In contrast to classification such as positive, negative or neutral; in fine-grained sentiment analysis, the polarity categories could include ‘very positive’ and ‘very negative’ sentiments. A five-star review or rating would fall under the ‘very positive’ category, while a one-star review would be ‘very negative’.
The typical process involves the sentence being broken down into clauses and phrases, with each part analyzed relative to others. It allows you to identify the person giving their feedback about a product. What’s more, it even helps comprehend why someone evaluates it in the way they do.
Fine-grained sentiment analysis is particularly useful in processing comparative statements, while it could also be applied to analyze social media content.
Aspect-based : sentiment analysis solution
This is a technique with which businesses can get the most out of their data. What do we mean by that?
If you need a comprehensive picture of customer feedback from different sources (surveys, social media, online reviews and more), aspect-based sentiment analysis is your go-to solution.
For example, you can gain the following information with this type of sentiment analysis: user experience with one of your products, response time for a customer query, the ease of software integration.
With an in-depth understanding of the most pressing issues to customers, companies are then able to deliver the desired customer experience and greatly increase customer retention.
At a time when customer-centricity is key to the success of any business, sentiment analysis software helps leverage the large volumes of feedback data to understand customer requirements and their opinions about brands. Here we have covered three main types of sentiment analysis which help glean insights about key customer concerns.
teX.ai is the one-stop solution for all your sentiment analysis requirements. Reach out to us today.