Guide to Sentiment analysis – It is a machine learning technique that helps identify feelings and emotions expressed in comments – text, audio, or video. Through text analytics and semantic clustering, and powered by natural language processing (NLP) tasks in sentiment analysis, the process can sieve through millions of reviews and opinions posted in social media, online surveys, and even videos to give brand insights. NLP sentiment analysis, in short, gives you a tangible view of your strengths, weaknesses, and business opportunities, undiluted and from the source directly.
The purpose of this guide is to walk you through every aspect of sentiment analysis – its types; applications; challenges & solutions; how it’s done; and special features. By the end of this article, you will have a fair understanding of how sentiment analysis helps in business decisions and how it is being applied in different industries.
Sentiment Analysis Approach: Document; Topic; and Aspect
Different approaches to sentiment analysis are required when trying to understand customer emotions. There are three types of sentiment analysis approaches that you can employ – each depending on the size and complexity of the data. They are document-level sentiment analysis, topic analysis, and aspect-based sentiment analysis. Let’s examine them.
1. Document-level sentiment analysis
The document-level approach uses NLP sentiment analysis to classify the sentiment based on the information in a document. Semantics in a document can be drawn from word representation, sentence structure and its composition, and the document composition itself. This approach is good as long as there is only one sentiment in the complete text.
2. Topic Analysis
In this approach, NLP with sentiment analysis finds the emotional context related to a specific topic. This type of sentiment analysis identifies and extracts topics through keywords and aggregate scoring on which the machine learning (ML) model has been trained and customized based on the industry requirement.
3. Aspect-based sentiment analysis
Aspect-based sentiment analysis (ABSA) is a more granular approach to analyzing information. It identifies the main aspects or features of an entity and gives you a holistic view of the average sentiment expressed for each aspect.
The best approach to choosing the type of sentiment analysis that suits you most is to consider which one it is that provides the greatest degree of granular results and tangible insights that you can use to make a real difference in your business.
Usually, a sentiment analysis API that uses aspect-based granularity provides the best results, especially if your customers write complex and detailed reviews. This is the case in many industries like technology firms or hotel chains. Learn how Amazon review analysis is helping Amazon resellers in mining customer insights from thousands of reviews within seconds.
Sentiment Analysis Features
Key features in an emotion monitoring tool are powered by NLP in sentiment analysis. These are processing speed, multilingual ability, named entity recognition, flexibility in deployment, and an insights dashboard, to name a few.
Let’s get to know these sentiment analysis features better.
1. Speed and Scale
A sentiment analysis tool should process no less than 500 posts per second and be able to handle millions of API calls per day. It should be powerful enough to maintain the same speed even when performing at scale.
The accuracy of a sentiment analysis platform is based on its ability to precisely score the sentiment expressed to various degrees of accuracy, regardless of language or data source.
3. Aspect-based Analysis
Having NLP in sentiment analysis means that this feature can give you the most detailed insights through aspect-based sentiment analysis (ABSA). This in turn tells you the strengths and weaknesses of a product or service more accurately.
This feature ensures that vital sentiment analysis information is harnessed from your data regardless of the language. True multilingual abilities allow for a much higher degree of accuracy in NLP sentiment analysis, so you can reach multiple markets.
5. Social Media
This feature gleans all the information you are looking for through social media sentiment analysis. NLP functionality in sentiment analysis ensures that the engine understands social media slang, industry-specific jargon, hashtags, as well as emojis and emoticons.
Applications of NLP Powered Sentiment Analysis
Sentiment analysis is applied on a large scale in almost all industries today – whether it’s for better customer experience, healthcare, or brand insights. With customer sentiment insights available in real-time, companies are able to focus on product betterment, sales strategies, and even social media-leaning marketing approaches such as influencer programs for brand building and amplification.
Here is a list of some important sentiment analysis applications that are already present in everyday business environments.
1. Patient Voice
Patient experience (PX) data can give crucial information to hospitals, pharmaceuticals firms, and health insurance companies for improving patient care. NLP using sentiment analysis of this patient feedback and information is already being employed on data such as hospital surveys, patient voice notes, emails, and other such forms for improved PX.
2. Social Media Listening
Social media sentiment analysis helps businesses monitor online brand reputation (ORM) and perception by processing reviews and mentions in social media chatter. Repustate’s sentiment analysis tool not only collects and understands data from text but also from video uploads on platforms like TikTok, YouTube, and Instagram Live through video content analysis and search inside video functions.
3. Business Intelligence
Through NLP techniques for sentiment analysis, a company can have a treasure trove of business intelligence for a pool of hidden opportunities. Sentiment mining tools can help you boost your marketing and sales efforts, driving up your ROI. Apart from this, Repustate’s Semantic Search for enterprises uses machine learning techniques to find all of the entities and topics in a company’s big data.
4. Brand Insights
Brand insights aim to give you detailed consumer insights to benchmark and elevate your brand reputation, especially for potential customers. Through NLP with sentiment analysis, you can easily know what aspects of your business resonate with your customers thus making them your strong points, and what aspects you need to be working on.
5. Business Reputation Management
Sentiment analysis allows you to find and fill the gap in your brand image. Since the overall reputation of a company depends not only on its business operations but also on its employees, organizations it associates with, and politicians it supports, sentiment analysis of news and media enables you to keep a tight grip on your reputation.
Sentiment Analysis Challenges & Solutions
NLP with sentiment analysis gives companies insights for improved product features, pricing, store locations, customer experience, and overall employee satisfaction. Yet, when it comes to the practical application of sentiment analysis, businesses do face some issues. These sentiment analysis challenges can be tackled with different approaches. Let’s get to know them a little better.
Sarcasm expresses negative sentiment using overt language and implying things. This can often result in a higher volume of “positive” feedback that is actually negative. When a sentiment analysis tool is trained to detect the context of a text, it can overcome this issue and give precise results.
Negations can confuse the ML model but NLP tasks in sentiment analysis can allow the platform to understand that double negatives turn a sentence into a positive one.
High polarity words “love” and “hate” are easy but phrases such as “not so bad” can sometimes be left out, thus diluting the sentiment score. NLP in sentiment analysis can help with this by easily figuring out these mid-polar phrases and words.
Social media content is full of emojis. Most sentiment analysis solutions remove them from the data during text mining. But if you have an engine that has an emotion analyzer to decode emojis like Repustate’s API does, you can beat this problem. This is especially useful if you are conducting voice of the customer analytics.
5. Multilingual data
Every language needs a unique NLP solution so that the sentiment analysis and text analytics model does not need to translate the text in order to understand it. If you choose a solution that reads languages natively and has a unique named entity recognition (NER) model for every language, this issue is solved easily.