Practical Guide to Sentiment Analysis - Everything you need to know
Sentiment analysis 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.
- 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.
- 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.
- 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.
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.
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.
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.
A very important feature in a sentiment analysis solution is multimedia comprehension. With video content analysis, the engine can identify brand logos in videos or even on a moving bus in the background.
NLP techniques in sentiment analysis detect and classify any entities such as people, businesses, brands, products, locations, or other things of note, mentioned in your data. Entities can be known in many ways as aliases or common misspellings, such as Hudson’s Bay, The Bay, HBC, HB$ all refer to Hudson’s Bay, the iconic Canadian department store.
Dashboard & Reporting
A visualization dashboard gives you holistic insight into your data for strategic decision-making. By using charts, graphs, and tables for showcasing NLP sentiment analysis insights, your business can gain quick insight into current performance and future trends.
Customization allows for greater accuracy and relevancy of outputs because the NLP tasks in sentiment analysis can process your industry-speak, product names, important entities, and specific semantic nuances.
This feature allows you to choose between an on-premise solution or a cloud-based one. The on-prem provides more reliability, and security, while the cloud software removes the pressure of maintaining and updating systems.
Read more in detail about the top features of a sentiment analysis solution.
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.
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.
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.
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.
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.
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.
NLP using sentiment analysis allows you to have comparative data on your competitors so you can work on audience engagement, and contextualize and granulate key performance indicators (KPIs) for your campaigns or product development and marketing objectives.
Opinion mining helps businesses in market research by helping them monitor social media round the clock. You can invest in influencer programs, reach out to a wider net of potential customers, amplify your brand, and even trace your performance with historical and current data.
Voice of the Customer (VoC)
NLP in sentiment analysis allows you to extract information from surveys, posts, emails, and other sources of data by discovering recurring themes and topics buried in your Voice of the Customer (VoC) data that would otherwise go unnoticed.
Voice of the Employee (VoE)
By applying aspect-based sentiment analysis (ABSA) to your voice of the employee (VoE) data, you can gain insights to increase employee satisfaction and identify factors that contribute to employee attrition.
Learn more about sentiment analysis applications.
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.
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.
An idiom is a figure of speech and machine models do not understand figures unless they are specifically trained to understand them. This issue can be solved by employing a sentiment analysis model that has been trained such that it can interpret idioms and map them to particular emotions.
Comparative sentences don’t always have an opinion but rather may just be statements. It is up to the model to gauge whether the comparison should be tied to a negative or positive sentiment or not.
Many times a business can find it difficult to derive subjective sentiments and properly analyze phrases and their intended tone. A solution that can decipher subjective statements from objective ones and then find the right tone in it can help uncover nuances and thus give more accurate results.
It can become difficult to parse information from employee data because of biases that can be either from the employee’s perspective or from the company’s such as trusting an ex-employee’s response or an employee not trusting their management. These issues can be solved by a machine-learned model that eliminates human intervention.
Videos need to be transcribed but they may have captions that need to be analyzed for brand logos. Social media videos also come with comments in addition to the video data. Video content analysis can easily fix this problem because it can break down videos to extract entities and glean insights.
Read about sentiment analysis challenges and their solutions in detail.
How Do You Conduct Sentiment Analysis?
Repustate’s powerful machine learning (ML) engine uses NLP in sentiment analysis to extract insights from text, audio, and video data to give emotion mining insights. The ML model uses video content analysis to semantically archive and gather consumer insights from YouTube, TikTok, corporate video repositories, you name it.
Let’s break down the process to see how the engine actually conducts sentiment analysis.
Step 1. Audio-to-text transcription Speech-to-text transcription backed by neural networks (NN) converts audio and video files into text. This enables you to analyze data not only from surveys or comments but also videos or podcasts.
Step 2. Caption overlay Videos are broken down into frame-by-frame images. Text that appears in these frames is recognized and extracted.
Step 3. Image recognition The engine detects images in the background and identifies brands, people, logos, et cetera classified as entities.
Step 4. Text analysis Text from documents and comments accompanying videos is processed using the text analytics API. This includes emojis and hashtags.
Step 5. Sentiment & Semantic analysis NLP using sentiment analysis and semantic analysis are employed to extract key topics and aspects and attach relative sentiment scores to them.
Step 6. Visualization All the sentiment insights are shown in a customer sentiment dashboard so the findings can be discussed, shared, and used for marketing tactics.
Sentiment Analysis Datasets
A sentiment analysis API needs to be trained on specialized sentiment analysis datasets so it can learn how to process fresh data in a similar manner. Such datasets need to vary across industries and business areas. Below are the top datasets you can use to train a sentiment analysis machine learning (ML) model.
Amazon product data:
This dataset has amazon product reviews and metadata including 142.8 million reviews that include ratings and product metadata such as descriptions, brand, category, and price.
OpinRank Review Dataset for hotels and cars:
This dataset has reviews on hotels and cars collected from TripAdvisor and Edmunds. It includes dates, favorite hotels and car models, and user names from 10 cities.
Stanford Sentiment Dataset:
This dataset has more than 10,000 pieces of Stanford data from HTML files of Rotten Tomatoes.
Cornell Movie Review Dataset:
This sentiment analysis dataset has more than 10,000 negative and positive tagged sentence texts.
Lexicoder Sentiment Dictionary:
This sentiment analysis dataset has more than 2,800 negative and 1,709 positive sentiment words.
Twitter US Airline Dataset:
This dataset contains tweets about all the major US airlines since Feb 2015 including sentiment confidence score, retweet counts, and tweet text, date, time, and location.
This dataset has more than 7000 positive and negative opinion or sentiment words in English.
Paper Reviews Data Set:
This dataset gives reviews on computing and informatics conferences in English and Spanish.
First GOP Debate Twitter Sentiment:
This sentiment analysis dataset has 14,000 labeled tweets about the first GOP debate in 2016.
IMDB Reviews Dataset:
This dataset contains 25,000 highly polar movie reviews for training and 25,000 for testing.
Sentiment Polarity Lexicons For 81 Languages:
This NLP sentiment analysis dataset contains positive and negative sentiment lexicons for 81 languages.
Which Are the Major Business Uses of NLP based Sentiment Analysis?
Let’s take a closer look at how sentiment analysis is employed in business scenarios. Below are six real-world sentiment analysis use cases where companies have used emotion mining for business intelligence, growth, and customer satisfaction.
A South African bank wanted to improve its services and ensure that its market share was not usurped by its competitors. It approached Repustate for a clever AI-powered sentiment analysis API that could extract information and derive insights from 2 million texts collected from the social media campaign it ran with hashtags over 3 months. NLP in sentiment analysis was able to show the bank what issues customers faced and what that bank could do in order to solve them. With the new systems in place, the bank saw an increase in its customer base and a decrease in the attrition rate.
2. Call Centers
A mobile network operator based in Europe needed to track and analyze all its customer service representative (CSR) interactions to know customer pain points. Repustate’s robust sentiment analysis software analyzed each stored audio file for voice of the customer analytics. Data was broken down into finer details. This eventually allowed the company to send text messages to customers apologizing for inconveniences and offering discounts and other promotional offers. Apart from this, whenever a customer called, the engine enabled historical data to be generated from the database so that the CSR was able to offer the customer promotions and enquire if they were happy with the service.
A healthcare consulting company based in Saudi Arabia wanted to locate the invisible dysfunctions and gaps in healthcare services in the Gulf region and map the patient’s journey at the various hospitals. To achieve this goal, they conducted 12 million surveys annually. NLP in sentiment analysis helped the company analyze all the survey responses, most of which were in Arabic dialects mixed with English. This automation helped them replace manual data processing that was leading to high costs and inefficiencies. The NLP sentiment analysis insights were more accurate and results, faster, and the company was able to help the Ministry of Health, KSA and other agencies to formulate policies based on the findings.
4. Government/Public Sector
A government ministry in Asia-Pacific wanted to increase the efficiency of its public services and be proactive in order to serve its citizens better. It wanted to know the main issues that citizens faced, ranging from traffic jams, to erratic service at the passport office. The government wanted to ensure that not only were the problems solved but that citizens did not face similar problems in the future. Aspect-based sentiment analysis allowed the ministry predictive analysis using historical data and helped services become more agile, efficient, and approachable.
5. Stock Sentiment Analysis
A hedge fund company needed to analyze the Asia-Pacific market in real-time as it was essential to have all the information quickly and correctly before making quick stock exchange transactions. They were not able to do so because most of the news was in Mandarin. Repustate’s sentiment analysis solution, with its multilingual entity extraction capability, helped the company build a real-time dashboard that covered market sentiment and share prices for different debt instruments and equities. This way it was able to figure the tone of the market based on price movements of the securities traded and prepare for a bullish or bearish market.
6. Sentiment Analysis in Market Research
A new healthy snacks food entrant wanted to do a market survey to understand the market response to a new healthy snack. To make the survey as unbiased as possible, the questions were open-ended and no existing snack brands were mentioned. With the help of an NLP sentiment analysis API, the company was able to analyze the surveys and find interesting insights about what food brands were frequently mentioned, and who the new company was competing against. With all the information available, the company was able to develop specific strategies pertaining to new territories, target audiences, and product roll-out.
Learn more about real-world sentiment analysis examples in business.
We hope that this guide will help you navigate your way through the world of NLP and sentiment analysis as you choose the approach best suited for your company and industry vertical. As mentioned earlier, the heart of any sentiment analysis engine is its named entity recognition capability. The more robust and comprehensive it is, the more accurate and efficient will the entity extraction capabilities of the model be. To put it in perspective, Repustate’s NER capability overshadows even those platforms in the Gartner Magic Quadrant like Amazon and Google. Take a look at this comparison chart to see the data.