Sentiment analysis, powered by artificial intelligence, has taken the central role in modern business intelligence (BI), digital marketing, navigation software industry, social media, and many other domains. According to the Market Research Future information, the global market size of sentiment analytics is expected to cross $6 billion by 2023 with gigantic growth of over 14% CAGR during 2017-2023. The most important component that contributes to this rapid growth is text analytics, which is projected to reach $16.46 billion by 2026 with over 17.8% CAGR during 2020-2026, as per Facts & Factors Research forecast. The demand for a text annotator and labeler has increased significantly in the recent years, due to faster growth of data sentiment analysis market.
What Is Machine Learning Sentiment Analysis and Major Groups of It?
The process to rate the data, especially in the form of text, in terms of a range of sentiments such as positive, negative, neutral, anger, frustration, and other feelings is known as sentiment analysis. In modern processes powered by artificial intelligence, the training data for sentiment analysis is built from multiple types of data such as text, voice, expressions, gestures, and other cues. The sentiment analysis is also referred to as opinion mining through text analysis AI tools.
AI-powered sentiment tracking and analytics, and even recruitment sentiment analytics, is performed through certain natural language processing (NLP) techniques such as word classification, sentimental phrasing, unstructured text datasets, and other types of data annotations. The implementation of sentiment analysis positive, negative, neutral, and other tones can be done through different analysis groups as mentioned below:
This group uses a dictionary of words, which have already been assigned certain sentiment values. The NLP technique uses different processes for sentiment detection such as word stemming, tokenization, categorization, and others before accessing sentiment value of the word in the dictionary. This group of sentiment analysis needs regular updates because it does not support many features such as position of the word in sentence, arrangement of words, sarcasm detection NLP, and others.
Automatic Machine Learning Analysis
To provide more capabilities to the ML algorithms, additional training through annotated datasets is given to learn different kinds of sentiments such as anger, sarcasm, synonyms, and other tones to perform automated text mood analysis through prior deep learning. This method is more efficient as compared to the rule-based group.
In this category of sentiment analysis, both the rule-based and automatic ML categories are used to perform emotion analysis from text data.
What Are Advantages and Disadvantages of AI Sentiment Analysis Services?
Like many other technologies and techniques, AI sentiment analysis has also both upsides and downsides as mentioned below:
- Improved customer experience – Analyzing the intention, interest, and feedback from customers and implementing into the customer relationships increases the user experience significantly.
- Real-time analysis – Sentiment analysis provides you with power to respond and engage with your customer in real-time for better business results.
- Data sorting at scale – A properly trained algorithm is capable of handling text ambiguity in huge piles of data easily without any human-intervention.
- Informed decisions – AI sentiment analysis helps you make data-driven decisions by knowing the sentiments of the customer base, brands, markets, and many others.
- Chances of mistakes – Machines have improved significantly but to understand sentiments through machines can make errors in detection and analysis of sentiments.
Different Ways to Apply Sentiment Data Analysis to Your Business
Sentiment analysis can be applied to different domains in your business such as:
- Customer services – Your customer services will take a new height if you apply sentiment analysis effectively in customer support and services to improve customer satisfaction.
- Social media marketing – Monitoring the sentiments of your clients and brand stakeholders and converting them into feedback is another very important use of sentiment analysis in your business.
- Brand value monitoring – The monitoring of brand discussion on different blogs, social sites, communities, and review websites can be effectively done by using sentiment analysis techniques.
- Market research – Applying sentiment analysis through AI training data services can improve insight into different factors of markets such as competition, competitors’ weaknesses and strengths, contestant brands, future trends, and much more.
How Does AI Sentiment Analysis Work?
Text sentiment analysis through rule-based approach works through the following steps:
- Splitting of documents in different factors like sentences, phrases, tokens and others
- Only sentiment-related factors sorted out
- Dictionary score is assigned and fed to an algorithm to calculate the sentiment value.
Automatic sentiment approach works through the following steps:
- Text data training sets with tags are formed through annotation and fed to feature extractor algorithm of machine learning project for training purpose
- In the prediction process, unstructured raw input is fed through classifier model of the machine learning algorithm to detect the tags for sentiment analysis
Top Industries that Use Sentiment Analysis Natural Language Processing Services Today
There are many industries in today’s world that benefit from the use of sentiment analysis NLP services.
A few top of those industries include:
Effective future planning, improvement in operations, and performance of investment are a few very desirable outcomes achieved through sentiment analysis in the finance industry.
Enhancement of the online shoppers by providing the right products, plans, and other guidelines can be easily obtained by mining a wide range of reviews, comments, and customer interests through sentiment analysis to improve user experience tremendously.
Sentiment analysis of patients can help improve healthcare services of hospitals as well as the provision of right psychological therapies to improve mental health so that they can fight against the diseases effectively.
Social media is extensively used for marketing, community development, advertisement, sales, and other processes to augment business growth. An effective use of sentiment analysis techniques helps to enhance the effectiveness of those processes significantly.
The most effective way to increase the insurance business, you need to automate customer interaction through chatbots and online communication and take client feedback for gauging the user experience, brand value, and market trends. Insurance industry uses sentiment analysis extensively to achieve those objectives.
The use of sentiment analysis in the government sector is extensively used for the maintenance of law and order. It helps track the fake news, communal comments, harsh speeches and other such activities with the help of text and voice sentiment analysis.
An Introduction to Our Sentiment Analysis Company
Our company is a reliable provider of data annotators and experts to assist you benefit from remote outsourcing capabilities. Our professional service is characterized by:
- Higher quality
- And much more
What Exactly AI Sentiment Analysis Services Do We Offer to Our Clients?
The main services offered by our company include:
- Remote team building from scratch – Our company recruits a virtual team of data labelers to provide sentiment analysis support from the very beginning. We develop a proper workspace remotely in line with the corporate identity and standards of our clients.
- Team augmentation – We can assist our clients in expanding their existing teams by recruiting the desired human resources as per requirements of businesses.
- HR consultancy – Our team specializes in providing detailed human resource consulting services based on your business requirements and objectives to achieve. Our HR consulting services help our clients to achieve the optimum usage of required resources to improve cost-efficiency and team productivity simultaneously.
- Customized HR recruitment – We can provide customized recruitment services to meet a wide range of requirements in terms of skills, expertise, projects, time-period, budgets, tasks, and others. Our recruitment services can provide you with full-time, part-time, contact-based, project-based, permanent, outsourced, skill-based, peak-load seasonal, and other types of customized recruitment services.
How Do Our Text Mining and Sentiment Analysis Staff Hiring Services Work?
Our recruitment and human resource consulting services are based on a few very simple and easy-to-follow steps as mentioned below:
- Getting in touch with us – Clients need to just get in touch with us and provide the detailed business requirements and the goals to achieve through the desired HR solutions.
- Suggesting suitable HR solutions – Our team collects, analyzes, and discusses with our clients to provide the most suitable HR solution that can fulfil clients’ needs to achieve the business objectives.
- Starting of recruitment process – Client gives go-ahead for the desired solution to start recruitment process. We start sourcing the most relevant resources, shortlisting them through screening, and prepare for a series of interviews in coordination and collaboration with our clients.
- Finalization of hiring process – Our clients give final approval for recruitment. We send job offers to hired candidates for acceptance. Client signs a detailed job contract with a hired candidate. Our recruitment team onboards your newly hired resources to connect with you for job tasks with your full control